WorldWideScience

Sample records for motor imagery-based brain-computer

  1. Calibrating EEG-based motor imagery brain-computer interface from passive movement.

    Science.gov (United States)

    Ang, Kai Keng; Guan, Cuntai; Wang, Chuanchu; Phua, Kok Soon; Tan, Adrian Hock Guan; Chin, Zheng Yang

    2011-01-01

    EEG data from performing motor imagery are usually collected to calibrate a subject-specific model for classifying the EEG data during the evaluation phase of motor imagery Brain-Computer Interface (BCI). However, there is no direct objective measure to determine if a subject is performing motor imagery correctly for proper calibration. Studies have shown that passive movement, which is directly observable, induces Event-Related Synchronization patterns that are similar to those induced from motor imagery. Hence, this paper investigates the feasibility of calibrating EEG-based motor imagery BCI from passive movement. EEG data of 12 healthy subjects were collected during motor imagery and passive movement of the hand by a haptic knob robot. The calibration models using the Filter Bank Common Spatial Pattern algorithm on the EEG data from motor imagery were compared against using the EEG data from passive movement. The performances were compared based on the 10×10-fold cross-validation accuracies of the calibration data, and off-line session-to-session transfer kappa values to other sessions of motor imagery performed on another day. The results showed that the calibration performed using passive movement yielded higher model accuracy and off-line session-to-session transfer (73.6% and 0.354) than the calibration performed using motor imagery (71.3% and 0.311), and no significant differences were observed between the two groups (p=0.20, 0.23). Hence, this study shows that it is feasible to calibrate EEG-based motor imagery BCI from passive movement.

  2. A Wearable Channel Selection-Based Brain-Computer Interface for Motor Imagery Detection.

    Science.gov (United States)

    Lo, Chi-Chun; Chien, Tsung-Yi; Chen, Yu-Chun; Tsai, Shang-Ho; Fang, Wai-Chi; Lin, Bor-Shyh

    2016-02-06

    Motor imagery-based brain-computer interface (BCI) is a communication interface between an external machine and the brain. Many kinds of spatial filters are used in BCIs to enhance the electroencephalography (EEG) features related to motor imagery. The approach of channel selection, developed to reserve meaningful EEG channels, is also an important technique for the development of BCIs. However, current BCI systems require a conventional EEG machine and EEG electrodes with conductive gel to acquire multi-channel EEG signals and then transmit these EEG signals to the back-end computer to perform the approach of channel selection. This reduces the convenience of use in daily life and increases the limitations of BCI applications. In order to improve the above issues, a novel wearable channel selection-based brain-computer interface is proposed. Here, retractable comb-shaped active dry electrodes are designed to measure the EEG signals on a hairy site, without conductive gel. By the design of analog CAR spatial filters and the firmware of EEG acquisition module, the function of spatial filters could be performed without any calculation, and channel selection could be performed in the front-end device to improve the practicability of detecting motor imagery in the wearable EEG device directly or in commercial mobile phones or tablets, which may have relatively low system specifications. Finally, the performance of the proposed BCI is investigated, and the experimental results show that the proposed system is a good wearable BCI system prototype.

  3. A hybrid NIRS-EEG system for self-paced brain computer interface with online motor imagery.

    Science.gov (United States)

    Koo, Bonkon; Lee, Hwan-Gon; Nam, Yunjun; Kang, Hyohyeong; Koh, Chin Su; Shin, Hyung-Cheul; Choi, Seungjin

    2015-04-15

    For a self-paced motor imagery based brain-computer interface (BCI), the system should be able to recognize the occurrence of a motor imagery, as well as the type of the motor imagery. However, because of the difficulty of detecting the occurrence of a motor imagery, general motor imagery based BCI studies have been focusing on the cued motor imagery paradigm. In this paper, we present a novel hybrid BCI system that uses near infrared spectroscopy (NIRS) and electroencephalography (EEG) systems together to achieve online self-paced motor imagery based BCI. We designed a unique sensor frame that records NIRS and EEG simultaneously for the realization of our system. Based on this hybrid system, we proposed a novel analysis method that detects the occurrence of a motor imagery with the NIRS system, and classifies its type with the EEG system. An online experiment demonstrated that our hybrid system had a true positive rate of about 88%, a false positive rate of 7% with an average response time of 10.36 s. As far as we know, there is no report that explored hemodynamic brain switch for self-paced motor imagery based BCI with hybrid EEG and NIRS system. From our experimental results, our hybrid system showed enough reliability for using in a practical self-paced motor imagery based BCI. Copyright © 2014 Elsevier B.V. All rights reserved.

  4. Effect of instructive visual stimuli on neurofeedback training for motor imagery-based brain-computer interface.

    Science.gov (United States)

    Kondo, Toshiyuki; Saeki, Midori; Hayashi, Yoshikatsu; Nakayashiki, Kosei; Takata, Yohei

    2015-10-01

    Event-related desynchronization (ERD) of the electroencephalogram (EEG) from the motor cortex is associated with execution, observation, and mental imagery of motor tasks. Generation of ERD by motor imagery (MI) has been widely used for brain-computer interfaces (BCIs) linked to neuroprosthetics and other motor assistance devices. Control of MI-based BCIs can be acquired by neurofeedback training to reliably induce MI-associated ERD. To develop more effective training conditions, we investigated the effect of static and dynamic visual representations of target movements (a picture of forearms or a video clip of hand grasping movements) during the BCI neurofeedback training. After 4 consecutive training days, the group that performed MI while viewing the video showed significant improvement in generating MI-associated ERD compared with the group that viewed the static image. This result suggests that passively observing the target movement during MI would improve the associated mental imagery and enhance MI-based BCIs skills. Copyright © 2014 Elsevier B.V. All rights reserved.

  5. User's Self-Prediction of Performance in Motor Imagery Brain-Computer Interface.

    Science.gov (United States)

    Ahn, Minkyu; Cho, Hohyun; Ahn, Sangtae; Jun, Sung C

    2018-01-01

    Performance variation is a critical issue in motor imagery brain-computer interface (MI-BCI), and various neurophysiological, psychological, and anatomical correlates have been reported in the literature. Although the main aim of such studies is to predict MI-BCI performance for the prescreening of poor performers, studies which focus on the user's sense of the motor imagery process and directly estimate MI-BCI performance through the user's self-prediction are lacking. In this study, we first test each user's self-prediction idea regarding motor imagery experimental datasets. Fifty-two subjects participated in a classical, two-class motor imagery experiment and were asked to evaluate their easiness with motor imagery and to predict their own MI-BCI performance. During the motor imagery experiment, an electroencephalogram (EEG) was recorded; however, no feedback on motor imagery was given to subjects. From EEG recordings, the offline classification accuracy was estimated and compared with several questionnaire scores of subjects, as well as with each subject's self-prediction of MI-BCI performance. The subjects' performance predictions during motor imagery task showed a high positive correlation ( r = 0.64, p performance even without feedback information. This implies that the human brain is an active learning system and, by self-experiencing the endogenous motor imagery process, it can sense and adopt the quality of the process. Thus, it is believed that users may be able to predict MI-BCI performance and results may contribute to a better understanding of low performance and advancing BCI.

  6. Application of a common spatial pattern-based algorithm for an fNIRS-based motor imagery brain-computer interface.

    Science.gov (United States)

    Zhang, Shen; Zheng, Yanchun; Wang, Daifa; Wang, Ling; Ma, Jianai; Zhang, Jing; Xu, Weihao; Li, Deyu; Zhang, Dan

    2017-08-10

    Motor imagery is one of the most investigated paradigms in the field of brain-computer interfaces (BCIs). The present study explored the feasibility of applying a common spatial pattern (CSP)-based algorithm for a functional near-infrared spectroscopy (fNIRS)-based motor imagery BCI. Ten participants performed kinesthetic imagery of their left- and right-hand movements while 20-channel fNIRS signals were recorded over the motor cortex. The CSP method was implemented to obtain the spatial filters specific for both imagery tasks. The mean, slope, and variance of the CSP filtered signals were taken as features for BCI classification. Results showed that the CSP-based algorithm outperformed two representative channel-wise methods for classifying the two imagery statuses using either data from all channels or averaged data from imagery responsive channels only (oxygenated hemoglobin: CSP-based: 75.3±13.1%; all-channel: 52.3±5.3%; averaged: 64.8±13.2%; deoxygenated hemoglobin: CSP-based: 72.3±13.0%; all-channel: 48.8±8.2%; averaged: 63.3±13.3%). Furthermore, the effectiveness of the CSP method was also observed for the motor execution data to a lesser extent. A partial correlation analysis revealed significant independent contributions from all three types of features, including the often-ignored variance feature. To our knowledge, this is the first study demonstrating the effectiveness of the CSP method for fNIRS-based motor imagery BCIs. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Performance variation in motor imagery brain-computer interface: a brief review.

    Science.gov (United States)

    Ahn, Minkyu; Jun, Sung Chan

    2015-03-30

    Brain-computer interface (BCI) technology has attracted significant attention over recent decades, and has made remarkable progress. However, BCI still faces a critical hurdle, in that performance varies greatly across and even within subjects, an obstacle that degrades the reliability of BCI systems. Understanding the causes of these problems is important if we are to create more stable systems. In this short review, we report the most recent studies and findings on performance variation, especially in motor imagery-based BCI, which has found that low-performance groups have a less-developed brain network that is incapable of motor imagery. Further, psychological and physiological states influence performance variation within subjects. We propose a possible strategic approach to deal with this variation, which may contribute to improving the reliability of BCI. In addition, the limitations of current work and opportunities for future studies are discussed. Copyright © 2015 Elsevier B.V. All rights reserved.

  8. Principles of Motor Recovery in Post-Stroke Patients using Hand Exoskeleton Controlled by the Brain-Computer Interface Based on Motor Imagery

    Czech Academy of Sciences Publication Activity Database

    Frolov, A. A.; Húsek, Dušan; Biryukova, E. V.; Bobrov, P.; Mokienko, O.; Alexandrov, A.V.

    2017-01-01

    Roč. 27, č. 1 (2017), s. 107-137 ISSN 1210-0552 Grant - others:Russian Ministry of Education and Science(RU) RFMEFI60715X0128 Institutional support: RVO:67985807 Keywords : brain computer interface * motor imagery * post-stroke and post-traumatic patients * arm and hand exoskeleton * proportional derivative controller * motor synergy * clinical application Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 0.394, year: 2016

  9. BRAIN-COMPUTER-INTERFACE – SUPPORTED MOTOR IMAGERY TRAININTG FOR PATIENTS WITH HEMIPARESIS

    Directory of Open Access Journals (Sweden)

    O. A. Mokienko

    2013-01-01

    Full Text Available The aim of study was to assess the feasibility of motor imagery supported brain-computer interface in patients with hemiparesis. 13 patients with central paresis of the hand and 15 healthy volunteers were learning to control EEG-based interface with feedback. No differences on interface control quality were found between patients and healthy subjects. The trainings were accompanied by the desynchronization of sensorimotor rhythm. In patients with cortical damage the source of EEG-activity was dislocated.

  10. Assessing motor imagery in brain-computer interface training: Psychological and neurophysiological correlates.

    Science.gov (United States)

    Vasilyev, Anatoly; Liburkina, Sofya; Yakovlev, Lev; Perepelkina, Olga; Kaplan, Alexander

    2017-03-01

    Motor imagery (MI) is considered to be a promising cognitive tool for improving motor skills as well as for rehabilitation therapy of movement disorders. It is believed that MI training efficiency could be improved by using the brain-computer interface (BCI) technology providing real-time feedback on person's mental attempts. While BCI is indeed a convenient and motivating tool for practicing MI, it is not clear whether it could be used for predicting or measuring potential positive impact of the training. In this study, we are trying to establish whether the proficiency in BCI control is associated with any of the neurophysiological or psychological correlates of motor imagery, as well as to determine possible interrelations among them. For that purpose, we studied motor imagery in a group of 19 healthy BCI-trained volunteers and performed a correlation analysis across various quantitative assessment metrics. We examined subjects' sensorimotor event-related EEG events, corticospinal excitability changes estimated with single-pulse transcranial magnetic stimulation (TMS), BCI accuracy and self-assessment reports obtained with specially designed questionnaires and interview routine. Our results showed, expectedly, that BCI performance is dependent on the subject's capability to suppress EEG sensorimotor rhythms, which in turn is correlated with the idle state amplitude of those oscillations. Neither BCI accuracy nor the EEG features associated with MI were found to correlate with the level of corticospinal excitability increase during motor imagery, and with assessed imagery vividness. Finally, a significant correlation was found between the level of corticospinal excitability increase and kinesthetic vividness of imagery (KVIQ-20 questionnaire). Our results suggest that two distinct neurophysiological mechanisms might mediate possible effects of motor imagery: the non-specific cortical sensorimotor disinhibition and the focal corticospinal excitability increase

  11. Sources of EEG activity most relevant to performance of brain-computer interface based on motor imagery

    Czech Academy of Sciences Publication Activity Database

    Frolov, A.; Húsek, Dušan; Bobrov, P.; Korshakov, A.V.; Chernikova, L.; Konovalov, R.; Mokienko, O.

    2012-01-01

    Roč. 22, č. 1 (2012), s. 21-37 ISSN 1210-0552 R&D Projects: GA ČR GAP202/10/0262 Grant - others:GA MŠk(CZ) ED1.1.00/02.0070 Program:ED Institutional research plan: CEZ:AV0Z10300504 Keywords : brain-computer interface * independent component analysis * pattern classification * motor imagery * inverse problem * fMRI * EEG Subject RIV: IN - Informatics, Computer Science Impact factor: 0.362, year: 2012

  12. A square root ensemble Kalman filter application to a motor-imagery brain-computer interface.

    Science.gov (United States)

    Kamrunnahar, M; Schiff, S J

    2011-01-01

    We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%-90% for the hand movements and 70%-90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models.

  13. Sensorimotor rhythm-based brain-computer interface training: the impact on motor cortical responsiveness

    Science.gov (United States)

    Pichiorri, F.; De Vico Fallani, F.; Cincotti, F.; Babiloni, F.; Molinari, M.; Kleih, S. C.; Neuper, C.; Kübler, A.; Mattia, D.

    2011-04-01

    The main purpose of electroencephalography (EEG)-based brain-computer interface (BCI) technology is to provide an alternative channel to support communication and control when motor pathways are interrupted. Despite the considerable amount of research focused on the improvement of EEG signal detection and translation into output commands, little is known about how learning to operate a BCI device may affect brain plasticity. This study investigated if and how sensorimotor rhythm-based BCI training would induce persistent functional changes in motor cortex, as assessed with transcranial magnetic stimulation (TMS) and high-density EEG. Motor imagery (MI)-based BCI training in naïve participants led to a significant increase in motor cortical excitability, as revealed by post-training TMS mapping of the hand muscle's cortical representation; peak amplitude and volume of the motor evoked potentials recorded from the opponens pollicis muscle were significantly higher only in those subjects who develop a MI strategy based on imagination of hand grasping to successfully control a computer cursor. Furthermore, analysis of the functional brain networks constructed using a connectivity matrix between scalp electrodes revealed a significant decrease in the global efficiency index for the higher-beta frequency range (22-29 Hz), indicating that the brain network changes its topology with practice of hand grasping MI. Our findings build the neurophysiological basis for the use of non-invasive BCI technology for monitoring and guidance of motor imagery-dependent brain plasticity and thus may render BCI a viable tool for post-stroke rehabilitation.

  14. Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis.

    Science.gov (United States)

    Vourvopoulos, Athanasios; Bermúdez I Badia, Sergi

    2016-08-09

    The use of Brain-Computer Interface (BCI) technology in neurorehabilitation provides new strategies to overcome stroke-related motor limitations. Recent studies demonstrated the brain's capacity for functional and structural plasticity through BCI. However, it is not fully clear how we can take full advantage of the neurobiological mechanisms underlying recovery and how to maximize restoration through BCI. In this study we investigate the role of multimodal virtual reality (VR) simulations and motor priming (MP) in an upper limb motor-imagery BCI task in order to maximize the engagement of sensory-motor networks in a broad range of patients who can benefit from virtual rehabilitation training. In order to investigate how different BCI paradigms impact brain activation, we designed 3 experimental conditions in a within-subject design, including an immersive Multimodal Virtual Reality with Motor Priming (VRMP) condition where users had to perform motor-execution before BCI training, an immersive Multimodal VR condition, and a control condition with standard 2D feedback. Further, these were also compared to overt motor-execution. Finally, a set of questionnaires were used to gather subjective data on Workload, Kinesthetic Imagery and Presence. Our findings show increased capacity to modulate and enhance brain activity patterns in all extracted EEG rhythms matching more closely those present during motor-execution and also a strong relationship between electrophysiological data and subjective experience. Our data suggest that both VR and particularly MP can enhance the activation of brain patterns present during overt motor-execution. Further, we show changes in the interhemispheric EEG balance, which might play an important role in the promotion of neural activation and neuroplastic changes in stroke patients in a motor-imagery neurofeedback paradigm. In addition, electrophysiological correlates of psychophysiological responses provide us with valuable information

  15. Increased motor cortex excitability during motor imagery in brain-computer interface trained subjects

    Science.gov (United States)

    Mokienko, Olesya A.; Chervyakov, Alexander V.; Kulikova, Sofia N.; Bobrov, Pavel D.; Chernikova, Liudmila A.; Frolov, Alexander A.; Piradov, Mikhail A.

    2013-01-01

    Background: Motor imagery (MI) is the mental performance of movement without muscle activity. It is generally accepted that MI and motor performance have similar physiological mechanisms. Purpose: To investigate the activity and excitability of cortical motor areas during MI in subjects who were previously trained with an MI-based brain-computer interface (BCI). Subjects and Methods: Eleven healthy volunteers without neurological impairments (mean age, 36 years; range: 24–68 years) were either trained with an MI-based BCI (BCI-trained, n = 5) or received no BCI training (n = 6, controls). Subjects imagined grasping in a blocked paradigm task with alternating rest and task periods. For evaluating the activity and excitability of cortical motor areas we used functional MRI and navigated transcranial magnetic stimulation (nTMS). Results: fMRI revealed activation in Brodmann areas 3 and 6, the cerebellum, and the thalamus during MI in all subjects. The primary motor cortex was activated only in BCI-trained subjects. The associative zones of activation were larger in non-trained subjects. During MI, motor evoked potentials recorded from two of the three targeted muscles were significantly higher only in BCI-trained subjects. The motor threshold decreased (median = 17%) during MI, which was also observed only in BCI-trained subjects. Conclusion: Previous BCI training increased motor cortex excitability during MI. These data may help to improve BCI applications, including rehabilitation of patients with cerebral palsy. PMID:24319425

  16. Increased motor cortex excitability during motor imagery in brain-computer interface trained subjects

    Directory of Open Access Journals (Sweden)

    Olesya eMokienko

    2013-11-01

    Full Text Available Background: Motor imagery (MI is the mental performance of movement without muscle activity. It is generally accepted that MI and motor performance have similar physiological mechanisms.Purpose: To investigate the activity and excitability of cortical motor areas during MI in subjects who were previously trained with an MI-based brain-computer interface (BCI.Subjects and methods: Eleven healthy volunteers without neurological impairments (mean age, 36 years; range: 24–68 years were either trained with an MI-based BCI (BCI-trained, n = 5 or received no BCI training (n = 6, controls. Subjects imagined grasping in a blocked paradigm task with alternating rest and task periods. For evaluating the activity and excitability of cortical motor areas we used functional MRI and navigated transcranial magnetic stimulation (nTMS.Results: fMRI revealed activation in Brodmann areas 3 and 6, the cerebellum, and the thalamus during MI in all subjects. The primary motor cortex was activated only in BCI-trained subjects. The associative zones of activation were larger in non-trained subjects. During MI, motor evoked potentials recorded from two of the three targeted muscles were significantly higher only in BCI-trained subjects. The motor threshold decreased (median = 17% during MI, which was also observed only in BCI-trained subjects.Conclusion: Previous BCI training increased motor cortex excitability during MI. These data may help to improve BCI applications, including rehabilitation of patients with cerebral palsy.

  17. The Changes in the Hemodynamic Activity of the Brain during Motor Imagery Training with the Use of Brain-Computer Interface

    Czech Academy of Sciences Publication Activity Database

    Frolov, A. A.; Húsek, Dušan; Silchenko, A.V.; Tintěra, J.; Rydlo, J.

    2016-01-01

    Roč. 42, č. 1 (2016), s. 1-12 ISSN 0362-1197 R&D Projects: GA MŠk ED1.1.00/02.0070 Grant - others:GA MŠk(CZ) EE.2.3.20.0073 Institutional support: RVO:67985807 Keywords : brain-computer interface * motor imagery * hemodynamic activity * brain plasticity * functional MRI Subject RIV: IN - Informatics, Computer Science

  18. The Study of Object-Oriented Motor Imagery Based on EEG Suppression.

    Directory of Open Access Journals (Sweden)

    Lili Li

    Full Text Available Motor imagery is a conventional method for brain computer interface and motor learning. To avoid the great individual difference of the motor imagery ability, object-oriented motor imagery was applied, and the effects were studied. Kinesthetic motor imagery and visual observation were administered to 15 healthy volunteers. The EEG during cue-based simple imagery (SI, object-oriented motor imagery (OI, non-object-oriented motor imagery (NI and visual observation (VO was recorded. Study results showed that OI and NI presented significant contralateral suppression in mu rhythm (p 0.05. Compared with NI, OI showed significant difference (p < 0.05 in mu rhythm and weak significant difference (p = 0.0612 in beta rhythm over the contralateral hemisphere. The ability of motor imagery can be reflected by the suppression degree of mu and beta frequencies which are the motor related rhythms. Thus, greater enhancement of activation in mirror neuron system is involved in response to object-oriented motor imagery. The object-oriented motor imagery is favorable for improvement of motor imagery ability.

  19. Optimized Motor Imagery Paradigm Based on Imagining Chinese Characters Writing Movement.

    Science.gov (United States)

    Qiu, Zhaoyang; Allison, Brendan Z; Jin, Jing; Zhang, Yu; Wang, Xingyu; Li, Wei; Cichocki, Andrzej

    2017-07-01

    motor imagery (MI) is a mental representation of motor behavior. The MI-based brain computer interfaces (BCIs) can provide communication for the physically impaired. The performance of MI-based BCI mainly depends on the subject's ability to self-modulate electroencephalogram signals. Proper training can help naive subjects learn to modulate brain activity proficiently. However, training subjects typically involve abstract motor tasks and are time-consuming. to improve the performance of naive subjects during motor imagery, a novel paradigm was presented that would guide naive subjects to modulate brain activity effectively. In this new paradigm, pictures of the left or right hand were used as cues for subjects to finish the motor imagery task. Fourteen healthy subjects (11 male, aged 22-25 years, and mean 23.6±1.16) participated in this study. The task was to imagine writing a Chinese character. Specifically, subjects could imagine hand movements corresponding to the sequence of writing strokes in the Chinese character. This paradigm was meant to find an effective and familiar action for most Chinese people, to provide them with a specific, extensively practiced task and help them modulate brain activity. results showed that the writing task paradigm yielded significantly better performance than the traditional arrow paradigm (p paradigm was easier. the proposed new motor imagery paradigm could guide subjects to help them modulate brain activity effectively. Results showed that there were significant improvements using new paradigm, both in classification accuracy and usability.

  20. Brain-Computer Interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair.

    Science.gov (United States)

    Ron-Angevin, Ricardo; Velasco-Álvarez, Francisco; Fernández-Rodríguez, Álvaro; Díaz-Estrella, Antonio; Blanca-Mena, María José; Vizcaíno-Martín, Francisco Javier

    2017-05-30

    Certain diseases affect brain areas that control the movements of the patients' body, thereby limiting their autonomy and communication capacity. Research in the field of Brain-Computer Interfaces aims to provide patients with an alternative communication channel not based on muscular activity, but on the processing of brain signals. Through these systems, subjects can control external devices such as spellers to communicate, robotic prostheses to restore limb movements, or domotic systems. The present work focus on the non-muscular control of a robotic wheelchair. A proposal to control a wheelchair through a Brain-Computer Interface based on the discrimination of only two mental tasks is presented in this study. The wheelchair displacement is performed with discrete movements. The control signals used are sensorimotor rhythms modulated through a right-hand motor imagery task or mental idle state. The peculiarity of the control system is that it is based on a serial auditory interface that provides the user with four navigation commands. The use of two mental tasks to select commands may facilitate control and reduce error rates compared to other endogenous control systems for wheelchairs. Seventeen subjects initially participated in the study; nine of them completed the three sessions of the proposed protocol. After the first calibration session, seven subjects were discarded due to a low control of their electroencephalographic signals; nine out of ten subjects controlled a virtual wheelchair during the second session; these same nine subjects achieved a medium accuracy level above 0.83 on the real wheelchair control session. The results suggest that more extensive training with the proposed control system can be an effective and safe option that will allow the displacement of a wheelchair in a controlled environment for potential users suffering from some types of motor neuron diseases.

  1. Motor imagery based brain-computer interfaces: An emerging technology to rehabilitate motor deficits.

    Science.gov (United States)

    Alonso-Valerdi, Luz Maria; Salido-Ruiz, Ricardo Antonio; Ramirez-Mendoza, Ricardo A

    2015-12-01

    When the sensory-motor integration system is malfunctioning provokes a wide variety of neurological disorders, which in many cases cannot be treated with conventional medication, or via existing therapeutic technology. A brain-computer interface (BCI) is a tool that permits to reintegrate the sensory-motor loop, accessing directly to brain information. A potential, promising and quite investigated application of BCI has been in the motor rehabilitation field. It is well-known that motor deficits are the major disability wherewith the worldwide population lives. Therefore, this paper aims to specify the foundation of motor rehabilitation BCIs, as well as to review the recent research conducted so far (specifically, from 2007 to date), in order to evaluate the suitability and reliability of this technology. Although BCI for post-stroke rehabilitation is still in its infancy, the tendency is towards the development of implantable devices that encompass a BCI module plus a stimulation system. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Electrophysiological Brain Activity during the Control of a Motor Imagery-Based Brain–Computer Interface

    Czech Academy of Sciences Publication Activity Database

    Frolov, A. A.; Aziatskaya, G.A.; Bobrov, P.D.; Luykmanov, R. Kh.; Fedotova, I.R.; Húsek, Dušan; Snášel, V.

    2017-01-01

    Roč. 43, č. 5 (2017), s. 501-511 ISSN 0362-1197 Institutional support: RVO:67985807 Keywords : brain–computer interface * neurointerface * EEG * motor imagery * EEG rhythm synchronization and desynchronization * independent component analysis * EEG inverse problem * neurorehabilitation Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

  3. Motor Imagery-Based Brain-Computer Interface Coupled to a Robotic Hand Orthosis Aimed for Neurorehabilitation of Stroke Patients

    Directory of Open Access Journals (Sweden)

    Jessica Cantillo-Negrete

    2018-01-01

    Full Text Available Motor imagery-based brain-computer interfaces (BCI have shown potential for the rehabilitation of stroke patients; however, low performance has restricted their application in clinical environments. Therefore, this work presents the implementation of a BCI system, coupled to a robotic hand orthosis and driven by hand motor imagery of healthy subjects and the paralysed hand of stroke patients. A novel processing stage was designed using a bank of temporal filters, the common spatial pattern algorithm for feature extraction and particle swarm optimisation for feature selection. Offline tests were performed for testing the proposed processing stage, and results were compared with those computed with common spatial patterns. Afterwards, online tests with healthy subjects were performed in which the orthosis was activated by the system. Stroke patients’ average performance was 74.1 ± 11%. For 4 out of 6 patients, the proposed method showed a statistically significant higher performance than the common spatial pattern method. Healthy subjects’ average offline and online performances were of 76.2 ± 7.6% and 70 ± 6.7, respectively. For 3 out of 8 healthy subjects, the proposed method showed a statistically significant higher performance than the common spatial pattern method. System’s performance showed that it has a potential to be used for hand rehabilitation of stroke patients.

  4. Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial.

    Science.gov (United States)

    Frolov, Alexander A; Mokienko, Olesya; Lyukmanov, Roman; Biryukova, Elena; Kotov, Sergey; Turbina, Lydia; Nadareyshvily, Georgy; Bushkova, Yulia

    2017-01-01

    Repeated use of brain-computer interfaces (BCIs) providing contingent sensory feedback of brain activity was recently proposed as a rehabilitation approach to restore motor function after stroke or spinal cord lesions. However, there are only a few clinical studies that investigate feasibility and effectiveness of such an approach. Here we report on a placebo-controlled, multicenter clinical trial that investigated whether stroke survivors with severe upper limb (UL) paralysis benefit from 10 BCI training sessions each lasting up to 40 min. A total of 74 patients participated: median time since stroke is 8 months, 25 and 75% quartiles [3.0; 13.0]; median severity of UL paralysis is 4.5 points [0.0; 30.0] as measured by the Action Research Arm Test, ARAT, and 19.5 points [11.0; 40.0] as measured by the Fugl-Meyer Motor Assessment, FMMA. Patients in the BCI group ( n = 55) performed motor imagery of opening their affected hand. Motor imagery-related brain electroencephalographic activity was translated into contingent hand exoskeleton-driven opening movements of the affected hand. In a control group ( n = 19), hand exoskeleton-driven opening movements of the affected hand were independent of brain electroencephalographic activity. Evaluation of the UL clinical assessments indicated that both groups improved, but only the BCI group showed an improvement in the ARAT's grasp score from 0 [0.0; 14.0] to 3.0 [0.0; 15.0] points ( p exoskeleton-assisted physical therapy can improve post-stroke rehabilitation outcomes. Both maximum and mean values of the percentage of successfully decoded imagery-related EEG activity, were higher than chance level. A correlation between the classification accuracy and the improvement in the upper extremity function was found. An improvement of motor function was found for patients with different duration, severity and location of the stroke.

  5. EEG datasets for motor imagery brain-computer interface.

    Science.gov (United States)

    Cho, Hohyun; Ahn, Minkyu; Ahn, Sangtae; Kwon, Moonyoung; Jun, Sung Chan

    2017-07-01

    Most investigators of brain-computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)-based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states. We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information. Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states. © The Authors 2017. Published by Oxford University Press.

  6. Binary particle swarm optimization for frequency band selection in motor imagery based brain-computer interfaces.

    Science.gov (United States)

    Wei, Qingguo; Wei, Zhonghai

    2015-01-01

    A brain-computer interface (BCI) enables people suffering from affective neurological diseases to communicate with the external world. Common spatial pattern (CSP) is an effective algorithm for feature extraction in motor imagery based BCI systems. However, many studies have proved that the performance of CSP depends heavily on the frequency band of EEG signals used for the construction of covariance matrices. The use of different frequency bands to extract signal features may lead to different classification performances, which are determined by the discriminative and complementary information they contain. In this study, the broad frequency band (8-30 Hz) is divided into 10 sub-bands of band width 4 Hz and overlapping 2 Hz. Binary particle swarm optimization (BPSO) is used to find the best sub-band set to improve the performance of CSP and subsequent classification. Experimental results demonstrate that the proposed method achieved an average improvement of 6.91% in cross-validation accuracy when compared to broad band CSP.

  7. A binary motor imagery tasks based brain-computer interface for two-dimensional movement control

    Science.gov (United States)

    Xia, Bin; Cao, Lei; Maysam, Oladazimi; Li, Jie; Xie, Hong; Su, Caixia; Birbaumer, Niels

    2017-12-01

    Objective. Two-dimensional movement control is a popular issue in brain-computer interface (BCI) research and has many applications in the real world. In this paper, we introduce a combined control strategy to a binary class-based BCI system that allows the user to move a cursor in a two-dimensional (2D) plane. Users focus on a single moving vector to control 2D movement instead of controlling vertical and horizontal movement separately. Approach. Five participants took part in a fixed-target experiment and random-target experiment to verify the effectiveness of the combination control strategy under the fixed and random routine conditions. Both experiments were performed in a virtual 2D dimensional environment and visual feedback was provided on the screen. Main results. The five participants achieved an average hit rate of 98.9% and 99.4% for the fixed-target experiment and the random-target experiment, respectively. Significance. The results demonstrate that participants could move the cursor in the 2D plane effectively. The proposed control strategy is based only on a basic two-motor imagery BCI, which enables more people to use it in real-life applications.

  8. Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface.

    Science.gov (United States)

    Zhang, Yu; Zhou, Guoxu; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

    2015-11-30

    Common spatial pattern (CSP) has been most popularly applied to motor-imagery (MI) feature extraction for classification in brain-computer interface (BCI) application. Successful application of CSP depends on the filter band selection to a large degree. However, the most proper band is typically subject-specific and can hardly be determined manually. This study proposes a sparse filter band common spatial pattern (SFBCSP) for optimizing the spatial patterns. SFBCSP estimates CSP features on multiple signals that are filtered from raw EEG data at a set of overlapping bands. The filter bands that result in significant CSP features are then selected in a supervised way by exploiting sparse regression. A support vector machine (SVM) is implemented on the selected features for MI classification. Two public EEG datasets (BCI Competition III dataset IVa and BCI Competition IV IIb) are used to validate the proposed SFBCSP method. Experimental results demonstrate that SFBCSP help improve the classification performance of MI. The optimized spatial patterns by SFBCSP give overall better MI classification accuracy in comparison with several competing methods. The proposed SFBCSP is a potential method for improving the performance of MI-based BCI. Copyright © 2015 Elsevier B.V. All rights reserved.

  9. Motor imagery learning modulates functional connectivity of multiple brain systems in resting state.

    Science.gov (United States)

    Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

    2014-01-01

    Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning.

  10. Motor Imagery Learning Modulates Functional Connectivity of Multiple Brain Systems in Resting State

    Science.gov (United States)

    Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

    2014-01-01

    Background Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. Methodology/Principal Findings We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. Conclusions/Significance These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning. PMID:24465577

  11. A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Charles Yaacoub

    2017-01-01

    Full Text Available Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5% while improving the accuracy, sensitivity, specificity, and precision of the classifier.

  12. A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface.

    Science.gov (United States)

    Yaacoub, Charles; Mhanna, Georges; Rihana, Sandy

    2017-01-23

    Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier.

  13. An Efficient Framework for EEG Analysis with Application to Hybrid Brain Computer Interfaces Based on Motor Imagery and P300

    Directory of Open Access Journals (Sweden)

    Jinyi Long

    2017-01-01

    Full Text Available The hybrid brain computer interface (BCI based on motor imagery (MI and P300 has been a preferred strategy aiming to improve the detection performance through combining the features of each. However, current methods used for combining these two modalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to optimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a dual spectral norm regularizer is applied to the combined features. Under this framework, the hybrid features of MI and P300 can be learned, selected, and combined together directly. Experimental results on the data set of hybrid BCI based on MI and P300 are provided to illustrate competitive performance of the proposed method against other conventional methods. This provides an evidence that the method used here contributes to the discrimination performance of the brain state in hybrid BCI.

  14. Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial

    Directory of Open Access Journals (Sweden)

    Alexander A. Frolov

    2017-07-01

    Full Text Available Repeated use of brain-computer interfaces (BCIs providing contingent sensory feedback of brain activity was recently proposed as a rehabilitation approach to restore motor function after stroke or spinal cord lesions. However, there are only a few clinical studies that investigate feasibility and effectiveness of such an approach. Here we report on a placebo-controlled, multicenter clinical trial that investigated whether stroke survivors with severe upper limb (UL paralysis benefit from 10 BCI training sessions each lasting up to 40 min. A total of 74 patients participated: median time since stroke is 8 months, 25 and 75% quartiles [3.0; 13.0]; median severity of UL paralysis is 4.5 points [0.0; 30.0] as measured by the Action Research Arm Test, ARAT, and 19.5 points [11.0; 40.0] as measured by the Fugl-Meyer Motor Assessment, FMMA. Patients in the BCI group (n = 55 performed motor imagery of opening their affected hand. Motor imagery-related brain electroencephalographic activity was translated into contingent hand exoskeleton-driven opening movements of the affected hand. In a control group (n = 19, hand exoskeleton-driven opening movements of the affected hand were independent of brain electroencephalographic activity. Evaluation of the UL clinical assessments indicated that both groups improved, but only the BCI group showed an improvement in the ARAT's grasp score from 0 [0.0; 14.0] to 3.0 [0.0; 15.0] points (p < 0.01 and pinch scores from 0.0 [0.0; 7.0] to 1.0 [0.0; 12.0] points (p < 0.01. Upon training completion, 21.8% and 36.4% of the patients in the BCI group improved their ARAT and FMMA scores respectively. The corresponding numbers for the control group were 5.1% (ARAT and 15.8% (FMMA. These results suggests that adding BCI control to exoskeleton-assisted physical therapy can improve post-stroke rehabilitation outcomes. Both maximum and mean values of the percentage of successfully decoded imagery-related EEG activity, were higher

  15. Enhancing performance of a motor imagery based brain-computer interface by incorporating electrical stimulation-induced SSSEP

    Science.gov (United States)

    Yi, Weibo; Qiu, Shuang; Wang, Kun; Qi, Hongzhi; Zhao, Xin; He, Feng; Zhou, Peng; Yang, Jiajia; Ming, Dong

    2017-04-01

    Objective. We proposed a novel simultaneous hybrid brain-computer interface (BCI) by incorporating electrical stimulation into a motor imagery (MI) based BCI system. The goal of this study was to enhance the overall performance of an MI-based BCI. In addition, the brain oscillatory pattern in the hybrid task was also investigated. Approach. 64-channel electroencephalographic (EEG) data were recorded during MI, selective attention (SA) and hybrid tasks in fourteen healthy subjects. In the hybrid task, subjects performed MI with electrical stimulation which was applied to bilateral median nerve on wrists simultaneously. Main results. The hybrid task clearly presented additional steady-state somatosensory evoked potential (SSSEP) induced by electrical stimulation with MI-induced event-related desynchronization (ERD). By combining ERD and SSSEP features, the performance in the hybrid task was significantly better than in both MI and SA tasks, achieving a ~14% improvement in total relative to the MI task alone and reaching ~89% in mean classification accuracy. On the contrary, there was no significant enhancement obtained in performance while separate ERD feature was utilized in the hybrid task. In terms of the hybrid task, the performance using combined feature was significantly better than using separate ERD or SSSEP feature. Significance. The results in this work validate the feasibility of our proposed approach to form a novel MI-SSSEP hybrid BCI outperforming a conventional MI-based BCI through combing MI with electrical stimulation.

  16. Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

    Science.gov (United States)

    Wang, Deng; Miao, Duoqian; Blohm, Gunnar

    2012-01-01

    Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications. PMID:23087607

  17. A Comparison of Independent Event-Related Desynchronization Responses in Motor-Related Brain Areas to Movement Execution, Movement Imagery, and Movement Observation.

    Science.gov (United States)

    Duann, Jeng-Ren; Chiou, Jin-Chern

    2016-01-01

    Electroencephalographic (EEG) event-related desynchronization (ERD) induced by movement imagery or by observing biological movements performed by someone else has recently been used extensively for brain-computer interface-based applications, such as applications used in stroke rehabilitation training and motor skill learning. However, the ERD responses induced by the movement imagery and observation might not be as reliable as the ERD responses induced by movement execution. Given that studies on the reliability of the EEG ERD responses induced by these activities are still lacking, here we conducted an EEG experiment with movement imagery, movement observation, and movement execution, performed multiple times each in a pseudorandomized order in the same experimental runs. Then, independent component analysis (ICA) was applied to the EEG data to find the common motor-related EEG source activity shared by the three motor tasks. Finally, conditional EEG ERD responses associated with the three movement conditions were computed and compared. Among the three motor conditions, the EEG ERD responses induced by motor execution revealed the alpha power suppression with highest strengths and longest durations. The ERD responses of the movement imagery and movement observation only partially resembled the ERD pattern of the movement execution condition, with slightly better detectability for the ERD responses associated with the movement imagery and faster ERD responses for movement observation. This may indicate different levels of involvement in the same motor-related brain circuits during different movement conditions. In addition, because the resulting conditional EEG ERD responses from the ICA preprocessing came with minimal contamination from the non-related and/or artifactual noisy components, this result can play a role of the reference for devising a brain-computer interface using the EEG ERD features of movement imagery or observation.

  18. [The Changes in the Hemodynamic Activity of the Brain during Moroe Imagery Training with the Use of Brain-Computer Interface].

    Science.gov (United States)

    Frolov, A A; Husek, D; Silchenko, A V; Tintera, Y; Rydlo, J

    2016-01-01

    With the use of functional MRI (fMRI), we studied the changes in brain hemodynamic activity of healthy subjects during motor imagery training with the use brain-computer interface (BCI), which is based on the recognition of EEG patterns of imagined movements. ANOVA dispersion analysis showed there are 14 areas of the brain where statistically sgnificant changes were registered. Detailed analysis of the activity in these areas before and after training (Student's and Mann-Whitney tests) reduced the amount of areas with significantly changed activity to five; these are Brodmann areas 44 and 45, insula, middle frontal gyrus, and anterior cingulate gyrus. We suggest that these changes are caused by the formation of memory traces of those brain activity patterns which are most accurately recognized by BCI classifiers as correspondent with limb movements. We also observed a tendency of increase in the activity of motor imagery after training. The hemodynamic activity in all these 14 areas during real movements was either approximatly the same or significantly higher than during motor imagery; activity during imagined leg movements was higher that that during imagined arm movements, except for the areas of representation of arms.

  19. The investigation of brain-computer interface for motor imagery and execution using functional near-infrared spectroscopy

    Science.gov (United States)

    Zhang, Zhen; Jiao, Xuejun; Xu, Fengang; Jiang, Jin; Yang, Hanjun; Cao, Yong; Fu, Jiahao

    2017-01-01

    Functional near-infrared spectroscopy (fNIRS), which can measure cortex hemoglobin activity, has been widely adopted in brain-computer interface (BCI). To explore the feasibility of recognizing motor imagery (MI) and motor execution (ME) in the same motion. We measured changes of oxygenated hemoglobin (HBO) and deoxygenated hemoglobin (HBR) on PFC and Motor Cortex (MC) when 15 subjects performing hand extension and finger tapping tasks. The mean, slope, quadratic coefficient and approximate entropy features were extracted from HBO as the input of support vector machine (SVM). For the four-class fNIRS-BCI classifiers, we realized 87.65% and 87.58% classification accuracy corresponding to hand extension and finger tapping tasks. In conclusion, it is effective for fNIRS-BCI to recognize MI and ME in the same motion.

  20. Short-lived brain state after cued motor imagery in naive subjects

    NARCIS (Netherlands)

    Pfurtscheller, G.; Scherer, R.; Müller-Putz, G.R.; Lopes da Silva, F.H.

    2008-01-01

    Multi-channel electroencephalography recordings have shown that a visual cue, indicating right hand, left hand or foot motor imagery, can induce a short-lived brain state in the order of about 500 ms. In the present study, 10 able-bodied subjects without any motor imagery experience (naive subjects)

  1. Localization of Brain Electrical Activity Sources and Hemodynamic Activity Foci during Motor Imagery

    Czech Academy of Sciences Publication Activity Database

    Frolov, A. A.; Húsek, Dušan; Mokienko, O.; Bobrov, P.; Chernikova, L.; Konovalov, R.

    2014-01-01

    Roč. 40, č. 3 (2014), s. 273-283 ISSN 0362-1197 Grant - others:GA MŠk(CZ) ED1.1.00/02.0070; GA MŠk(CZ) EE.2.3.20.0073 Program:ED Institutional support: RVO:67985807 Keywords : brain computer interface * independent component analysis * EEG pattern classification * motor imagery * inverse EEG problem Subject RIV: IN - Informatics, Computer Science

  2. Analisa Spektrum Motor Imagery pada Sinyal Aktivitas Otak

    Directory of Open Access Journals (Sweden)

    Johan Chandra

    2017-01-01

    Full Text Available Otak merupakan organ vital pada tubuh manusia yang berperan sebagai pusat kendali sistem saraf manusia. Sinyal yang dikeluarkan otak (EEG mengandung berbagai informasi yang dapat dimanfaatkan pada teknologi BCI. Salah satu informasi yang dapat digunakan adalah informasi motorik baik mengenai motor execution maupung motor imagery. Pada penderita stroke yang biasanya mengalami kelumpuhan pada anggota gerak tubuhnya, informasi mengenai motor imagery dapat dimanfaatkan untuk aplikasi Brain Computer Interface terutama dalam rehabilitasi kelumpuhan anggota gerak pasien tersebut. Pada penelitian ini dirancang sebuah alat sistem EEG untuk merekam sinyal EEG pada otak untuk menganalisa spektrum motor imagery pada sinyal aktivitas otak. Sistem terdiri dari rangkaian filter pasif, rangkaian proteksi, penguat isntrumentasi, common mode rejection, amplifier, dan filter. Pengujian dilakukan dengan membandingkan sinyal EEG pada tasking motor imagery dan motor execution. Selanjutnya, informasi motorik baik motor execution dan motor imagery dapat diaplikasikan lebih lanjut pada sistem BCI terutama pada rehabilitasi medik.

  3. An efficient rhythmic component expression and weighting synthesis strategy for classifying motor imagery EEG in a brain computer interface

    Science.gov (United States)

    Wang, Tao; He, Bin

    2004-03-01

    The recognition of mental states during motor imagery tasks is crucial for EEG-based brain computer interface research. We have developed a new algorithm by means of frequency decomposition and weighting synthesis strategy for recognizing imagined right- and left-hand movements. A frequency range from 5 to 25 Hz was divided into 20 band bins for each trial, and the corresponding envelopes of filtered EEG signals for each trial were extracted as a measure of instantaneous power at each frequency band. The dimensionality of the feature space was reduced from 200 (corresponding to 2 s) to 3 by down-sampling of envelopes of the feature signals, and subsequently applying principal component analysis. The linear discriminate analysis algorithm was then used to classify the features, due to its generalization capability. Each frequency band bin was weighted by a function determined according to the classification accuracy during the training process. The present classification algorithm was applied to a dataset of nine human subjects, and achieved a success rate of classification of 90% in training and 77% in testing. The present promising results suggest that the present classification algorithm can be used in initiating a general-purpose mental state recognition based on motor imagery tasks.

  4. Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain-computer interface.

    Science.gov (United States)

    Siuly; Li, Yan; Paul Wen, Peng

    2014-03-01

    Motor imagery (MI) tasks classification provides an important basis for designing brain-computer interface (BCI) systems. If the MI tasks are reliably distinguished through identifying typical patterns in electroencephalography (EEG) data, a motor disabled people could communicate with a device by composing sequences of these mental states. In our earlier study, we developed a cross-correlation based logistic regression (CC-LR) algorithm for the classification of MI tasks for BCI applications, but its performance was not satisfactory. This study develops a modified version of the CC-LR algorithm exploring a suitable feature set that can improve the performance. The modified CC-LR algorithm uses the C3 electrode channel (in the international 10-20 system) as a reference channel for the cross-correlation (CC) technique and applies three diverse feature sets separately, as the input to the logistic regression (LR) classifier. The present algorithm investigates which feature set is the best to characterize the distribution of MI tasks based EEG data. This study also provides an insight into how to select a reference channel for the CC technique with EEG signals considering the anatomical structure of the human brain. The proposed algorithm is compared with eight of the most recently reported well-known methods including the BCI III Winner algorithm. The findings of this study indicate that the modified CC-LR algorithm has potential to improve the identification performance of MI tasks in BCI systems. The results demonstrate that the proposed technique provides a classification improvement over the existing methods tested. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  5. Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification

    Science.gov (United States)

    Dai, Mengxi; Liu, Shucong; Zhang, Pengju

    2018-01-01

    Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods. PMID:29743934

  6. Active training paradigm for motor imagery BCI.

    Science.gov (United States)

    Li, Junhua; Zhang, Liqing

    2012-06-01

    Brain-computer interface (BCI) allows the use of brain activities for people to directly communicate with the external world or to control external devices without participation of any peripheral nerves and muscles. Motor imagery is one of the most popular modes in the research field of brain-computer interface. Although motor imagery BCI has some advantages compared with other modes of BCI, such as asynchronization, it is necessary to require training sessions before using it. The performance of trained BCI system depends on the quality of training samples or the subject engagement. In order to improve training effect and decrease training time, we proposed a new paradigm where subjects participated in training more actively than in the traditional paradigm. In the traditional paradigm, a cue (to indicate what kind of motor imagery should be imagined during the current trial) is given to the subject at the beginning of a trial or during a trial, and this cue is also used as a label for this trial. It is usually assumed that labels for trials are accurate in the traditional paradigm, although subjects may not have performed the required or correct kind of motor imagery, and trials may thus be mislabeled. And then those mislabeled trials give rise to interference during model training. In our proposed paradigm, the subject is required to reconfirm the label and can correct the label when necessary. This active training paradigm may generate better training samples with fewer inconsistent labels because it overcomes mistakes when subject's motor imagination does not match the given cues. The experiments confirm that our proposed paradigm achieves better performance; the improvement is significant according to statistical analysis.

  7. Effector-independent brain activity during motor imagery of the upper and lower limbs: an fMRI study.

    Science.gov (United States)

    Mizuguchi, Nobuaki; Nakata, Hiroki; Kanosue, Kazuyuki

    2014-10-03

    We utilized functional magnetic resonance imaging (fMRI) to evaluate the common brain region of motor imagery for the right and left upper and lower limbs. The subjects were instructed to repeatedly imagined extension and flexion of the right or left hands/ankles. Brain regions, which included the supplemental motor area (SMA), premotor cortex and parietal cortex, were activated during motor imagery. Conjunction analysis revealed that the left SMA and inferior frontal gyrus (IFG)/ventral premotor cortex (vPM) were commonly activated with motor imagery of the right hand, left hand, right foot, and left foot. This result suggests that these brain regions are activated during motor imagery in an effector independent manner. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  8. Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller

    Science.gov (United States)

    Perdikis, S.; Leeb, R.; Williamson, J.; Ramsay, A.; Tavella, M.; Desideri, L.; Hoogerwerf, E.-J.; Al-Khodairy, A.; Murray-Smith, R.; Millán, J. d. R.

    2014-06-01

    Objective. While brain-computer interfaces (BCIs) for communication have reached considerable technical maturity, there is still a great need for state-of-the-art evaluation by the end-users outside laboratory environments. To achieve this primary objective, it is necessary to augment a BCI with a series of components that allow end-users to type text effectively. Approach. This work presents the clinical evaluation of a motor imagery (MI) BCI text-speller, called BrainTree, by six severely disabled end-users and ten able-bodied users. Additionally, we define a generic model of code-based BCI applications, which serves as an analytical tool for evaluation and design. Main results. We show that all users achieved remarkable usability and efficiency outcomes in spelling. Furthermore, our model-based analysis highlights the added value of human-computer interaction techniques and hybrid BCI error-handling mechanisms, and reveals the effects of BCI performances on usability and efficiency in code-based applications. Significance. This study demonstrates the usability potential of code-based MI spellers, with BrainTree being the first to be evaluated by a substantial number of end-users, establishing them as a viable, competitive alternative to other popular BCI spellers. Another major outcome of our model-based analysis is the derivation of a 80% minimum command accuracy requirement for successful code-based application control, revising upwards previous estimates attempted in the literature.

  9. Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface.

    Science.gov (United States)

    Naseer, Noman; Hong, Keum-Shik

    2013-10-11

    This paper presents a study on functional near-infrared spectroscopy (fNIRS) indicating that the hemodynamic responses of the right- and left-wrist motor imageries have distinct patterns that can be classified using a linear classifier for the purpose of developing a brain-computer interface (BCI). Ten healthy participants were instructed to imagine kinesthetically the right- or left-wrist flexion indicated on a computer screen. Signals from the right and left primary motor cortices were acquired simultaneously using a multi-channel continuous-wave fNIRS system. Using two distinct features (the mean and the slope of change in the oxygenated hemoglobin concentration), the linear discriminant analysis classifier was used to classify the right- and left-wrist motor imageries resulting in average classification accuracies of 73.35% and 83.0%, respectively, during the 10s task period. Moreover, when the analysis time was confined to the 2-7s span within the overall 10s task period, the average classification accuracies were improved to 77.56% and 87.28%, respectively. These results demonstrate the feasibility of an fNIRS-based BCI and the enhanced performance of the classifier by removing the initial 2s span and/or the time span after the peak value. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  10. fNIRS-based brain-computer interfaces: a review

    Directory of Open Access Journals (Sweden)

    Noman eNaseer

    2015-01-01

    Full Text Available A brain-computer interface (BCI is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis, multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine, hidden Markov model, artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.

  11. Selective sensation based brain-computer interface via mechanical vibrotactile stimulation.

    Science.gov (United States)

    Yao, Lin; Meng, Jianjun; Zhang, Dingguo; Sheng, Xinjun; Zhu, Xiangyang

    2013-01-01

    In this work, mechanical vibrotactile stimulation was applied to subjects' left and right wrist skins with equal intensity, and a selective sensation perception task was performed to achieve two types of selections similar to motor imagery Brain-Computer Interface. The proposed system was based on event-related desynchronization/synchronization (ERD/ERS), which had a correlation with processing of afferent inflow in human somatosensory system, and attentional effect which modulated the ERD/ERS. The experiments were carried out on nine subjects (without experience in selective sensation), and six of them showed a discrimination accuracy above 80%, three of them above 95%. Comparative experiments with motor imagery (with and without presence of stimulation) were also carried out, which further showed the feasibility of selective sensation as an alternative BCI task complementary to motor imagery. Specifically there was significant improvement ([Formula: see text]) from near 65% in motor imagery (with and without presence of stimulation) to above 80% in selective sensation on some subjects. The proposed BCI modality might well cooperate with existing BCI modalities in the literature in enlarging the widespread usage of BCI system.

  12. Selective Sensation Based Brain-Computer Interface via Mechanical Vibrotactile Stimulation

    Science.gov (United States)

    Yao, Lin; Meng, Jianjun; Zhang, Dingguo; Sheng, Xinjun; Zhu, Xiangyang

    2013-01-01

    In this work, mechanical vibrotactile stimulation was applied to subjects’ left and right wrist skins with equal intensity, and a selective sensation perception task was performed to achieve two types of selections similar to motor imagery Brain-Computer Interface. The proposed system was based on event-related desynchronization/synchronization (ERD/ERS), which had a correlation with processing of afferent inflow in human somatosensory system, and attentional effect which modulated the ERD/ERS. The experiments were carried out on nine subjects (without experience in selective sensation), and six of them showed a discrimination accuracy above 80%, three of them above 95%. Comparative experiments with motor imagery (with and without presence of stimulation) were also carried out, which further showed the feasibility of selective sensation as an alternative BCI task complementary to motor imagery. Specifically there was significant improvement () from near 65% in motor imagery (with and without presence of stimulation) to above 80% in selective sensation on some subjects. The proposed BCI modality might well cooperate with existing BCI modalities in the literature in enlarging the widespread usage of BCI system. PMID:23762253

  13. Motor imagery beyond the motor repertoire: Activity in the primary visual cortex during kinesthetic motor imagery of difficult whole body movements.

    Science.gov (United States)

    Mizuguchi, N; Nakata, H; Kanosue, K

    2016-02-19

    To elucidate the neural substrate associated with capabilities for kinesthetic motor imagery of difficult whole-body movements, we measured brain activity during a trial involving both kinesthetic motor imagery and action observation as well as during a trial with action observation alone. Brain activity was assessed with functional magnetic resonance imaging (fMRI). Nineteen participants imagined three types of whole-body movements with the horizontal bar: the giant swing, kip, and chin-up during action observation. No participant had previously tried to perform the giant swing. The vividness of kinesthetic motor imagery as assessed by questionnaire was highest for the chin-up, less for the kip and lowest for the giant swing. Activity in the primary visual cortex (V1) during kinesthetic motor imagery with action observation minus that during action observation alone was significantly greater in the giant swing condition than in the chin-up condition within participants. Across participants, V1 activity of kinesthetic motor imagery of the kip during action observation minus that during action observation alone was negatively correlated with vividness of the kip imagery. These results suggest that activity in V1 is dependent upon the capability of kinesthetic motor imagery for difficult whole-body movements. Since V1 activity is likely related to the creation of a visual image, we speculate that visual motor imagery is recruited unintentionally for the less vivid kinesthetic motor imagery of difficult whole-body movements. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  14. Structure constrained semi-nonnegative matrix factorization for EEG-based motor imagery classification.

    Science.gov (United States)

    Lu, Na; Li, Tengfei; Pan, Jinjin; Ren, Xiaodong; Feng, Zuren; Miao, Hongyu

    2015-05-01

    Electroencephalogram (EEG) provides a non-invasive approach to measure the electrical activities of brain neurons and has long been employed for the development of brain-computer interface (BCI). For this purpose, various patterns/features of EEG data need to be extracted and associated with specific events like cue-paced motor imagery. However, this is a challenging task since EEG data are usually non-stationary time series with a low signal-to-noise ratio. In this study, we propose a novel method, called structure constrained semi-nonnegative matrix factorization (SCS-NMF), to extract the key patterns of EEG data in time domain by imposing the mean envelopes of event-related potentials (ERPs) as constraints on the semi-NMF procedure. The proposed method is applicable to general EEG time series, and the extracted temporal features by SCS-NMF can also be combined with other features in frequency domain to improve the performance of motor imagery classification. Real data experiments have been performed using the SCS-NMF approach for motor imagery classification, and the results clearly suggest the superiority of the proposed method. Comparison experiments have also been conducted. The compared methods include ICA, PCA, Semi-NMF, Wavelets, EMD and CSP, which further verified the effectivity of SCS-NMF. The SCS-NMF method could obtain better or competitive performance over the state of the art methods, which provides a novel solution for brain pattern analysis from the perspective of structure constraint. Copyright © 2015 Elsevier Ltd. All rights reserved.

  15. Proprioceptive feedback and brain computer interface (BCI based neuroprostheses.

    Directory of Open Access Journals (Sweden)

    Ander Ramos-Murguialday

    Full Text Available Brain computer interface (BCI technology has been proposed for motor neurorehabilitation, motor replacement and assistive technologies. It is an open question whether proprioceptive feedback affects the regulation of brain oscillations and therefore BCI control. We developed a BCI coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers. 24 healthy participants performed five different tasks of closing and opening the hand: (1 motor imagery of the hand movement without any overt movement and without feedback, (2 motor imagery with movement as online feedback (participants see and feel their hand, with the exoskeleton moving according to their brain signals, (3 passive (the orthosis passively opens and closes the hand without imagery and (4 active (overt movement of the hand and rest. Performance was defined as the difference in power of the sensorimotor rhythm during motor task and rest and calculated offline for different tasks. Participants were divided in three groups depending on the feedback receiving during task 2 (the other tasks were the same for all participants. Group 1 (n = 9 received contingent positive feedback (participants' sensorimotor rhythm (SMR desynchronization was directly linked to hand orthosis movements, group 2 (n = 8 contingent "negative" feedback (participants' sensorimotor rhythm synchronization was directly linked to hand orthosis movements and group 3 (n = 7 sham feedback (no link between brain oscillations and orthosis movements. We observed that proprioceptive feedback (feeling and seeing hand movements improved BCI performance significantly. Furthermore, in the contingent positive group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. Furthermore, we observed a significantly stronger SMR desynchronization in the contingent positive group compared to the other groups during active and

  16. Proprioceptive feedback and brain computer interface (BCI) based neuroprostheses.

    Science.gov (United States)

    Ramos-Murguialday, Ander; Schürholz, Markus; Caggiano, Vittorio; Wildgruber, Moritz; Caria, Andrea; Hammer, Eva Maria; Halder, Sebastian; Birbaumer, Niels

    2012-01-01

    Brain computer interface (BCI) technology has been proposed for motor neurorehabilitation, motor replacement and assistive technologies. It is an open question whether proprioceptive feedback affects the regulation of brain oscillations and therefore BCI control. We developed a BCI coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers. 24 healthy participants performed five different tasks of closing and opening the hand: (1) motor imagery of the hand movement without any overt movement and without feedback, (2) motor imagery with movement as online feedback (participants see and feel their hand, with the exoskeleton moving according to their brain signals, (3) passive (the orthosis passively opens and closes the hand without imagery) and (4) active (overt) movement of the hand and rest. Performance was defined as the difference in power of the sensorimotor rhythm during motor task and rest and calculated offline for different tasks. Participants were divided in three groups depending on the feedback receiving during task 2 (the other tasks were the same for all participants). Group 1 (n = 9) received contingent positive feedback (participants' sensorimotor rhythm (SMR) desynchronization was directly linked to hand orthosis movements), group 2 (n = 8) contingent "negative" feedback (participants' sensorimotor rhythm synchronization was directly linked to hand orthosis movements) and group 3 (n = 7) sham feedback (no link between brain oscillations and orthosis movements). We observed that proprioceptive feedback (feeling and seeing hand movements) improved BCI performance significantly. Furthermore, in the contingent positive group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. Furthermore, we observed a significantly stronger SMR desynchronization in the contingent positive group compared to the other groups during active and passive

  17. Motor imagery enhancement paradigm using moving rubber hand illusion system.

    Science.gov (United States)

    Minsu Song; Jonghyun Kim

    2017-07-01

    Motor imagery (MI) has been widely used in neurorehabilitation and brain computer interface. The size of event-related desynchronization (ERD) is a key parameter for successful motor imaginary rehabilitation and BCI adaptation. Many studies have used visual guidance for enhancement/ amplification of motor imagery ERD amplitude, but their enhancements were not significant. We propose a novel ERD enhancing paradigm using body-ownership illusion, or also known as rubber hand illusion (RHI). The system was made by motorized, moving rubber hand which can simulate wrist extension. The amplifying effects of the proposed RHI paradigm were evaluated by comparing ERD sizes of the proposed paradigm with motor imagery and actual motor execution paradigms. The comparison result shows that the improvement of ERD size due to the proposed paradigm was statistically significant (pparadigms.

  18. A neural network-based optimal spatial filter design method for motor imagery classification.

    Directory of Open Access Journals (Sweden)

    Ayhan Yuksel

    Full Text Available In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classification method combined with spatial filter optimization. We simultaneously train the spatial filter and the classifier using a neural network approach. The proposed spatial filter network (SFN is composed of two layers: a spatial filtering layer and a classifier layer. These two layers are linked to each other with non-linear mapping functions. The proposed method addresses two shortcomings of the common spatial patterns (CSP algorithm. First, CSP aims to maximize the between-classes variance while ignoring the minimization of within-classes variances. Consequently, the features obtained using the CSP method may have large within-classes variances. Second, the maximizing optimization function of CSP increases the classification accuracy indirectly because an independent classifier is used after the CSP method. With SFN, we aimed to maximize the between-classes variance while minimizing within-classes variances and simultaneously optimizing the spatial filter and the classifier. To classify motor imagery EEG signals, we modified the well-known feed-forward structure and derived forward and backward equations that correspond to the proposed structure. We tested our algorithm on simple toy data. Then, we compared the SFN with conventional CSP and its multi-class version, called one-versus-rest CSP, on two data sets from BCI competition III. The evaluation results demonstrate that SFN is a good alternative for classifying motor imagery EEG signals with increased classification accuracy.

  19. Improved Volitional Recall of Motor-Imagery-Related Brain Activation Patterns Using Real-Time Functional MRI-Based Neurofeedback.

    Science.gov (United States)

    Bagarinao, Epifanio; Yoshida, Akihiro; Ueno, Mika; Terabe, Kazunori; Kato, Shohei; Isoda, Haruo; Nakai, Toshiharu

    2018-01-01

    Motor imagery (MI), a covert cognitive process where an action is mentally simulated but not actually performed, could be used as an effective neurorehabilitation tool for motor function improvement or recovery. Recent approaches employing brain-computer/brain-machine interfaces to provide online feedback of the MI during rehabilitation training have promising rehabilitation outcomes. In this study, we examined whether participants could volitionally recall MI-related brain activation patterns when guided using neurofeedback (NF) during training. The participants' performance was compared to that without NF. We hypothesized that participants would be able to consistently generate the relevant activation pattern associated with the MI task during training with NF compared to that without NF. To assess activation consistency, we used the performance of classifiers trained to discriminate MI-related brain activation patterns. Our results showed significantly higher predictive values of MI-related activation patterns during training with NF. Additionally, this improvement in the classification performance tends to be associated with the activation of middle temporal gyrus/inferior occipital gyrus, a region associated with visual motion processing, suggesting the importance of performance monitoring during MI task training. Taken together, these findings suggest that the efficacy of MI training, in terms of generating consistent brain activation patterns relevant to the task, can be enhanced by using NF as a mechanism to enable participants to volitionally recall task-related brain activation patterns.

  20. Imitation and matching of meaningless gestures: distinct involvement from motor and visual imagery.

    Science.gov (United States)

    Lesourd, Mathieu; Navarro, Jordan; Baumard, Josselin; Jarry, Christophe; Le Gall, Didier; Osiurak, François

    2017-05-01

    The aim of the present study was to understand the underlying cognitive processes of imitation and matching of meaningless gestures. Neuropsychological evidence obtained in brain damaged patients, has shown that distinct cognitive processes supported imitation and matching of meaningless gestures. Left-brain damaged (LBD) patients failed to imitate while right-brain damaged (RBD) patients failed to match meaningless gestures. Moreover, other studies with brain damaged patients showed that LBD patients were impaired in motor imagery while RBD patients were impaired in visual imagery. Thus, we hypothesize that imitation of meaningless gestures might rely on motor imagery, whereas matching of meaningless gestures might be based on visual imagery. In a first experiment, using a correlational design, we demonstrated that posture imitation relies on motor imagery but not on visual imagery (Experiment 1a) and that posture matching relies on visual imagery but not on motor imagery (Experiment 1b). In a second experiment, by manipulating directly the body posture of the participants, we demonstrated that such manipulation evokes a difference only in imitation task but not in matching task. In conclusion, the present study provides direct evidence that the way we imitate or we have to compare postures depends on motor imagery or visual imagery, respectively. Our results are discussed in the light of recent findings about underlying mechanisms of meaningful and meaningless gestures.

  1. Improved Volitional Recall of Motor-Imagery-Related Brain Activation Patterns Using Real-Time Functional MRI-Based Neurofeedback

    Directory of Open Access Journals (Sweden)

    Epifanio Bagarinao

    2018-04-01

    Full Text Available Motor imagery (MI, a covert cognitive process where an action is mentally simulated but not actually performed, could be used as an effective neurorehabilitation tool for motor function improvement or recovery. Recent approaches employing brain–computer/brain–machine interfaces to provide online feedback of the MI during rehabilitation training have promising rehabilitation outcomes. In this study, we examined whether participants could volitionally recall MI-related brain activation patterns when guided using neurofeedback (NF during training. The participants’ performance was compared to that without NF. We hypothesized that participants would be able to consistently generate the relevant activation pattern associated with the MI task during training with NF compared to that without NF. To assess activation consistency, we used the performance of classifiers trained to discriminate MI-related brain activation patterns. Our results showed significantly higher predictive values of MI-related activation patterns during training with NF. Additionally, this improvement in the classification performance tends to be associated with the activation of middle temporal gyrus/inferior occipital gyrus, a region associated with visual motion processing, suggesting the importance of performance monitoring during MI task training. Taken together, these findings suggest that the efficacy of MI training, in terms of generating consistent brain activation patterns relevant to the task, can be enhanced by using NF as a mechanism to enable participants to volitionally recall task-related brain activation patterns.

  2. Motor imagery and swallowing: a systematic literature review

    Directory of Open Access Journals (Sweden)

    Ada Salvetti Cavalcanti Caldas

    Full Text Available ABSTRACT Objetive: to identify, in the literature, studies that address the use of motor imagery of swallowing. Methods: a systematic review in SCOPUS databases, Science Direct and Medline, with descriptors and free terms "Motor Imagery"; "Swallow"; "Feeding"; "Stomatognathic System"; "mastication ", "Chew "; "Deglutition "; "Deglutition Disorders "; and "Mental Practice". Original articles using the motor imagery of swallowing were included, while reviews were excluded. For data analysis, at the first and second steps, the reading of titles and abstracts of the studies was carried out. In the third step, all studies that were not excluded were read in full. Results: four manuscripts were selected. The use of motor imagery in the rehabilitation of swallowing shows to be a recent proposal (2014-2015. The sample was reduced and comprised mainly healthy individuals. The EMG of the supra-hyoid muscles was used in two manuscripts. The most used neuroimaging technique was the Near-Infrared Spectroscopy, demonstrating the occurrence of hemodynamic changes during motor imagery and motor execution of swallowing. Conclusion: the motor imagery produces brain response in the motor area of the brain, suggesting that mentalization of actions related to swallowing is effective. However, further studies are needed for the application of this approach in the swallowing rehabilitation.

  3. A subject-independent pattern-based Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Andreas Markus Ray

    2015-10-01

    Full Text Available While earlier Brain-Computer Interface (BCI studies have mostly focused on modulating specific brain regions or signals, new developments in pattern classification of brain states are enabling real-time decoding and modulation of an entire functional network. The present study proposes a new method for real-time pattern classification and neurofeedback of brain states from electroencephalographic (EEG signals. It involves the creation of a fused classification model based on the method of Common Spatial Patterns (CSPs from data of several healthy individuals. The subject-independent model is then used to classify EEG data in real-time and provide feedback to new individuals. In a series of offline experiments involving training and testing of the classifier with individual data from 27 healthy subjects, a mean classification accuracy of 75.30% was achieved, demonstrating that the classification system at hand can reliably decode two types of imagery used in our experiments, i.e. happy emotional imagery and motor imagery. In a subsequent experiment it is shown that the classifier can be used to provide neurofeedback to new subjects, and that these subjects learn to match their brain pattern to that of the fused classification model in a few days of neurofeedback training. This finding can have important implications for future studies on neurofeedback and its clinical applications on neuropsychiatric disorders.

  4. Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata.

    Science.gov (United States)

    Liu, Aiming; Chen, Kun; Liu, Quan; Ai, Qingsong; Xie, Yi; Chen, Anqi

    2017-11-08

    Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain-computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain-computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain-computer interface systems.

  5. Real time system design of motor imagery brain-computer interface based on multi band CSP and SVM

    Science.gov (United States)

    Zhao, Li; Li, Xiaoqin; Bian, Yan

    2018-04-01

    Motion imagery (MT) is an effective method to promote the recovery of limbs in patients after stroke. Though an online MT brain computer interface (BCT) system, which apply MT, can enhance the patient's participation and accelerate their recovery process. The traditional method deals with the electroencephalogram (EEG) induced by MT by common spatial pattern (CSP), which is used to extract information from a frequency band. Tn order to further improve the classification accuracy of the system, information of two characteristic frequency bands is extracted. The effectiveness of the proposed feature extraction method is verified by off-line analysis of competition data and the analysis of online system.

  6. Achieving a hybrid brain-computer interface with tactile selective attention and motor imagery

    Science.gov (United States)

    Ahn, Sangtae; Ahn, Minkyu; Cho, Hohyun; Jun, Sung Chan

    2014-12-01

    Objective. We propose a new hybrid brain-computer interface (BCI) system that integrates two different EEG tasks: tactile selective attention (TSA) using a vibro-tactile stimulator on the left/right finger and motor imagery (MI) of left/right hand movement. Event-related desynchronization (ERD) from the MI task and steady-state somatosensory evoked potential (SSSEP) from the TSA task are retrieved and combined into two hybrid senses. Approach. One hybrid approach is to measure two tasks simultaneously; the features of each task are combined for testing. Another hybrid approach is to measure two tasks consecutively (TSA first and MI next) using only MI features. For comparison with the hybrid approaches, the TSA and MI tasks are measured independently. Main results. Using a total of 16 subject datasets, we analyzed the BCI classification performance for MI, TSA and two hybrid approaches in a comparative manner; we found that the consecutive hybrid approach outperformed the others, yielding about a 10% improvement in classification accuracy relative to MI alone. It is understood that TSA may play a crucial role as a prestimulus in that it helps to generate earlier ERD prior to MI and thus sustains ERD longer and to a stronger degree; this ERD may give more discriminative information than ERD in MI alone. Significance. Overall, our proposed consecutive hybrid approach is very promising for the development of advanced BCI systems.

  7. Combining motor imagery with selective sensation toward a hybrid-modality BCI.

    Science.gov (United States)

    Yao, Lin; Meng, Jianjun; Zhang, Dingguo; Sheng, Xinjun; Zhu, Xiangyang

    2014-08-01

    A hybrid modality brain-computer interface (BCI) is proposed in this paper, which combines motor imagery with selective sensation to enhance the discrimination between left and right mental tasks, e.g., the classification between left/ right stimulation sensation and right/ left motor imagery. In this paradigm, wearable vibrotactile rings are used to stimulate both the skin on both wrists. Subjects are required to perform the mental tasks according to the randomly presented cues (i.e., left hand motor imagery, right hand motor imagery, left stimulation sensation or right stimulation sensation). Two-way ANOVA statistical analysis showed a significant group effect (F (2,20) = 7.17, p = 0.0045), and the Benferroni-corrected multiple comparison test (with α = 0.05) showed that the hybrid modality group is 11.13% higher on average than the motor imagery group, and 10.45% higher than the selective sensation group. The hybrid modality experiment exhibits potentially wider spread usage within ten subjects crossed 70% accuracy, followed by four subjects in motor imagery and five subjects in selective sensation. Six subjects showed statistically significant improvement ( Benferroni-corrected) in hybrid modality in comparison with both motor imagery and selective sensation. Furthermore, among subjects having difficulties in both motor imagery and selective sensation, the hybrid modality improves their performance to 90% accuracy. The proposed hybrid modality BCI has demonstrated clear benefits for those poorly performing BCI users. Not only does the requirement of motor and sensory anticipation in this hybrid modality provide basic function of BCI for communication and control, it also has the potential for enhancing the rehabilitation during motor recovery.

  8. Low Intensity Focused tDCS Over the Motor Cortex Shows Inefficacy to Improve Motor Imagery Performance

    Directory of Open Access Journals (Sweden)

    Irma N. Angulo-Sherman

    2017-07-01

    Full Text Available Transcranial direct current stimulation (tDCS is a brain stimulation technique that can enhance motor activity by stimulating the motor path. Thus, tDCS has the potential of improving the performance of brain-computer interfaces during motor neurorehabilitation. tDCS effects depend on several aspects, including the current density, which usually varies between 0.02 and 0.08 mA/cm2, and the location of the stimulation electrodes. Hence, testing tDCS montages at several current levels would allow the selection of current parameters for improving stimulation outcomes and the comparison of montages. In a previous study, we found that cortico-cerebellar tDCS shows potential of enhancing right-hand motor imagery. In this paper, we aim to evaluate the effects of the focal stimulation of the motor cortex over motor imagery. In particular, the effect of supplying tDCS with a 4 × 1 ring montage, which consists in placing an anode on the motor cortex and four cathodes around it, over motor imagery was assessed with different current densities. Electroencephalographic (EEG classification into rest or right-hand/feet motor imagery was evaluated on five healthy subjects for two stimulation schemes: applying tDCS for 10 min on the (1 right-hand or (2 feet motor cortex before EEG recording. Accuracy differences related to the tDCS intensity, as well as μ and β band power changes, were tested for each subject and tDCS modality. In addition, a simulation of the electric field induced by the montage was used to describe its effect on the brain. Results show no improvement trends on classification for the evaluated currents, which is in accordance with the observation of variable EEG band power results despite the focused stimulation. The lack of effects is probably related to the underestimation of the current intensity required to apply a particular current density for small electrodes and the relatively short inter-electrode distance. Hence, higher current

  9. Performance evaluation of a motor-imagery-based EEG-Brain computer interface using a combined cue with heterogeneous training data in BCI-Naive subjects

    Directory of Open Access Journals (Sweden)

    Lee Youngbum

    2011-10-01

    Full Text Available Abstract Background The subjects in EEG-Brain computer interface (BCI system experience difficulties when attempting to obtain the consistent performance of the actual movement by motor imagery alone. It is necessary to find the optimal conditions and stimuli combinations that affect the performance factors of the EEG-BCI system to guarantee equipment safety and trust through the performance evaluation of using motor imagery characteristics that can be utilized in the EEG-BCI testing environment. Methods The experiment was carried out with 10 experienced subjects and 32 naive subjects on an EEG-BCI system. There were 3 experiments: The experienced homogeneous experiment, the naive homogeneous experiment and the naive heterogeneous experiment. Each experiment was compared in terms of the six audio-visual cue combinations and consisted of 50 trials. The EEG data was classified using the least square linear classifier in case of the naive subjects through the common spatial pattern filter. The accuracy was calculated using the training and test data set. The p-value of the accuracy was obtained through the statistical significance test. Results In the case in which a naive subject was trained by a heterogeneous combined cue and tested by a visual cue, the result was not only the highest accuracy (p Conclusions We propose the use of this measuring methodology of a heterogeneous combined cue for training data and a visual cue for test data by the typical EEG-BCI algorithm on the EEG-BCI system to achieve effectiveness in terms of consistence, stability, cost, time, and resources management without the need for a trial and error process.

  10. Brain-computer interfacing under distraction: an evaluation study

    DEFF Research Database (Denmark)

    Brandl, Stephanie; Frølich, Laura; Höhne, Johannes

    2016-01-01

    Objective. While motor-imagery based brain-computer interfaces (BCIs) have been studied over many years by now, most of these studies have taken place in controlled lab settings. Bringing BCI technology into everyday life is still one of the main challenges in this field of research. Approach...

  11. A brain computer interface-based explorer.

    Science.gov (United States)

    Bai, Lijuan; Yu, Tianyou; Li, Yuanqing

    2015-04-15

    In recent years, various applications of brain computer interfaces (BCIs) have been studied. In this paper, we present a hybrid BCI combining P300 and motor imagery to operate an explorer. Our system is mainly composed of a BCI mouse, a BCI speller and an explorer. Through this system, the user can access his computer and manipulate (open, close, copy, paste, and delete) files such as documents, pictures, music, movies and so on. The system has been tested with five subjects, and the experimental results show that the explorer can be successfully operated according to subjects' intentions. Copyright © 2014 Elsevier B.V. All rights reserved.

  12. Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere.

    Science.gov (United States)

    Xu, Fangzhou; Zhou, Weidong; Zhen, Yilin; Yuan, Qi; Wu, Qi

    2016-09-01

    The feature extraction and classification of brain signal is very significant in brain-computer interface (BCI). In this study, we describe an algorithm for motor imagery (MI) classification of electrocorticogram (ECoG)-based BCI. The proposed approach employs multi-resolution fractal measures and local binary pattern (LBP) operators to form a combined feature for characterizing an ECoG epoch recording from the right hemisphere of the brain. A classifier is trained by using the gradient boosting in conjunction with ordinary least squares (OLS) method. The fractal intercept, lacunarity and LBP features are extracted to classify imagined movements of either the left small finger or the tongue. Experimental results on dataset I of BCI competition III demonstrate the superior performance of our method. The cross-validation accuracy and accuracy is 90.6% and 95%, respectively. Furthermore, the low computational burden of this method makes it a promising candidate for real-time BCI systems.

  13. Single-trial effective brain connectivity patterns enhance discriminability of mental imagery tasks

    Science.gov (United States)

    Rathee, Dheeraj; Cecotti, Hubert; Prasad, Girijesh

    2017-10-01

    Objective. The majority of the current approaches of connectivity based brain-computer interface (BCI) systems focus on distinguishing between different motor imagery (MI) tasks. Brain regions associated with MI are anatomically close to each other, hence these BCI systems suffer from low performances. Our objective is to introduce single-trial connectivity feature based BCI system for cognition imagery (CI) based tasks wherein the associated brain regions are located relatively far away as compared to those for MI. Approach. We implemented time-domain partial Granger causality (PGC) for the estimation of the connectivity features in a BCI setting. The proposed hypothesis has been verified with two publically available datasets involving MI and CI tasks. Main results. The results support the conclusion that connectivity based features can provide a better performance than a classical signal processing framework based on bandpass features coupled with spatial filtering for CI tasks, including word generation, subtraction, and spatial navigation. These results show for the first time that connectivity features can provide a reliable performance for imagery-based BCI system. Significance. We show that single-trial connectivity features for mixed imagery tasks (i.e. combination of CI and MI) can outperform the features obtained by current state-of-the-art method and hence can be successfully applied for BCI applications.

  14. The Importance of Visual Feedback Design in BCIs; from Embodiment to Motor Imagery Learning.

    Science.gov (United States)

    Alimardani, Maryam; Nishio, Shuichi; Ishiguro, Hiroshi

    2016-01-01

    Brain computer interfaces (BCIs) have been developed and implemented in many areas as a new communication channel between the human brain and external devices. Despite their rapid growth and broad popularity, the inaccurate performance and cost of user-training are yet the main issues that prevent their application out of the research and clinical environment. We previously introduced a BCI system for the control of a very humanlike android that could raise a sense of embodiment and agency in the operators only by imagining a movement (motor imagery) and watching the robot perform it. Also using the same setup, we further discovered that the positive bias of subjects' performance both increased their sensation of embodiment and improved their motor imagery skills in a short period. In this work, we studied the shared mechanism between the experience of embodiment and motor imagery. We compared the trend of motor imagery learning when two groups of subjects BCI-operated different looking robots, a very humanlike android's hands and a pair of metallic gripper. Although our experiments did not show a significant change of learning between the two groups immediately during one session, the android group revealed better motor imagery skills in the follow up session when both groups repeated the task using the non-humanlike gripper. This result shows that motor imagery skills learnt during the BCI-operation of humanlike hands are more robust to time and visual feedback changes. We discuss the role of embodiment and mirror neuron system in such outcome and propose the application of androids for efficient BCI training.

  15. The Importance of Visual Feedback Design in BCIs; from Embodiment to Motor Imagery Learning.

    Directory of Open Access Journals (Sweden)

    Maryam Alimardani

    Full Text Available Brain computer interfaces (BCIs have been developed and implemented in many areas as a new communication channel between the human brain and external devices. Despite their rapid growth and broad popularity, the inaccurate performance and cost of user-training are yet the main issues that prevent their application out of the research and clinical environment. We previously introduced a BCI system for the control of a very humanlike android that could raise a sense of embodiment and agency in the operators only by imagining a movement (motor imagery and watching the robot perform it. Also using the same setup, we further discovered that the positive bias of subjects' performance both increased their sensation of embodiment and improved their motor imagery skills in a short period. In this work, we studied the shared mechanism between the experience of embodiment and motor imagery. We compared the trend of motor imagery learning when two groups of subjects BCI-operated different looking robots, a very humanlike android's hands and a pair of metallic gripper. Although our experiments did not show a significant change of learning between the two groups immediately during one session, the android group revealed better motor imagery skills in the follow up session when both groups repeated the task using the non-humanlike gripper. This result shows that motor imagery skills learnt during the BCI-operation of humanlike hands are more robust to time and visual feedback changes. We discuss the role of embodiment and mirror neuron system in such outcome and propose the application of androids for efficient BCI training.

  16. Programming an offline-analyzer of motor imagery signals via python language.

    Science.gov (United States)

    Alonso-Valerdi, Luz María; Sepulveda, Francisco

    2011-01-01

    Brain Computer Interface (BCI) systems control the user's environment via his/her brain signals. Brain signals related to motor imagery (MI) have become a widespread method employed by the BCI community. Despite the large number of references describing the MI signal treatment, there is not enough information related to the available programming languages that could be suitable to develop a specific-purpose MI-based BCI. The present paper describes the development of an offline-analysis system based on MI-EEG signals via open-source programming languages, and the assessment of the system using electrical activity recorded from three subjects. The analyzer recognized at least 63% of the MI signals corresponding to three classes. The results of the offline analysis showed a promising performance considering that the subjects have never undergone MI trainings.

  17. Large-Scale Assessment of a Fully Automatic Co-Adaptive Motor Imagery-Based Brain Computer Interface.

    Directory of Open Access Journals (Sweden)

    Laura Acqualagna

    Full Text Available In the last years Brain Computer Interface (BCI technology has benefited from the development of sophisticated machine leaning methods that let the user operate the BCI after a few trials of calibration. One remarkable example is the recent development of co-adaptive techniques that proved to extend the use of BCIs also to people not able to achieve successful control with the standard BCI procedure. Especially for BCIs based on the modulation of the Sensorimotor Rhythm (SMR these improvements are essential, since a not negligible percentage of users is unable to operate SMR-BCIs efficiently. In this study we evaluated for the first time a fully automatic co-adaptive BCI system on a large scale. A pool of 168 participants naive to BCIs operated the co-adaptive SMR-BCI in one single session. Different psychological interventions were performed prior the BCI session in order to investigate how motor coordination training and relaxation could influence BCI performance. A neurophysiological indicator based on the Power Spectral Density (PSD was extracted by the recording of few minutes of resting state brain activity and tested as predictor of BCI performances. Results show that high accuracies in operating the BCI could be reached by the majority of the participants before the end of the session. BCI performances could be significantly predicted by the neurophysiological indicator, consolidating the validity of the model previously developed. Anyway, we still found about 22% of users with performance significantly lower than the threshold of efficient BCI control at the end of the session. Being the inter-subject variability still the major problem of BCI technology, we pointed out crucial issues for those who did not achieve sufficient control. Finally, we propose valid developments to move a step forward to the applicability of the promising co-adaptive methods.

  18. Neuroanatomical correlates of brain-computer interface performance.

    Science.gov (United States)

    Kasahara, Kazumi; DaSalla, Charles Sayo; Honda, Manabu; Hanakawa, Takashi

    2015-04-15

    Brain-computer interfaces (BCIs) offer a potential means to replace or restore lost motor function. However, BCI performance varies considerably between users, the reasons for which are poorly understood. Here we investigated the relationship between sensorimotor rhythm (SMR)-based BCI performance and brain structure. Participants were instructed to control a computer cursor using right- and left-hand motor imagery, which primarily modulated their left- and right-hemispheric SMR powers, respectively. Although most participants were able to control the BCI with success rates significantly above chance level even at the first encounter, they also showed substantial inter-individual variability in BCI success rate. Participants also underwent T1-weighted three-dimensional structural magnetic resonance imaging (MRI). The MRI data were subjected to voxel-based morphometry using BCI success rate as an independent variable. We found that BCI performance correlated with gray matter volume of the supplementary motor area, supplementary somatosensory area, and dorsal premotor cortex. We suggest that SMR-based BCI performance is associated with development of non-primary somatosensory and motor areas. Advancing our understanding of BCI performance in relation to its neuroanatomical correlates may lead to better customization of BCIs based on individual brain structure. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. Evolution of brain-computer interfaces: going beyond classic motor physiology

    Science.gov (United States)

    Leuthardt, Eric C.; Schalk, Gerwin; Roland, Jarod; Rouse, Adam; Moran, Daniel W.

    2010-01-01

    The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future. PMID:19569892

  20. A brain-computer interface controlled mail client.

    Science.gov (United States)

    Yu, Tianyou; Li, Yuanqing; Long, Jinyi; Wang, Cong

    2013-01-01

    In this paper, we propose a brain-computer interface (BCI) based mail client. This system is controlled by hybrid features extracted from scalp-recorded electroencephalographic (EEG). We emulate the computer mouse by the motor imagery-based mu rhythm and the P300 potential. Furthermore, an adaptive P300 speller is included to provide text input function. With this BCI mail client, users can receive, read, write mails, as well as attach files in mail writing. The system has been tested on 3 subjects. Experimental results show that mail communication with this system is feasible.

  1. Filter Bank Regularized Common Spatial Pattern Ensemble for Small Sample Motor Imagery Classification.

    Science.gov (United States)

    Park, Sang-Hoon; Lee, David; Lee, Sang-Goog

    2018-02-01

    For the last few years, many feature extraction methods have been proposed based on biological signals. Among these, the brain signals have the advantage that they can be obtained, even by people with peripheral nervous system damage. Motor imagery electroencephalograms (EEG) are inexpensive to measure, offer a high temporal resolution, and are intuitive. Therefore, these have received a significant amount of attention in various fields, including signal processing, cognitive science, and medicine. The common spatial pattern (CSP) algorithm is a useful method for feature extraction from motor imagery EEG. However, performance degradation occurs in a small-sample setting (SSS), because the CSP depends on sample-based covariance. Since the active frequency range is different for each subject, it is also inconvenient to set the frequency range to be different every time. In this paper, we propose the feature extraction method based on a filter bank to solve these problems. The proposed method consists of five steps. First, motor imagery EEG is divided by a using filter bank. Second, the regularized CSP (R-CSP) is applied to the divided EEG. Third, we select the features according to mutual information based on the individual feature algorithm. Fourth, parameter sets are selected for the ensemble. Finally, we classify using ensemble based on features. The brain-computer interface competition III data set IVa is used to evaluate the performance of the proposed method. The proposed method improves the mean classification accuracy by 12.34%, 11.57%, 9%, 4.95%, and 4.47% compared with CSP, SR-CSP, R-CSP, filter bank CSP (FBCSP), and SR-FBCSP. Compared with the filter bank R-CSP ( , ), which is a parameter selection version of the proposed method, the classification accuracy is improved by 3.49%. In particular, the proposed method shows a large improvement in performance in the SSS.

  2. Sensory threshold neuromuscular electrical stimulation fosters motor imagery performance.

    Science.gov (United States)

    Corbet, Tiffany; Iturrate, Iñaki; Pereira, Michael; Perdikis, Serafeim; Millán, José Del R

    2018-04-21

    Motor imagery (MI) has been largely studied as a way to enhance motor learning and to restore motor functions. Although it is agreed that users should emphasize kinesthetic imagery during MI, recordings of MI brain patterns are not sufficiently reliable for many subjects. It has been suggested that the usage of somatosensory feedback would be more suitable than standardly used visual feedback to enhance MI brain patterns. However, somatosensory feed-back should not interfere with the recorded MI brain pattern. In this study we propose a novel feedback modality to guide subjects during MI based on sensory threshold neuromuscular electrical stimulation (St-NMES). St-NMES depolarizes sensory and motor axons without eliciting any muscular contraction. We hypothesize that St-NMES does not induce detectable ERD brain patterns and fosters MI performance. Twelve novice subjects were included in a cross-over design study. We recorded their EEG, comparing St-NMES with visual feed-back during MI or resting tasks. We found that St-NMES not only induced significantly larger desynchronization over sensorimotor areas (p<0.05) but also significantly enhanced MI brain connectivity patterns. Moreover, classification accuracy and stability were significantly higher with St-NMES. Importantly, St-NMES alone did not induce detectable artifacts, but rather the changes in the detected patterns were due to an increased MI performance. Our findings indicate that St-NMES is a promising feedback in order to foster MI performance and cold be used for BMI online applications. Copyright © 2018. Published by Elsevier Inc.

  3. Enhancing voluntary imitation through attention and motor imagery.

    Science.gov (United States)

    Bek, Judith; Poliakoff, Ellen; Marshall, Hannah; Trueman, Sophie; Gowen, Emma

    2016-07-01

    Action observation activates brain areas involved in performing the same action and has been shown to increase motor learning, with potential implications for neurorehabilitation. Recent work indicates that the effects of action observation on movement can be increased by motor imagery or by directing attention to observed actions. In voluntary imitation, activation of the motor system during action observation is already increased. We therefore explored whether imitation could be further enhanced by imagery or attention. Healthy participants observed and then immediately imitated videos of human hand movement sequences, while movement kinematics were recorded. Two blocks of trials were completed, and after the first block participants were instructed to imagine performing the observed movement (Imagery group, N = 18) or attend closely to the characteristics of the movement (Attention group, N = 15), or received no further instructions (Control group, N = 17). Kinematics of the imitated movements were modulated by instructions, with both Imagery and Attention groups being closer in duration, peak velocity and amplitude to the observed model compared with controls. These findings show that both attention and motor imagery can increase the accuracy of imitation and have implications for motor learning and rehabilitation. Future work is required to understand the mechanisms by which these two strategies influence imitation accuracy.

  4. Robot-Aided Upper-Limb Rehabilitation Based on Motor Imagery EEG

    Directory of Open Access Journals (Sweden)

    Baoguo Xu

    2011-09-01

    Full Text Available Stroke is a leading cause of disability worldwide. In this paper, a novel robot-assisted rehabilitation system based on motor imagery electroencephalography (EEG is developed for regular training of neurological rehabilitation for upper limb stroke patients. Firstly, three-dimensional animation was used to guide the patient image the upper limb movement and EEG signals were acquired by EEG amplifier. Secondly, eigenvectors were extracted by harmonic wavelet transform (HWT and linear discriminant analysis (LDA classifier was utilized to classify the pattern of the left and right upper limb motor imagery EEG signals. Finally, PC triggered the upper limb rehabilitation robot to perform motor therapy and gave the virtual feedback. Using this robot-assisted upper limb rehabilitation system, the patient's EEG of upper limb movement imagination is translated to control rehabilitation robot directly. Consequently, the proposed rehabilitation system can fully explore the patient's motivation and attention and directly facilitate upper limb post-stroke rehabilitation therapy. Experimental results on unimpaired participants were presented to demonstrate the feasibility of the rehabilitation system. Combining robot-assisted training with motor imagery-based BCI will make future rehabilitation therapy more effective. Clinical testing is still required for further proving this assumption.

  5. Development of a Novel Motor Imagery Control Technique and Application in a Gaming Environment

    OpenAIRE

    Li, Ting; Zhang, Jinhua; Xue, Tao; Wang, Baozeng

    2017-01-01

    We present a methodology for a hybrid brain-computer interface (BCI) system, with the recognition of motor imagery (MI) based on EEG and blink EOG signals. We tested the BCI system in a 3D Tetris and an analogous 2D game playing environment. To enhance player’s BCI control ability, the study focused on feature extraction from EEG and control strategy supporting Game-BCI system operation. We compared the numerical differences between spatial features extracted with common spatial pattern (CSP)...

  6. Individually adapted imagery improves brain-computer interface performance in end-users with disability.

    Science.gov (United States)

    Scherer, Reinhold; Faller, Josef; Friedrich, Elisabeth V C; Opisso, Eloy; Costa, Ursula; Kübler, Andrea; Müller-Putz, Gernot R

    2015-01-01

    Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e.g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology

  7. Individually adapted imagery improves brain-computer interface performance in end-users with disability.

    Directory of Open Access Journals (Sweden)

    Reinhold Scherer

    Full Text Available Brain-computer interfaces (BCIs translate oscillatory electroencephalogram (EEG patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS tissue damage such as persons with stroke or spinal cord injury (SCI. Motor imagery (MI, that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand, is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association and "dynamic imagery" (e.g. hand and feet MI tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day? and between-day (How well does a model trained on day one perform on unseen data of day two? analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI

  8. A Transform-Based Feature Extraction Approach for Motor Imagery Tasks Classification

    Science.gov (United States)

    Khorshidtalab, Aida; Mesbah, Mostefa; Salami, Momoh J. E.

    2015-01-01

    In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$T^{2}$ \\end{document} statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%. PMID:27170898

  9. Motor experience with a sport-specific implement affects motor imagery

    Science.gov (United States)

    Zhu, Hua; Shen, Cheng; Zhang, Jian

    2018-01-01

    The present study tested whether sport-specific implements facilitate motor imagery, whereas nonspecific implements disrupt motor imagery. We asked a group of basketball players (experts) and a group of healthy controls (novices) to physically perform (motor execution) and mentally simulate (motor imagery) basketball throws. Subjects produced motor imagery when they were holding a basketball, a volleyball, or nothing. Motor imagery performance was measured by temporal congruence, which is the correspondence between imagery and execution times estimated as (imagery time minus execution time) divided by (imagery time plus execution time), as well as the vividness of motor imagery. Results showed that experts produced greater temporal congruence and vividness of kinesthetic imagery while holding a basketball compared to when they were holding nothing, suggesting a facilitation effect from sport-specific implements. In contrast, experts produced lower temporal congruence and vividness of kinesthetic imagery while holding a volleyball compared to when they were holding nothing, suggesting the interference effect of nonspecific implements. Furthermore, we found a negative correlation between temporal congruence and the vividness of kinesthetic imagery in experts while holding a basketball. On the contrary, the implement manipulation did not modulate the temporal congruence of novices. Our findings suggest that motor representation in experts is built on motor experience associated with specific-implement use and thus was subjected to modulation of the implement held. We conclude that sport-specific implements facilitate motor imagery, whereas nonspecific implements could disrupt motor representation in experts. PMID:29719738

  10. Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury

    Directory of Open Access Journals (Sweden)

    Alkinoos Athanasiou

    2018-01-01

    Full Text Available Reciprocal communication of the central and peripheral nervous systems is compromised during spinal cord injury due to neurotrauma of ascending and descending pathways. Changes in brain organization after spinal cord injury have been associated with differences in prognosis. Changes in functional connectivity may also serve as injury biomarkers. Most studies on functional connectivity have focused on chronic complete injury or resting-state condition. In our study, ten right-handed patients with incomplete spinal cord injury and ten age- and gender-matched healthy controls performed multiple visual motor imagery tasks of upper extremities and walking under high-resolution electroencephalography recording. Directed transfer function was used to study connectivity at the cortical source space between sensorimotor nodes. Chronic disruption of reciprocal communication in incomplete injury could result in permanent significant decrease of connectivity in a subset of the sensorimotor network, regardless of positive or negative neurological outcome. Cingulate motor areas consistently contributed the larger outflow (right and received the higher inflow (left among all nodes, across all motor imagery categories, in both groups. Injured subjects had higher outflow from left cingulate than healthy subjects and higher inflow in right cingulate than healthy subjects. Alpha networks were less dense, showing less integration and more segregation than beta networks. Spinal cord injury patients showed signs of increased local processing as adaptive mechanism. This trial is registered with NCT02443558.

  11. Brain networks underlying mental imagery of auditory and visual information.

    Science.gov (United States)

    Zvyagintsev, Mikhail; Clemens, Benjamin; Chechko, Natalya; Mathiak, Krystyna A; Sack, Alexander T; Mathiak, Klaus

    2013-05-01

    Mental imagery is a complex cognitive process that resembles the experience of perceiving an object when this object is not physically present to the senses. It has been shown that, depending on the sensory nature of the object, mental imagery also involves correspondent sensory neural mechanisms. However, it remains unclear which areas of the brain subserve supramodal imagery processes that are independent of the object modality, and which brain areas are involved in modality-specific imagery processes. Here, we conducted a functional magnetic resonance imaging study to reveal supramodal and modality-specific networks of mental imagery for auditory and visual information. A common supramodal brain network independent of imagery modality, two separate modality-specific networks for imagery of auditory and visual information, and a common deactivation network were identified. The supramodal network included brain areas related to attention, memory retrieval, motor preparation and semantic processing, as well as areas considered to be part of the default-mode network and multisensory integration areas. The modality-specific networks comprised brain areas involved in processing of respective modality-specific sensory information. Interestingly, we found that imagery of auditory information led to a relative deactivation within the modality-specific areas for visual imagery, and vice versa. In addition, mental imagery of both auditory and visual information widely suppressed the activity of primary sensory and motor areas, for example deactivation network. These findings have important implications for understanding the mechanisms that are involved in generation of mental imagery. © 2013 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  12. Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering.

    Science.gov (United States)

    Suraj; Tiwari, Purnendu; Ghosh, Subhojit; Sinha, Rakesh Kumar

    2015-01-01

    Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.

  13. Rapid prototyping of an EEG-based brain-computer interface (BCI).

    Science.gov (United States)

    Guger, C; Schlögl, A; Neuper, C; Walterspacher, D; Strein, T; Pfurtscheller, G

    2001-03-01

    The electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (e.g., late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is known as an EEG-based brain-computer interface (BCI). This paper describes a new type of BCI system that uses rapid prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing. Rapid prototyping is possible by using Matlab, Simulink, and the Real-Time Workshop. It is shown how to automate real-time experiments and perform the interplay between on-line experiments and offline analysis. The system is able to process multiple EEG channels on-line and operates under Windows 95 in real-time on a standard PC without an additional digital signal processor (DSP) board. The BCI can be controlled over the Internet, LAN or modem. This BCI was tested on 3 subjects whose task it was to imagine either left or right hand movement. A classification accuracy between 70% and 95% could be achieved with two EEG channels after some sessions with feedback using an adaptive autoregressive (AAR) model and linear discriminant analysis (LDA).

  14. Performance of Brain-computer Interfacing based on tactile selective sensation and motor imagery

    DEFF Research Database (Denmark)

    Yao, Lin; Sheng, Xinjun; Mrachacz-Kersting, Natalie

    2018-01-01

    We proposed a multi-class tactile brain-computer interface that utilizes stimulus-induced oscillatory dynamics. It was hypothesized that somatosensory attention can modulate tactile induced oscillation changes, which can decode different sensation attention tasks. Subjects performed four tactile...

  15. Bilinear Regularized Locality Preserving Learning on Riemannian Graph for Motor Imagery BCI.

    Science.gov (United States)

    Xie, Xiaofeng; Yu, Zhu Liang; Gu, Zhenghui; Zhang, Jun; Cen, Ling; Li, Yuanqing

    2018-03-01

    In off-line training of motor imagery-based brain-computer interfaces (BCIs), to enhance the generalization performance of the learned classifier, the local information contained in test data could be used to improve the performance of motor imagery as well. Further considering that the covariance matrices of electroencephalogram (EEG) signal lie on Riemannian manifold, in this paper, we construct a Riemannian graph to incorporate the information of training and test data into processing. The adjacency and weight in Riemannian graph are determined by the geodesic distance of Riemannian manifold. Then, a new graph embedding algorithm, called bilinear regularized locality preserving (BRLP), is derived upon the Riemannian graph for addressing the problems of high dimensionality frequently arising in BCIs. With a proposed regularization term encoding prior information of EEG channels, the BRLP could obtain more robust performance. Finally, an efficient classification algorithm based on extreme learning machine is proposed to perform on the tangent space of learned embedding. Experimental evaluations on the BCI competition and in-house data sets reveal that the proposed algorithms could obtain significantly higher performance than many competition algorithms after using same filter process.

  16. Quadcopter control in three-dimensional space using a noninvasive motor imagery based brain-computer interface

    Science.gov (United States)

    LaFleur, Karl; Cassady, Kaitlin; Doud, Alexander; Shades, Kaleb; Rogin, Eitan; He, Bin

    2013-01-01

    Objective At the balanced intersection of human and machine adaptation is found the optimally functioning brain-computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional physical space using noninvasive scalp EEG in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that operation of a real world device has on subjects’ control with comparison to a two-dimensional virtual cursor task. Approach Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a three-dimensional physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m/s. Significance Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain-computer interfaces are systems that aim to restore or enhance a user’s ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in the three-dimensional physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG based BCI systems to accomplish complex control in three-dimensional physical space. The present study may serve as a framework for the investigation of multidimensional non-invasive brain-computer interface control in a physical environment using telepresence robotics. PMID:23735712

  17. Investigating the effects of a sensorimotor rhythm-based BCI training on the cortical activity elicited by mental imagery

    Science.gov (United States)

    Toppi, J.; Risetti, M.; Quitadamo, L. R.; Petti, M.; Bianchi, L.; Salinari, S.; Babiloni, F.; Cincotti, F.; Mattia, D.; Astolfi, L.

    2014-06-01

    Objective. It is well known that to acquire sensorimotor (SMR)-based brain-computer interface (BCI) control requires a training period before users can achieve their best possible performances. Nevertheless, the effect of this training procedure on the cortical activity related to the mental imagery ability still requires investigation to be fully elucidated. The aim of this study was to gain insights into the effects of SMR-based BCI training on the cortical spectral activity associated with the performance of different mental imagery tasks. Approach. Linear cortical estimation and statistical brain mapping techniques were applied on high-density EEG data acquired from 18 healthy participants performing three different mental imagery tasks. Subjects were divided in two groups, one of BCI trained subjects, according to their previous exposure (at least six months before this study) to motor imagery-based BCI training, and one of subjects who were naive to any BCI paradigms. Main results. Cortical activation maps obtained for trained and naive subjects indicated different spectral and spatial activity patterns in response to the mental imagery tasks. Long-term effects of the previous SMR-based BCI training were observed on the motor cortical spectral activity specific to the BCI trained motor imagery task (simple hand movements) and partially generalized to more complex motor imagery task (playing tennis). Differently, mental imagery with spatial attention and memory content could elicit recognizable cortical spectral activity even in subjects completely naive to (BCI) training. Significance. The present findings contribute to our understanding of BCI technology usage and might be of relevance in those clinical conditions when training to master a BCI application is challenging or even not possible.

  18. Comparison of four classification methods for brain-computer interface

    Czech Academy of Sciences Publication Activity Database

    Frolov, A.; Húsek, Dušan; Bobrov, P.

    2011-01-01

    Roč. 21, č. 2 (2011), s. 101-115 ISSN 1210-0552 R&D Projects: GA MŠk(CZ) 1M0567; GA ČR GA201/05/0079; GA ČR GAP202/10/0262 Institutional research plan: CEZ:AV0Z10300504 Keywords : brain computer interface * motor imagery * visual imagery * EEG pattern classification * Bayesian classification * Common Spatial Patterns * Common Tensor Discriminant Analysis Subject RIV: IN - Informatics, Computer Science Impact factor: 0.646, year: 2011

  19. Task-dependent activation of distinct fast and slow(er) motor pathways during motor imagery.

    Science.gov (United States)

    Keller, Martin; Taube, Wolfgang; Lauber, Benedikt

    2018-02-22

    Motor imagery and actual movements share overlapping activation of brain areas but little is known about task-specific activation of distinct motor pathways during mental simulation of movements. For real contractions, it was demonstrated that the slow(er) motor pathways are activated differently in ballistic compared to tonic contractions but it is unknown if this also holds true for imagined contractions. The aim of the present study was to assess the activity of fast and slow(er) motor pathways during mentally simulated movements of ballistic and tonic contractions. H-reflexes were conditioned with transcranial magnetic stimulation at different interstimulus intervals to assess the excitability of fast and slow(er) motor pathways during a) the execution of tonic and ballistic contractions, b) motor imagery of these contraction types, and c) at rest. In contrast to the fast motor pathways, the slow(er) pathways displayed a task-specific activation: for imagined ballistic as well as real ballistic contractions, the activation was reduced compared to rest whereas enhanced activation was found for imagined tonic and real tonic contractions. This study provides evidence that the excitability of fast and slow(er) motor pathways during motor imagery resembles the activation pattern observed during real contractions. The findings indicate that motor imagery results in task- and pathway-specific subliminal activation of distinct subsets of neurons in the primary motor cortex. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

  20. EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine

    Science.gov (United States)

    Gao, Lin; Cheng, Wei; Zhang, Jinhua; Wang, Jue

    2016-08-01

    Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of motion intention from a common resting state. In this paper, we sought a novel approach for multi-class classification in BCI applications. We collected electroencephalographic (EEG) signals registered by electrodes placed over the scalp during left hand motor imagery, right hand motor imagery, and resting state for ten healthy human subjects. We proposed using the Kolmogorov complexity (Kc) for feature extraction and a multi-class Adaboost classifier with extreme learning machine as base classifier for classification, in order to classify the three-class EEG samples. An average classification accuracy of 79.5% was obtained for ten subjects, which greatly outperformed commonly used approaches. Thus, it is concluded that the proposed method could improve the performance for classification of motor imagery tasks for multi-class samples. It could be applied in further studies to generate the control commands to initiate the movement of a robotic exoskeleton or orthosis, which finally facilitates the rehabilitation of disabled people.

  1. Neural correlates of learning in an electrocorticographic motor-imagery brain-computer interface

    Science.gov (United States)

    Blakely, Tim M.; Miller, Kai J.; Rao, Rajesh P. N.; Ojemann, Jeffrey G.

    2014-01-01

    Human subjects can learn to control a one-dimensional electrocorticographic (ECoG) brain-computer interface (BCI) using modulation of primary motor (M1) high-gamma activity (signal power in the 75–200 Hz range). However, the stability and dynamics of the signals over the course of new BCI skill acquisition have not been investigated. In this study, we report 3 characteristic periods in evolution of the high-gamma control signal during BCI training: initial, low task accuracy with corresponding low power modulation in the gamma spectrum, followed by a second period of improved task accuracy with increasing average power separation between activity and rest, and a final period of high task accuracy with stable (or decreasing) power separation and decreasing trial-to-trial variance. These findings may have implications in the design and implementation of BCI control algorithms. PMID:25599079

  2. Effect of biased feedback on motor imagery learning in BCI-teleoperation system

    Directory of Open Access Journals (Sweden)

    Maryam eAlimardani

    2014-04-01

    Full Text Available Feedback design is an important issue in motor imagery BCI systems. Regardless, to date it has not been reported how feedback presentation can optimize co-adaptation between a human brain and such systems. This paper assesses the effect of realistic visual feedback on users’ BC performance and motor imagery skills. We previously developed a tele-operation system for a pair of humanlike robotic hands and showed that BCI control of such hands along with first-person perspective visual feedback of movements can arouse a sense of embodiment in the operators. In the first stage of this study, we found that the intensity of this ownership illusion was associated with feedback presentation and subjects’ performance during BCI motion control. In the second stage, we probed the effect of positive and negative feedback bias on subjects’ BCI performance and motor imagery skills. Although the subject specific classifier, which was set up at the beginning of experiment, detected no significant change in the subjects’ online performance, evaluation of brain activity patterns revealed that subjects’ self-regulation of motor imagery features improved due to a positive bias of feedback and a possible occurrence of ownership illusion. Our findings suggest that in general training protocols for BCIs, manipulation of feedback can play an important role in the optimization of subjects’ motor imagery skills.

  3. Brain-computer interface training combined with transcranial direct current stimulation in patients with chronic severe hemiparesis: Proof of concept study.

    Science.gov (United States)

    Kasashima-Shindo, Yuko; Fujiwara, Toshiyuki; Ushiba, Junichi; Matsushika, Yayoi; Kamatani, Daiki; Oto, Misa; Ono, Takashi; Nishimoto, Atsuko; Shindo, Keiichiro; Kawakami, Michiyuki; Tsuji, Tetsuya; Liu, Meigen

    2015-04-01

    Brain-computer interface technology has been applied to stroke patients to improve their motor function. Event-related desynchronization during motor imagery, which is used as a brain-computer interface trigger, is sometimes difficult to detect in stroke patients. Anodal transcranial direct current stimulation (tDCS) is known to increase event-related desynchronization. This study investigated the adjunctive effect of anodal tDCS for brain-computer interface training in patients with severe hemiparesis. Eighteen patients with chronic stroke. A non-randomized controlled study. Subjects were divided between a brain-computer interface group and a tDCS- brain-computer interface group and participated in a 10-day brain-computer interface training. Event-related desynchronization was detected in the affected hemisphere during motor imagery of the affected fingers. The tDCS-brain-computer interface group received anodal tDCS before brain-computer interface training. Event-related desynchronization was evaluated before and after the intervention. The Fugl-Meyer Assessment upper extremity motor score (FM-U) was assessed before, immediately after, and 3 months after, the intervention. Event-related desynchronization was significantly increased in the tDCS- brain-computer interface group. The FM-U was significantly increased in both groups. The FM-U improvement was maintained at 3 months in the tDCS-brain-computer interface group. Anodal tDCS can be a conditioning tool for brain-computer interface training in patients with severe hemiparetic stroke.

  4. [Brain-Computer Interface: the First Clinical Experience in Russia].

    Science.gov (United States)

    Mokienko, O A; Lyukmanov, R Kh; Chernikova, L A; Suponeva, N A; Piradov, M A; Frolov, A A

    2016-01-01

    Motor imagery is suggested to stimulate the same plastic mechanisms in the brain as a real movement. The brain-computer interface (BCI) controls motor imagery by converting EEG during this process into the commands for an external device. This article presents the results of two-stage study of the clinical use of non-invasive BCI in the rehabilitation of patients with severe hemiparesis caused by focal brain damage. It was found that the ability to control BCI did not depend on the duration of a disease, brain lesion localization and the degree of neurological deficit. The first step of the study involved 36 patients; it showed that the efficacy of rehabilitation was higher in the group with the use of BCI (the score on the Action Research Arm Test (ARAT) improved from 1 [0; 2] to 5 [0; 16] points, p = 0.012; no significant improvement was observed in control group). The second step of the study involved 19 patients; the complex BCI-exoskeleton (i.e. with the kinesthetic feedback) was used for motor imagery trainings. The improvement of the motor function of hands was proved by ARAT (the score improved from 2 [0; 37] to 4 [1; 45:5] points, p = 0.005) and Fugl-Meyer scale (from 72 [63; 110 ] to 79 [68; 115] points, p = 0.005).

  5. Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms.

    Science.gov (United States)

    Liu, Rensong; Zhang, Zhiwen; Duan, Feng; Zhou, Xin; Meng, Zixuan

    2017-01-01

    Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K -nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance.

  6. Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms

    Science.gov (United States)

    Zhang, Zhiwen; Duan, Feng; Zhou, Xin; Meng, Zixuan

    2017-01-01

    Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K-nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance. PMID:28874909

  7. Brain activation profiles during kinesthetic and visual imagery: An fMRI study.

    Science.gov (United States)

    Kilintari, Marina; Narayana, Shalini; Babajani-Feremi, Abbas; Rezaie, Roozbeh; Papanicolaou, Andrew C

    2016-09-01

    The aim of this study was to identify brain regions involved in motor imagery and differentiate two alternative strategies in its implementation: imagining a motor act using kinesthetic or visual imagery. Fourteen adults were precisely instructed and trained on how to imagine themselves or others perform a movement sequence, with the aim of promoting kinesthetic and visual imagery, respectively, in the context of an fMRI experiment using block design. We found that neither modality of motor imagery elicits activation of the primary motor cortex and that each of the two modalities involves activation of the premotor area which is also activated during action execution and action observation conditions, as well as of the supplementary motor area. Interestingly, the visual and the posterior cingulate cortices show reduced BOLD signal during both imagery conditions. Our results indicate that the networks of regions activated in kinesthetic and visual imagery of motor sequences show a substantial, while not complete overlap, and that the two forms of motor imagery lead to a differential suppression of visual areas. Copyright © 2016 Elsevier B.V. All rights reserved.

  8. Toward brain-actuated car applications: Self-paced control with a motor imagery-based brain-computer interface.

    Science.gov (United States)

    Yu, Yang; Zhou, Zongtan; Yin, Erwei; Jiang, Jun; Tang, Jingsheng; Liu, Yadong; Hu, Dewen

    2016-10-01

    This study presented a paradigm for controlling a car using an asynchronous electroencephalogram (EEG)-based brain-computer interface (BCI) and presented the experimental results of a simulation performed in an experimental environment outside the laboratory. This paradigm uses two distinct MI tasks, imaginary left- and right-hand movements, to generate a multi-task car control strategy consisting of starting the engine, moving forward, turning left, turning right, moving backward, and stopping the engine. Five healthy subjects participated in the online car control experiment, and all successfully controlled the car by following a previously outlined route. Subject S1 exhibited the most satisfactory BCI-based performance, which was comparable to the manual control-based performance. We hypothesize that the proposed self-paced car control paradigm based on EEG signals could potentially be used in car control applications, and we provide a complementary or alternative way for individuals with locked-in disorders to achieve more mobility in the future, as well as providing a supplementary car-driving strategy to assist healthy people in driving a car. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Expanding the (kaleido)scope: exploring current literature trends for translating electroencephalography (EEG) based brain-computer interfaces for motor rehabilitation in children

    Science.gov (United States)

    Kinney-Lang, E.; Auyeung, B.; Escudero, J.

    2016-12-01

    Rehabilitation applications using brain-computer interfaces (BCI) have recently shown encouraging results for motor recovery. Effective BCI neurorehabilitation has been shown to exploit neuroplastic properties of the brain through mental imagery tasks. However, these applications and results are currently restricted to adults. A systematic search reveals there is essentially no literature describing motor rehabilitative BCI applications that use electroencephalograms (EEG) in children, despite advances in such applications with adults. Further inspection highlights limited literature pursuing research in the field, especially outside of neurofeedback paradigms. Then the question naturally arises, do current literature trends indicate that EEG based BCI motor rehabilitation applications could be translated to children? To provide further evidence beyond the available literature for this particular topic, we present an exploratory survey examining some of the indirect literature related to motor rehabilitation BCI in children. Our goal is to establish if evidence in the related literature supports research on this topic and if the related studies can help explain the dearth of current research in this area. The investigation found positive literature trends in the indirect studies which support translating these BCI applications to children and provide insight into potential pitfalls perhaps responsible for the limited literature. Careful consideration of these pitfalls in conjunction with support from the literature emphasize that fully realized motor rehabilitation BCI applications for children are feasible and would be beneficial. • BCI intervention has improved motor recovery in adult patients and offer supplementary rehabilitation options to patients. • A systematic literature search revealed that essentially no research has been conducted bringing motor rehabilitation BCI applications to children, despite advances in BCI. • Indirect studies discovered

  10. Expanding the (kaleido)scope: exploring current literature trends for translating electroencephalography (EEG) based brain-computer interfaces for motor rehabilitation in children.

    Science.gov (United States)

    Kinney-Lang, E; Auyeung, B; Escudero, J

    2016-12-01

    Rehabilitation applications using brain-computer interfaces (BCI) have recently shown encouraging results for motor recovery. Effective BCI neurorehabilitation has been shown to exploit neuroplastic properties of the brain through mental imagery tasks. However, these applications and results are currently restricted to adults. A systematic search reveals there is essentially no literature describing motor rehabilitative BCI applications that use electroencephalograms (EEG) in children, despite advances in such applications with adults. Further inspection highlights limited literature pursuing research in the field, especially outside of neurofeedback paradigms. Then the question naturally arises, do current literature trends indicate that EEG based BCI motor rehabilitation applications could be translated to children? To provide further evidence beyond the available literature for this particular topic, we present an exploratory survey examining some of the indirect literature related to motor rehabilitation BCI in children. Our goal is to establish if evidence in the related literature supports research on this topic and if the related studies can help explain the dearth of current research in this area. The investigation found positive literature trends in the indirect studies which support translating these BCI applications to children and provide insight into potential pitfalls perhaps responsible for the limited literature. Careful consideration of these pitfalls in conjunction with support from the literature emphasize that fully realized motor rehabilitation BCI applications for children are feasible and would be beneficial. •  BCI intervention has improved motor recovery in adult patients and offer supplementary rehabilitation options to patients. •  A systematic literature search revealed that essentially no research has been conducted bringing motor rehabilitation BCI applications to children, despite advances in BCI. •  Indirect studies

  11. Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI

    Directory of Open Access Journals (Sweden)

    Dieter Devlaminck

    2011-01-01

    Full Text Available Motor-imagery-based brain-computer interfaces (BCIs commonly use the common spatial pattern filter (CSP as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low.

  12. 3D visualization of movements can amplify motor cortex activation during subsequent motor imagery

    Directory of Open Access Journals (Sweden)

    Teresa eSollfrank

    2015-08-01

    Full Text Available A repetitive movement practice by motor imagery (MI can influence motor cortical excitability in the electroencephalogram (EEG. The feedback and the feedback environment should be inherently motivating and relevant for the learner and should have an appeal of novelty, real-world relevance or aesthetic value (Ryan and Deci, 2000; Merrill, 2007. This study investigated if a realistic visualization in 3D of upper and lower limb movements can amplify motor related potentials during motor imagery. We hypothesized that a richer sensory visualization might be more effective during instrumental conditioning, resulting in a more pronounced event related desynchronisation (ERD of the upper alpha band (10-12 Hz over the sensorimotor cortices thereby potentially improving MI based BCI protocols for motor rehabilitation. The results show a strong increase of the characteristic patterns of ERD of the upper alpha band components for left and right limb motor imagery present over the sensorimotor areas in both visualization conditions. Overall, significant differences were observed as a function of visualization modality (2D vs. 3D. The largest upper alpha band power decrease was obtained during motor imagery after a 3-dimensional visualization. In total in 12 out of 20 tasks the end-user of the 3D visualization group showed an enhanced upper alpha ERD relative to 2D visualization modality group, with statistical significance in nine tasks.With a realistic visualization of the limb movements, we tried to increase motor cortex activation during MI. Realistic visual feedback, consistent with the participant’s motor imagery, might be helpful for accomplishing successful motor imagery and the use of such feedback may assist in making BCI a more natural interface for motor imagery based BCI rehabilitation.

  13. Laterality of brain activity during motor imagery is modulated by the provision of source level neurofeedback.

    Science.gov (United States)

    Boe, Shaun; Gionfriddo, Alicia; Kraeutner, Sarah; Tremblay, Antoine; Little, Graham; Bardouille, Timothy

    2014-11-01

    Motor imagery (MI) may be effective as an adjunct to physical practice for motor skill acquisition. For example, MI is emerging as an effective treatment in stroke neurorehabilitation. As in physical practice, the repetitive activation of neural pathways during MI can drive short- and long-term brain changes that underlie functional recovery. However, the lack of feedback about MI performance may be a factor limiting its effectiveness. The provision of feedback about MI-related brain activity may overcome this limitation by providing the opportunity for individuals to monitor their own performance of this endogenous process. We completed a controlled study to isolate neurofeedback as the factor driving changes in MI-related brain activity across repeated sessions. Eighteen healthy participants took part in 3 sessions comprised of both actual and imagined performance of a button press task. During MI, participants in the neurofeedback group received source level feedback based on activity from the left and right sensorimotor cortex obtained using magnetoencephalography. Participants in the control group received no neurofeedback. MI-related brain activity increased in the sensorimotor cortex contralateral to the imagined movement across sessions in the neurofeedback group, but not in controls. Task performance improved across sessions but did not differ between groups. Our results indicate that the provision of neurofeedback during MI allows healthy individuals to modulate regional brain activity. This finding has the potential to improve the effectiveness of MI as a tool in neurorehabilitation. Copyright © 2014 Elsevier Inc. All rights reserved.

  14. Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia.

    Science.gov (United States)

    Blokland, Yvonne; Spyrou, Loukianos; Thijssen, Dick; Eijsvogels, Thijs; Colier, Willy; Floor-Westerdijk, Marianne; Vlek, Rutger; Bruhn, Jorgen; Farquhar, Jason

    2014-03-01

    Combining electrophysiological and hemodynamic features is a novel approach for improving current performance of brain switches based on sensorimotor rhythms (SMR). This study was conducted with a dual purpose: to test the feasibility of using a combined electroencephalogram/functional near-infrared spectroscopy (EEG-fNIRS) SMR-based brain switch in patients with tetraplegia, and to examine the performance difference between motor imagery and motor attempt for this user group. A general improvement was found when using both EEG and fNIRS features for classification as compared to using the single-modality EEG classifier, with average classification rates of 79% for attempted movement and 70% for imagined movement. For the control group, rates of 87% and 79% were obtained, respectively, where the "attempted movement" condition was replaced with "actual movement." A combined EEG-fNIRS system might be especially beneficial for users who lack sufficient control of current EEG-based brain switches. The average classification performance in the patient group for attempted movement was significantly higher than for imagined movement using the EEG-only as well as the combined classifier, arguing for the case of a paradigm shift in current brain switch research.

  15. How Kinesthetic Motor Imagery works: a predictive-processing theory of visualization in sports and motor expertise.

    Science.gov (United States)

    Ridderinkhof, K Richard; Brass, Marcel

    2015-01-01

    Kinesthetic Motor Imagery (KMI) is an important technique to acquire and refine motor skills. KMI is widely used by professional athletes as an effective way to improve motor performance without overt motor output. Despite this obvious relevance, the functional mechanisms and neural circuits involved in KMI in sports are still poorly understood. In the present article, which aims at bridging the sport sciences and cognitive neurophysiology literatures, we give a brief overview of relevant research in the field of KMI. Furthermore, we develop a theoretical account that relates KMI to predictive motor control theories assuming that it is based on internal activation of anticipatory images of action effects. This mechanism allows improving motor performance solely based on internal emulation of action. In accordance with previous literature, we propose that this emulation mechanism is implemented in brain regions that partially overlap with brain areas involved in overt motor performance including the posterior parietal cortex, the cerebellum, the basal ganglia and the premotor cortex. Finally, we outline one way to test the heuristic value of our theoretical framework for KMI; we suggest that experience with motor performance improves the ability to correctly infer the goals of others, in particular in penalty blocking in soccer. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Robot-Aided Upper-Limb Rehabilitation Based on Motor Imagery EEG

    Directory of Open Access Journals (Sweden)

    Baoguo Xu

    2011-09-01

    Full Text Available Stroke is a leading cause of disability worldwide. In this paper, a novel robot‐assisted rehabilitation system based on motor imagery electroencephalography (EEG is developed for regular training of neurological rehabilitation for upper limb stroke patients. Firstly, three‐dimensional animation was used to guide the patient image the upper limb movement and EEG signals were acquired by EEG amplifier. Secondly, eigenvectors were extracted by harmonic wavelet transform (HWT and linear discriminant analysis (LDA classifier was utilized to classify the pattern of the left and right upper limb motor imagery EEG signals. Finally, PC triggered the upper limb rehabilitation robot to perform motor therapy and gave the virtual feedback. Using this robot‐assisted upper limb rehabilitation system, the patientʹs EEG of upper limb movement imagination is translated to control rehabilitation robot directly. Consequently, the proposed rehabilitation system can fully explore the patientʹs motivation and attention and directly facilitate upper limb post‐stroke rehabilitation therapy. Experimental results on unimpaired participants were presented to demonstrate the feasibility of the rehabilitation system. Combining robot‐assisted training with motor imagery‐ based BCI will make future rehabilitation therapy more effective. Clinical testing is still required for further proving this assumption.

  17. A standardized motor imagery introduction program (MIIP) for neuro-rehabilitation: development and evaluation

    OpenAIRE

    Wondrusch, C.; Schuster-Amft, C.

    2013-01-01

    Background: For patients with central nervous system (CNS) lesions and sensorimotor impairments a solid motor imagery (MI) introduction is crucial to understand and use MI to improve motor performance. The study's aim was to develop and evaluate a standardized MI group introduction program (MIIP) for patients after stroke, multiple sclerosis (MS), Parkinson's disease (PD), and traumatic brain injury (TBI). Methods: Phase 1: Based on literature a MIIP was developed comprising MI theory (def...

  18. A standardized motor imagery introduction program (MIIP) for neuro-rehabilitation: development and evaluation

    OpenAIRE

    Christine eWondrusch; Corina eSchuster; Corina eSchuster

    2013-01-01

    Background: For patients with central nervous system lesions and sensorimotor impairments a solid motor imagery (MI) introduction is crucial to understand and use MI to improve motor performance. The study’s aim was to develop and evaluate a standardized MI group introduction program (MIIP) for patients after stroke, multiple sclerosis, Parkinson’s disease, and traumatic brain injury. Methods:Phase 1: Based on literature a MIIP was developed comprising MI theory (definition, type, mode, persp...

  19. Context-aware adaptive spelling in motor imagery BCI

    Science.gov (United States)

    Perdikis, S.; Leeb, R.; Millán, J. d. R.

    2016-06-01

    Objective. This work presents a first motor imagery-based, adaptive brain-computer interface (BCI) speller, which is able to exploit application-derived context for improved, simultaneous classifier adaptation and spelling. Online spelling experiments with ten able-bodied users evaluate the ability of our scheme, first, to alleviate non-stationarity of brain signals for restoring the subject’s performances, second, to guide naive users into BCI control avoiding initial offline BCI calibration and, third, to outperform regular unsupervised adaptation. Approach. Our co-adaptive framework combines the BrainTree speller with smooth-batch linear discriminant analysis adaptation. The latter enjoys contextual assistance through BrainTree’s language model to improve online expectation-maximization maximum-likelihood estimation. Main results. Our results verify the possibility to restore single-sample classification and BCI command accuracy, as well as spelling speed for expert users. Most importantly, context-aware adaptation performs significantly better than its unsupervised equivalent and similar to the supervised one. Although no significant differences are found with respect to the state-of-the-art PMean approach, the proposed algorithm is shown to be advantageous for 30% of the users. Significance. We demonstrate the possibility to circumvent supervised BCI recalibration, saving time without compromising the adaptation quality. On the other hand, we show that this type of classifier adaptation is not as efficient for BCI training purposes.

  20. Neurofeedback using real-time near-infrared spectroscopy enhances motor imagery related cortical activation.

    Directory of Open Access Journals (Sweden)

    Masahito Mihara

    Full Text Available Accumulating evidence indicates that motor imagery and motor execution share common neural networks. Accordingly, mental practices in the form of motor imagery have been implemented in rehabilitation regimes of stroke patients with favorable results. Because direct monitoring of motor imagery is difficult, feedback of cortical activities related to motor imagery (neurofeedback could help to enhance efficacy of mental practice with motor imagery. To determine the feasibility and efficacy of a real-time neurofeedback system mediated by near-infrared spectroscopy (NIRS, two separate experiments were performed. Experiment 1 was used in five subjects to evaluate whether real-time cortical oxygenated hemoglobin signal feedback during a motor execution task correlated with reference hemoglobin signals computed off-line. Results demonstrated that the NIRS-mediated neurofeedback system reliably detected oxygenated hemoglobin signal changes in real-time. In Experiment 2, 21 subjects performed motor imagery of finger movements with feedback from relevant cortical signals and irrelevant sham signals. Real neurofeedback induced significantly greater activation of the contralateral premotor cortex and greater self-assessment scores for kinesthetic motor imagery compared with sham feedback. These findings suggested the feasibility and potential effectiveness of a NIRS-mediated real-time neurofeedback system on performance of kinesthetic motor imagery. However, these results warrant further clinical trials to determine whether this system could enhance the effects of mental practice in stroke patients.

  1. Mental Representation and Motor Imagery Training

    Directory of Open Access Journals (Sweden)

    Thomas eSchack

    2014-05-01

    Full Text Available Research in sports, dance and rehabilitation has shown that Basic Action Concepts (BACs are fundamental building blocks of mental action representations. BACs are based on chunked body postures related to common functions for realizing action goals. In this paper, we outline issues in research methodology and an experimental method, SDA-M (structural dimensional analysis of mental representation, to assess action-relevant representational structures that reflect the organization of BACs. The SDA-M reveals a strong relationship between cognitive representation and performance if complex actions are performed. We show how the SDA-M can improve motor imagery training and how it contributes to our understanding of coaching processes. The SDA-M capitalizes on the objective measurement of individual mental movement representations before training and the integration of these results into the motor imagery training. Such motor imagery training based on mental representations has been applied successfully in professional sports such as golf, volleyball, gymnastics, windsurfing, and recently in the rehabilitation of patients who have suffered a stroke.

  2. Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface

    Science.gov (United States)

    LaFleur, Karl; Cassady, Kaitlin; Doud, Alexander; Shades, Kaleb; Rogin, Eitan; He, Bin

    2013-08-01

    Objective. At the balanced intersection of human and machine adaptation is found the optimally functioning brain-computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional (3D) physical space using noninvasive scalp electroencephalogram (EEG) in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that the operation of a real world device has on subjects' control in comparison to a 2D virtual cursor task. Approach. Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a 3D physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Main results. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m s-1. Significance. Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain-computer interfaces are systems that aim to restore or enhance a user's ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in 3D physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG-based BCI systems for accomplish complex control in 3D physical space. The present study may serve as a framework for the investigation of multidimensional noninvasive BCI control in a physical environment using telepresence robotics.

  3. Electro-encephalogram based brain-computer interface: improved performance by mental practice and concentration skills.

    Science.gov (United States)

    Mahmoudi, Babak; Erfanian, Abbas

    2006-11-01

    Mental imagination is the essential part of the most EEG-based communication systems. Thus, the quality of mental rehearsal, the degree of imagined effort, and mind controllability should have a major effect on the performance of electro-encephalogram (EEG) based brain-computer interface (BCI). It is now well established that mental practice using motor imagery improves motor skills. The effects of mental practice on motor skill learning are the result of practice on central motor programming. According to this view, it seems logical that mental practice should modify the neuronal activity in the primary sensorimotor areas and consequently change the performance of EEG-based BCI. For developing a practical BCI system, recognizing the resting state with eyes opened and the imagined voluntary movement is important. For this purpose, the mind should be able to focus on a single goal for a period of time, without deviation to another context. In this work, we are going to examine the role of mental practice and concentration skills on the EEG control during imaginative hand movements. The results show that the mental practice and concentration can generally improve the classification accuracy of the EEG patterns. It is found that mental training has a significant effect on the classification accuracy over the primary motor cortex and frontal area.

  4. Brain-computer interface analysis of a dynamic visuo-motor task.

    Science.gov (United States)

    Logar, Vito; Belič, Aleš

    2011-01-01

    The area of brain-computer interfaces (BCIs) represents one of the more interesting fields in neurophysiological research, since it investigates the development of the machines that perform different transformations of the brain's "thoughts" to certain pre-defined actions. Experimental studies have reported some successful implementations of BCIs; however, much of the field still remains unexplored. According to some recent reports the phase coding of informational content is an important mechanism in the brain's function and cognition, and has the potential to explain various mechanisms of the brain's data transfer, but it has yet to be scrutinized in the context of brain-computer interface. Therefore, if the mechanism of phase coding is plausible, one should be able to extract the phase-coded content, carried by brain signals, using appropriate signal-processing methods. In our previous studies we have shown that by using a phase-demodulation-based signal-processing approach it is possible to decode some relevant information on the current motor action in the brain from electroencephalographic (EEG) data. In this paper the authors would like to present a continuation of their previous work on the brain-information-decoding analysis of visuo-motor (VM) tasks. The present study shows that EEG data measured during more complex, dynamic visuo-motor (dVM) tasks carries enough information about the currently performed motor action to be successfully extracted by using the appropriate signal-processing and identification methods. The aim of this paper is therefore to present a mathematical model, which by means of the EEG measurements as its inputs predicts the course of the wrist movements as applied by each subject during the task in simulated or real time (BCI analysis). However, several modifications to the existing methodology are needed to achieve optimal decoding results and a real-time, data-processing ability. The information extracted from the EEG could

  5. A Ternary Hybrid EEG-NIRS Brain-Computer Interface for the Classification of Brain Activation Patterns during Mental Arithmetic, Motor Imagery, and Idle State.

    Science.gov (United States)

    Shin, Jaeyoung; Kwon, Jinuk; Im, Chang-Hwan

    2018-01-01

    The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI. To date, many BCI researchers have tried to implement a hybrid BCI system by combining electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) to improve the overall accuracy of binary classification. However, since hybrid EEG-NIRS BCI, which will be denoted by hBCI in this paper, has not been applied to ternary classification problems, paradigms and classification strategies appropriate for ternary classification using hBCI are not well investigated. Here we propose the use of an hBCI for the classification of three brain activation patterns elicited by mental arithmetic, motor imagery, and idle state, with the aim to elevate the information transfer rate (ITR) of hBCI by increasing the number of classes while minimizing the loss of accuracy. EEG electrodes were placed over the prefrontal cortex and the central cortex, and NIRS optodes were placed only on the forehead. The ternary classification problem was decomposed into three binary classification problems using the "one-versus-one" (OVO) classification strategy to apply the filter-bank common spatial patterns filter to EEG data. A 10 × 10-fold cross validation was performed using shrinkage linear discriminant analysis (sLDA) to evaluate the average classification accuracies for EEG-BCI, NIRS-BCI, and hBCI when the meta-classification method was adopted to enhance classification accuracy. The ternary classification accuracies for EEG-BCI, NIRS-BCI, and hBCI were 76.1 ± 12.8, 64.1 ± 9.7, and 82.2 ± 10.2%, respectively. The classification accuracy of the proposed hBCI was thus significantly higher than those of the other BCIs ( p < 0.005). The average ITR for the proposed hBCI was calculated to be 4.70 ± 1.92 bits/minute, which was 34.3% higher than that reported for a previous binary hBCI study.

  6. Selective effect of physical fatigue on motor imagery accuracy.

    Directory of Open Access Journals (Sweden)

    Franck Di Rienzo

    Full Text Available While the use of motor imagery (the mental representation of an action without overt execution during actual training sessions is usually recommended, experimental studies examining the effect of physical fatigue on subsequent motor imagery performance are sparse and yielded divergent findings. Here, we investigated whether physical fatigue occurring during an intense sport training session affected motor imagery ability. Twelve swimmers (nine males, mean age 15.5 years conducted a 45 min physically-fatiguing protocol where they swam from 70% to 100% of their maximal aerobic speed. We tested motor imagery ability immediately before and after fatigue state. Participants randomly imagined performing a swim turn using internal and external visual imagery. Self-reports ratings, imagery times and electrodermal responses, an index of alertness from the autonomic nervous system, were the dependent variables. Self-reports ratings indicated that participants did not encounter difficulty when performing motor imagery after fatigue. However, motor imagery times were significantly shortened during posttest compared to both pretest and actual turn times, thus indicating reduced timing accuracy. Looking at the selective effect of physical fatigue on external visual imagery did not reveal any difference before and after fatigue, whereas significantly shorter imagined times and electrodermal responses (respectively 15% and 48% decrease, p<0.001 were observed during the posttest for internal visual imagery. A significant correlation (r=0.64; p<0.05 was observed between motor imagery vividness (estimated through imagery questionnaire and autonomic responses during motor imagery after fatigue. These data support that unlike local muscle fatigue, physical fatigue occurring during intense sport training sessions is likely to affect motor imagery accuracy. These results might be explained by the updating of the internal representation of the motor sequence, due to

  7. 3D visualization of movements can amplify motor cortex activation during subsequent motor imagery.

    Science.gov (United States)

    Sollfrank, Teresa; Hart, Daniel; Goodsell, Rachel; Foster, Jonathan; Tan, Tele

    2015-01-01

    A repetitive movement practice by motor imagery (MI) can influence motor cortical excitability in the electroencephalogram (EEG). This study investigated if a realistic visualization in 3D of upper and lower limb movements can amplify motor related potentials during subsequent MI. We hypothesized that a richer sensory visualization might be more effective during instrumental conditioning, resulting in a more pronounced event related desynchronization (ERD) of the upper alpha band (10-12 Hz) over the sensorimotor cortices thereby potentially improving MI based brain-computer interface (BCI) protocols for motor rehabilitation. The results show a strong increase of the characteristic patterns of ERD of the upper alpha band components for left and right limb MI present over the sensorimotor areas in both visualization conditions. Overall, significant differences were observed as a function of visualization modality (VM; 2D vs. 3D). The largest upper alpha band power decrease was obtained during MI after a 3-dimensional visualization. In total in 12 out of 20 tasks the end-user of the 3D visualization group showed an enhanced upper alpha ERD relative to 2D VM group, with statistical significance in nine tasks.With a realistic visualization of the limb movements, we tried to increase motor cortex activation during subsequent MI. The feedback and the feedback environment should be inherently motivating and relevant for the learner and should have an appeal of novelty, real-world relevance or aesthetic value (Ryan and Deci, 2000; Merrill, 2007). Realistic visual feedback, consistent with the participant's MI, might be helpful for accomplishing successful MI and the use of such feedback may assist in making BCI a more natural interface for MI based BCI rehabilitation.

  8. Task-dependent engagements of the primary visual cortex during kinesthetic and visual motor imagery.

    Science.gov (United States)

    Mizuguchi, Nobuaki; Nakamura, Maiko; Kanosue, Kazuyuki

    2017-01-01

    Motor imagery can be divided into kinesthetic and visual aspects. In the present study, we investigated excitability in the corticospinal tract and primary visual cortex (V1) during kinesthetic and visual motor imagery. To accomplish this, we measured motor evoked potentials (MEPs) and probability of phosphene occurrence during the two types of motor imageries of finger tapping. The MEPs and phosphenes were induced by transcranial magnetic stimulation to the primary motor cortex and V1, respectively. The amplitudes of MEPs and probability of phosphene occurrence during motor imagery were normalized based on the values obtained at rest. Corticospinal excitability increased during both kinesthetic and visual motor imagery, while excitability in V1 was increased only during visual motor imagery. These results imply that modulation of cortical excitability during kinesthetic and visual motor imagery is task dependent. The present finding aids in the understanding of the neural mechanisms underlying motor imagery and provides useful information for the use of motor imagery in rehabilitation or motor imagery training. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  9. Multimodal 2D Brain Computer Interface.

    Science.gov (United States)

    Almajidy, Rand K; Boudria, Yacine; Hofmann, Ulrich G; Besio, Walter; Mankodiya, Kunal

    2015-08-01

    In this work we used multimodal, non-invasive brain signal recording systems, namely Near Infrared Spectroscopy (NIRS), disc electrode electroencephalography (EEG) and tripolar concentric ring electrodes (TCRE) electroencephalography (tEEG). 7 healthy subjects participated in our experiments to control a 2-D Brain Computer Interface (BCI). Four motor imagery task were performed, imagery motion of the left hand, the right hand, both hands and both feet. The signal slope (SS) of the change in oxygenated hemoglobin concentration measured by NIRS was used for feature extraction while the power spectrum density (PSD) of both EEG and tEEG in the frequency band 8-30Hz was used for feature extraction. Linear Discriminant Analysis (LDA) was used to classify different combinations of the aforementioned features. The highest classification accuracy (85.2%) was achieved by using features from all the three brain signals recording modules. The improvement in classification accuracy was highly significant (p = 0.0033) when using the multimodal signals features as compared to pure EEG features.

  10. To What Extent Can Motor Imagery Replace Motor Execution While Learning a Fine Motor Skill?

    NARCIS (Netherlands)

    Sobierajewicz, Jagna; Szarkiewicz, Sylwia; Prekoracka-Krawczyk, Anna; Jaskowski, Wojciech; van der Lubbe, Robert Henricus Johannes

    2016-01-01

    Motor imagery is generally thought to share common mechanisms with motor execution. In the present study, we examined to what extent learning a fine motor skill by motor imagery may substitute physical practice. Learning effects were assessed by manipulating the proportion of motor execution and

  11. Auditory and motor imagery modulate learning in music performance.

    Science.gov (United States)

    Brown, Rachel M; Palmer, Caroline

    2013-01-01

    Skilled performers such as athletes or musicians can improve their performance by imagining the actions or sensory outcomes associated with their skill. Performers vary widely in their auditory and motor imagery abilities, and these individual differences influence sensorimotor learning. It is unknown whether imagery abilities influence both memory encoding and retrieval. We examined how auditory and motor imagery abilities influence musicians' encoding (during Learning, as they practiced novel melodies), and retrieval (during Recall of those melodies). Pianists learned melodies by listening without performing (auditory learning) or performing without sound (motor learning); following Learning, pianists performed the melodies from memory with auditory feedback (Recall). During either Learning (Experiment 1) or Recall (Experiment 2), pianists experienced either auditory interference, motor interference, or no interference. Pitch accuracy (percentage of correct pitches produced) and temporal regularity (variability of quarter-note interonset intervals) were measured at Recall. Independent tests measured auditory and motor imagery skills. Pianists' pitch accuracy was higher following auditory learning than following motor learning and lower in motor interference conditions (Experiments 1 and 2). Both auditory and motor imagery skills improved pitch accuracy overall. Auditory imagery skills modulated pitch accuracy encoding (Experiment 1): Higher auditory imagery skill corresponded to higher pitch accuracy following auditory learning with auditory or motor interference, and following motor learning with motor or no interference. These findings suggest that auditory imagery abilities decrease vulnerability to interference and compensate for missing auditory feedback at encoding. Auditory imagery skills also influenced temporal regularity at retrieval (Experiment 2): Higher auditory imagery skill predicted greater temporal regularity during Recall in the presence of

  12. Auditory and motor imagery modulate learning in music performance

    Science.gov (United States)

    Brown, Rachel M.; Palmer, Caroline

    2013-01-01

    Skilled performers such as athletes or musicians can improve their performance by imagining the actions or sensory outcomes associated with their skill. Performers vary widely in their auditory and motor imagery abilities, and these individual differences influence sensorimotor learning. It is unknown whether imagery abilities influence both memory encoding and retrieval. We examined how auditory and motor imagery abilities influence musicians' encoding (during Learning, as they practiced novel melodies), and retrieval (during Recall of those melodies). Pianists learned melodies by listening without performing (auditory learning) or performing without sound (motor learning); following Learning, pianists performed the melodies from memory with auditory feedback (Recall). During either Learning (Experiment 1) or Recall (Experiment 2), pianists experienced either auditory interference, motor interference, or no interference. Pitch accuracy (percentage of correct pitches produced) and temporal regularity (variability of quarter-note interonset intervals) were measured at Recall. Independent tests measured auditory and motor imagery skills. Pianists' pitch accuracy was higher following auditory learning than following motor learning and lower in motor interference conditions (Experiments 1 and 2). Both auditory and motor imagery skills improved pitch accuracy overall. Auditory imagery skills modulated pitch accuracy encoding (Experiment 1): Higher auditory imagery skill corresponded to higher pitch accuracy following auditory learning with auditory or motor interference, and following motor learning with motor or no interference. These findings suggest that auditory imagery abilities decrease vulnerability to interference and compensate for missing auditory feedback at encoding. Auditory imagery skills also influenced temporal regularity at retrieval (Experiment 2): Higher auditory imagery skill predicted greater temporal regularity during Recall in the presence of

  13. Deep learning for hybrid EEG-fNIRS brain–computer interface: application to motor imagery classification

    Science.gov (United States)

    Chiarelli, Antonio Maria; Croce, Pierpaolo; Merla, Arcangelo; Zappasodi, Filippo

    2018-06-01

    Objective. Brain–computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional near infrared spectroscopy (fNIRS). A crucial step of BCI is brain state classification from recorded signal features. Deep artificial neural networks (DNNs) recently reached unprecedented complex classification outcomes. These performances were achieved through increased computational power, efficient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting significant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art deep learning procedures. Approach. We performed a guided left and right hand motor imagery task on 15 subjects with a fixed classification response time of 1 s and overall experiment length of 10 min. Left versus right classification accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classifiers. Main results. At a group level we obtained significant increase in performance when considering multi-modal recordings and DNN classifier with synergistic effect. Significance. BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.

  14. Kinesthetic motor imagery modulates body sway.

    Science.gov (United States)

    Rodrigues, E C; Lemos, T; Gouvea, B; Volchan, E; Imbiriba, L A; Vargas, C D

    2010-08-25

    The aim of this study was to investigate the effect of imagining an action implicating the body axis in the kinesthetic and visual motor imagery modalities upon the balance control system. Body sway analysis (measurement of center of pressure, CoP) together with electromyography (EMG) recording and verbal evaluation of imagery abilities were obtained from subjects during four tasks, performed in the upright position: to execute bilateral plantar flexions; to imagine themselves executing bilateral plantar flexions (kinesthetic modality); to imagine someone else executing the same movement (visual modality), and to imagine themselves singing a song (as a control imagery task). Body sway analysis revealed that kinesthetic imagery leads to a general increase in CoP oscillation, as reflected by an enhanced area of displacement. This effect was also verified for the CoP standard deviation in the medial-lateral direction. An increase in the trembling displacement (equivalent to center of pressure minus center of gravity) restricted to the anterior-posterior direction was also observed to occur during kinesthetic imagery. The visual imagery task did not differ from the control (sing) task for any of the analyzed parameters. No difference in the subjects' ability to perform the imagery tasks was found. No modulation of EMG data were observed across imagery tasks, indicating that there was no actual execution during motor imagination. These results suggest that motor imagery performed in the kinesthetic modality evokes motor representations involved in balance control. Copyright (c)10 IBRO. Published by Elsevier Ltd. All rights reserved.

  15. Auditory and motor imagery modulate learning in music performance

    Directory of Open Access Journals (Sweden)

    Rachel M. Brown

    2013-07-01

    Full Text Available Skilled performers such as athletes or musicians can improve their performance by imagining the actions or sensory outcomes associated with their skill. Performers vary widely in their auditory and motor imagery abilities, and these individual differences influence sensorimotor learning. It is unknown whether imagery abilities influence both memory encoding and retrieval. We examined how auditory and motor imagery abilities influence musicians’ encoding (during Learning, as they practiced novel melodies, and retrieval (during Recall of those melodies. Pianists learned melodies by listening without performing (auditory learning or performing without sound (motor learning; following Learning, pianists performed the melodies from memory with auditory feedback (Recall. During either Learning (Experiment 1 or Recall (Experiment 2, pianists experienced either auditory interference, motor interference, or no interference. Pitch accuracy (percentage of correct pitches produced and temporal regularity (variability of quarter-note interonset intervals were measured at Recall. Independent tests measured auditory and motor imagery skills. Pianists’ pitch accuracy was higher following auditory learning than following motor learning and lower in motor interference conditions (Experiments 1 and 2. Both auditory and motor imagery skills improved pitch accuracy overall. Auditory imagery skills modulated pitch accuracy encoding (Experiment 1: Higher auditory imagery skill corresponded to higher pitch accuracy following auditory learning with auditory or motor interference, and following motor learning with motor or no interference. These findings suggest that auditory imagery abilities decrease vulnerability to interference and compensate for missing auditory feedback at encoding. Auditory imagery skills also influenced temporal regularity at retrieval (Experiment 2: Higher auditory imagery skill predicted greater temporal regularity during Recall in the

  16. Towards a user-friendly brain-computer interface: initial tests in ALS and PLS patients.

    Science.gov (United States)

    Bai, Ou; Lin, Peter; Huang, Dandan; Fei, Ding-Yu; Floeter, Mary Kay

    2010-08-01

    Patients usually require long-term training for effective EEG-based brain-computer interface (BCI) control due to fatigue caused by the demands for focused attention during prolonged BCI operation. We intended to develop a user-friendly BCI requiring minimal training and less mental load. Testing of BCI performance was investigated in three patients with amyotrophic lateral sclerosis (ALS) and three patients with primary lateral sclerosis (PLS), who had no previous BCI experience. All patients performed binary control of cursor movement. One ALS patient and one PLS patient performed four-directional cursor control in a two-dimensional domain under a BCI paradigm associated with human natural motor behavior using motor execution and motor imagery. Subjects practiced for 5-10min and then participated in a multi-session study of either binary control or four-directional control including online BCI game over 1.5-2h in a single visit. Event-related desynchronization and event-related synchronization in the beta band were observed in all patients during the production of voluntary movement either by motor execution or motor imagery. The online binary control of cursor movement was achieved with an average accuracy about 82.1+/-8.2% with motor execution and about 80% with motor imagery, whereas offline accuracy was achieved with 91.4+/-3.4% with motor execution and 83.3+/-8.9% with motor imagery after optimization. In addition, four-directional cursor control was achieved with an accuracy of 50-60% with motor execution and motor imagery. Patients with ALS or PLS may achieve BCI control without extended training, and fatigue might be reduced during operation of a BCI associated with human natural motor behavior. The development of a user-friendly BCI will promote practical BCI applications in paralyzed patients. Copyright 2010 International Federation of Clinical Neurophysiology. All rights reserved.

  17. Common spatial pattern combined with kernel linear discriminate and generalized radial basis function for motor imagery-based brain computer interface applications

    Science.gov (United States)

    Hekmatmanesh, Amin; Jamaloo, Fatemeh; Wu, Huapeng; Handroos, Heikki; Kilpeläinen, Asko

    2018-04-01

    Brain Computer Interface (BCI) can be a challenge for developing of robotic, prosthesis and human-controlled systems. This work focuses on the implementation of a common spatial pattern (CSP) base algorithm to detect event related desynchronization patterns. Utilizing famous previous work in this area, features are extracted by filter bank with common spatial pattern (FBCSP) method, and then weighted by a sensitive learning vector quantization (SLVQ) algorithm. In the current work, application of the radial basis function (RBF) as a mapping kernel of linear discriminant analysis (KLDA) method on the weighted features, allows the transfer of data into a higher dimension for more discriminated data scattering by RBF kernel. Afterwards, support vector machine (SVM) with generalized radial basis function (GRBF) kernel is employed to improve the efficiency and robustness of the classification. Averagely, 89.60% accuracy and 74.19% robustness are achieved. BCI Competition III, Iva data set is used to evaluate the algorithm for detecting right hand and foot imagery movement patterns. Results show that combination of KLDA with SVM-GRBF classifier makes 8.9% and 14.19% improvements in accuracy and robustness, respectively. For all the subjects, it is concluded that mapping the CSP features into a higher dimension by RBF and utilization GRBF as a kernel of SVM, improve the accuracy and reliability of the proposed method.

  18. Quantum neural network-based EEG filtering for a brain-computer interface.

    Science.gov (United States)

    Gandhi, Vaibhav; Prasad, Girijesh; Coyle, Damien; Behera, Laxmidhar; McGinnity, Thomas Martin

    2014-02-01

    A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.

  19. Self-regulation of primary motor cortex activity with motor imagery induces functional connectivity modulation: A real-time fMRI neurofeedback study.

    Science.gov (United States)

    Makary, Meena M; Seulgi, Eun; Kyungmo Park

    2017-07-01

    Recent developments in data acquisition of functional magnetic resonance imaging (fMRI) have led to rapid preprocessing and analysis of brain activity in a quasireal-time basis, what so called real-time fMRI neurofeedback (rtfMRI-NFB). This information is fed back to subjects allowing them to gain a voluntary control over their own region-specific brain activity. Forty-one healthy participants were randomized into an experimental (NFB) group, who received a feedback directly proportional to their brain activity from the primary motor cortex (M1), and a control (CTRL) group who received a sham feedback. The M1 ROI was functionally localized during motor execution and imagery tasks. A resting-state functional run was performed before and after the neurofeedback training to investigate the default mode network (DMN) modulation after training. The NFB group revealed increased DMN functional connectivity after training to the cortical and subcortical sensory/motor areas (M1/S1 and caudate nucleus, respectively), which may be associated with sensorimotor processing of learning in the resting state. These results show that motor imagery training through rtfMRI-NFB could modulate the DMN functional connectivity to motor-related areas, suggesting that this modulation potentially subserved the establishment of motor learning in the NFB group.

  20. Multiresolution analysis over graphs for a motor imagery based online BCI game.

    Science.gov (United States)

    Asensio-Cubero, Javier; Gan, John Q; Palaniappan, Ramaswamy

    2016-01-01

    Multiresolution analysis (MRA) over graph representation of EEG data has proved to be a promising method for offline brain-computer interfacing (BCI) data analysis. For the first time we aim to prove the feasibility of the graph lifting transform in an online BCI system. Instead of developing a pointer device or a wheel-chair controller as test bed for human-machine interaction, we have designed and developed an engaging game which can be controlled by means of imaginary limb movements. Some modifications to the existing MRA analysis over graphs for BCI have also been proposed, such as the use of common spatial patterns for feature extraction at the different levels of decomposition, and sequential floating forward search as a best basis selection technique. In the online game experiment we obtained for three classes an average classification rate of 63.0% for fourteen naive subjects. The application of a best basis selection method helps significantly decrease the computing resources needed. The present study allows us to further understand and assess the benefits of the use of tailored wavelet analysis for processing motor imagery data and contributes to the further development of BCI for gaming purposes. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. Whatever works: a systematic user-centered training protocol to optimize brain-computer interfacing individually.

    Directory of Open Access Journals (Sweden)

    Elisabeth V C Friedrich

    Full Text Available This study implemented a systematic user-centered training protocol for a 4-class brain-computer interface (BCI. The goal was to optimize the BCI individually in order to achieve high performance within few sessions for all users. Eight able-bodied volunteers, who were initially naïve to the use of a BCI, participated in 10 sessions over a period of about 5 weeks. In an initial screening session, users were asked to perform the following seven mental tasks while multi-channel EEG was recorded: mental rotation, word association, auditory imagery, mental subtraction, spatial navigation, motor imagery of the left hand and motor imagery of both feet. Out of these seven mental tasks, the best 4-class combination as well as most reactive frequency band (between 8-30 Hz was selected individually for online control. Classification was based on common spatial patterns and Fisher's linear discriminant analysis. The number and time of classifier updates varied individually. Selection speed was increased by reducing trial length. To minimize differences in brain activity between sessions with and without feedback, sham feedback was provided in the screening and calibration runs in which usually no real-time feedback is shown. Selected task combinations and frequency ranges differed between users. The tasks that were included in the 4-class combination most often were (1 motor imagery of the left hand (2, one brain-teaser task (word association or mental subtraction (3, mental rotation task and (4 one more dynamic imagery task (auditory imagery, spatial navigation, imagery of the feet. Participants achieved mean performances over sessions of 44-84% and peak performances in single-sessions of 58-93% in this user-centered 4-class BCI protocol. This protocol is highly adjustable to individual users and thus could increase the percentage of users who can gain and maintain BCI control. A high priority for future work is to examine this protocol with severely

  2. An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information.

    Science.gov (United States)

    Kumar, Shiu; Sharma, Alok; Tsunoda, Tatsuhiko

    2017-12-28

    Common spatial pattern (CSP) has been an effective technique for feature extraction in electroencephalography (EEG) based brain computer interfaces (BCIs). However, motor imagery EEG signal feature extraction using CSP generally depends on the selection of the frequency bands to a great extent. In this study, we propose a mutual information based frequency band selection approach. The idea of the proposed method is to utilize the information from all the available channels for effectively selecting the most discriminative filter banks. CSP features are extracted from multiple overlapping sub-bands. An additional sub-band has been introduced that cover the wide frequency band (7-30 Hz) and two different types of features are extracted using CSP and common spatio-spectral pattern techniques, respectively. Mutual information is then computed from the extracted features of each of these bands and the top filter banks are selected for further processing. Linear discriminant analysis is applied to the features extracted from each of the filter banks. The scores are fused together, and classification is done using support vector machine. The proposed method is evaluated using BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, and it outperformed all other competing methods achieving the lowest misclassification rate and the highest kappa coefficient on all three datasets. Introducing a wide sub-band and using mutual information for selecting the most discriminative sub-bands, the proposed method shows improvement in motor imagery EEG signal classification.

  3. A question of intention in motor imagery.

    Science.gov (United States)

    Gabbard, Carl; Cordova, Alberto; Lee, Sunghan

    2009-03-01

    We examined the question-is the intention of completing a simulated motor action the same as the intention used in processing overt actions? Participants used motor imagery to estimate distance reachability in two conditions: Imagery-Only (IO) and Imagery-Execution (IE). With IO (red target) only a verbal estimate using imagery was given. With IE (green target) participants knew that they would actually reach after giving a verbal estimate and be judged on accuracy. After measuring actual maximum reach, used for the comparison, imagery targets were randomly presented across peripersonal- (within reach) and extrapersonal (beyond reach) space. Results indicated no difference in overall accuracy by condition, however, there was a significant distinction by space; participants were more accurate in peripersonal space. Although more research is needed, these findings support an increasing body of evidence suggesting that the neurocognitive processes (in this case, intention) driving motor imagery and overt actions are similar.

  4. Intense imagery movements: a common and distinct paediatric subgroup of motor stereotypies.

    Science.gov (United States)

    Robinson, Sally; Woods, Martin; Cardona, Francesco; Baglioni, Valentina; Hedderly, Tammy

    2014-12-01

    The aim of this article is to describe a subgroup of children who presented with stereotyped movements in the context of episodes of intense imagery. This is of relevance to current discussions regarding the clinical usefulness of diagnosing motor stereotypies during development. The sample consisted of 10 children (nine males, one female; mean age 8y 6mo [SD 2y 5mo], range 6-15y). Referrals were from acute paediatricians, neurologists, and tertiary epilepsy services. Children were assessed by multidisciplinary teams with expertise in paediatric movement disorders. Stereotypies presented as paroxysmal complex movements involving upper and lower limbs. Imagery themes typically included computer games (60%), cartoons/films (40%), and fantasy scenes (30%). Comorbid developmental difficulties were reported for 80% of children. Brain imaging and electrophysiological investigations had been conducted for 50% of the children before referral to the clinic. The descriptive term 'intense imagery movements' (IIM) was applied if (after interview) the children reported engaging in acts of imagery while performing stereotyped movements. We believe these children may form a common and discrete stereotypy subgroup, with the concept of IIM being clinically useful to ensure the accurate diagnosis and clinical management of this paediatric movement disorder. © 2014 Mac Keith Press.

  5. Can a single session of motor imagery promote motor learning of locomotion in older adults? A randomized controlled trial

    Directory of Open Access Journals (Sweden)

    Nicholson VP

    2018-04-01

    Full Text Available Vaughan P Nicholson,1 Justin WL Keogh,2–4 Nancy L Low Choy1 1School of Physiotherapy, Australian Catholic University, Brisbane, QLD, Australia; 2Faculty of Health Sciences and Medicine, Bond University, Robina, QLD, Australia; 3Human Potential Centre, AUT University, Auckland, New Zealand; 4Cluster for Health Improvement, Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Sunshine Coast, QLD, Australia Purpose: To investigate the influence of a single session of locomotor-based motor imagery training on motor learning and physical performance. Patients and methods: Thirty independent adults aged >65 years took part in the randomized controlled trial. The study was conducted within an exercise science laboratory. Participants were randomly divided into three groups following baseline locomotor testing: motor imagery training, physical training, and control groups. The motor imagery training group completed 20 imagined repetitions of a locomotor task, the physical training group completed 20 physical repetitions of a locomotor task, and the control group spent 25 minutes playing mentally stimulating games on an iPad. Imagined and physical performance times were measured for each training repetition. Gait speed (preferred and fast, timed-up-and-go, gait variability and the time to complete an obstacle course were completed before and after the single training session. Results: Motor learning occurred in both the motor imagery training and physical training groups. Motor imagery training led to refinements in motor planning resulting in imagined movements better matching the physically performed movement at the end of training. Motor imagery and physical training also promoted improvements in some locomotion outcomes as demonstrated by medium to large effect size improvements after training for fast gait speed and timed-up-and-go. There were no training effects on gait variability. Conclusion: A single session

  6. Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns.

    Directory of Open Access Journals (Sweden)

    Camille Jeunet

    Full Text Available Mental-Imagery based Brain-Computer Interfaces (MI-BCIs allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy-EEG, which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants' BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants' performance with a mean error of less than 3 points. This study determined how users' profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user.

  7. Are we there yet? Evaluating commercial grade brain-computer interface for control of computer applications by individuals with cerebral palsy.

    Science.gov (United States)

    Taherian, Sarvnaz; Selitskiy, Dmitry; Pau, James; Claire Davies, T

    2017-02-01

    Using a commercial electroencephalography (EEG)-based brain-computer interface (BCI), the training and testing protocol for six individuals with spastic quadriplegic cerebral palsy (GMFCS and MACS IV and V) was evaluated. A customised, gamified training paradigm was employed. Over three weeks, the participants spent two sessions exploring the system, and up to six sessions playing the game which focussed on EEG feedback of left and right arm motor imagery. The participants showed variable inconclusive results in the ability to produce two distinct EEG patterns. Participant performance was influenced by physical illness, motivation, fatigue and concentration. The results from this case study highlight the infancy of BCIs as a form of assistive technology for people with cerebral palsy. Existing commercial BCIs are not designed according to the needs of end-users. Implications for Rehabilitation Mood, fatigue, physical illness and motivation influence the usability of a brain-computer interface. Commercial brain-computer interfaces are not designed for practical assistive technology use for people with cerebral palsy. Practical brain-computer interface assistive technologies may need to be flexible to suit individual needs.

  8. The influence of motor imagery on the learning of a fine hand motor skill

    NARCIS (Netherlands)

    Sobierajewicz, Jagna; Przekoracka-Krawczyk, Anna; Jaśkowski, Wojciech; Verwey, Willem B.; van der Lubbe, Rob

    2017-01-01

    Motor imagery has been argued to affect the acquisition of motor skills. The present study examined the specificity of motor imagery on the learning of a fine hand motor skill by employing a modified discrete sequence production task: the Go/NoGo DSP task. After an informative cue, a response

  9. Motor-related brain activity during action observation: a neural substrate for electrocorticographic brain-computer interfaces after spinal cord injury

    Directory of Open Access Journals (Sweden)

    Jennifer L Collinger

    2014-02-01

    Full Text Available After spinal cord injury (SCI, motor commands from the brain are unable to reach peripheral nerves and muscles below the level of the lesion. Action observation, in which a person observes someone else performing an action, has been used to augment traditional rehabilitation paradigms. Similarly, action observation can be used to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface (BCI even when the user cannot generate overt movements. BCIs use brain signals to control external devices to replace functions that have been lost due to SCI or other motor impairment. Previous studies have reported congruent motor cortical activity during observed and overt movements using magnetoencephalography (MEG and functional magnetic resonance imaging (fMRI. Recent single-unit studies using intracortical microelectrodes also demonstrated that a large number of motor cortical neurons had similar firing rate patterns between overt and observed movements. Given the increasing interest in electrocorticography (ECoG-based BCIs, our goal was to identify whether action observation-related cortical activity could be recorded using ECoG during grasping tasks. Specifically, we aimed to identify congruent neural activity during observed and executed movements in both the sensorimotor rhythm (10-40 Hz and the high-gamma band (65-115 Hz which contains significant movement-related information. We observed significant motor-related high-gamma band activity during action observation in both able-bodied individuals and one participant with a complete C4 SCI. Furthermore, in able-bodied participants, both the low and high frequency bands demonstrated congruent activity between action execution and observation. Our results suggest that action observation could be an effective and critical procedure for deriving the mapping from ECoG signals to intended movement for an ECoG-based BCI system for individuals with

  10. Cognitive alterations in motor imagery process after left hemispheric ischemic stroke.

    Directory of Open Access Journals (Sweden)

    Jing Yan

    Full Text Available BACKGROUND: Motor imagery training is a promising rehabilitation strategy for stroke patients. However, few studies had focused on the neural mechanisms in time course of its cognitive process. This study investigated the cognitive alterations after left hemispheric ischemic stroke during motor imagery task. METHODOLOGY/PRINCIPAL FINDINGS: Eleven patients with ischemic stroke in left hemisphere and eleven age-matched control subjects participated in mental rotation task (MRT of hand pictures. Behavior performance, event-related potential (ERP and event-related (desynchronization (ERD/ERS in beta band were analyzed to investigate the cortical activation. We found that: (1 The response time increased with orientation angles in both groups, called "angle effect", however, stoke patients' responses were impaired with significantly longer response time and lower accuracy rate; (2 In early visual perceptual cognitive process, stroke patients showed hypo-activations in frontal and central brain areas in aspects of both P200 and ERD; (3 During mental rotation process, P300 amplitude in control subjects decreased while angle increased, called "amplitude modulation effect", which was not observed in stroke patients. Spatially, patients showed significant lateralization of P300 with activation only in contralesional (right parietal cortex while control subjects showed P300 in both parietal lobes. Stroke patients also showed an overall cortical hypo-activation of ERD during this sub-stage; (4 In the response sub-stage, control subjects showed higher ERD values with more activated cortical areas particularly in the right hemisphere while angle increased, named "angle effect", which was not observed in stroke patients. In addition, stroke patients showed significant lower ERD for affected hand (right response than that for unaffected hand. CONCLUSIONS/SIGNIFICANCE: Cortical activation was altered differently in each cognitive sub-stage of motor imagery after

  11. Could the beta rebound in the EEG be suitable to realize a "brain switch"?

    Science.gov (United States)

    Pfurtscheller, G; Solis-Escalante, T

    2009-01-01

    Performing foot motor imagery is accompanied by a peri-imagery ERD and a post-imagery beta ERS (beta rebound). Our aim was to study whether the post-imagery beta rebound is a suitable feature for a simple "brain switch". Such a brain switch is a specifically designed brain-computer interface (BCI) with the aim to detect only one predefined brain state (e.g. EEG pattern) in ongoing brain activity. One EEG (Laplacian) recorded at the vertex during cue-based brisk foot motor imagery was analysed in 5 healthy subjects. The peri-imagery ERD and the post-imagery beta rebound (ERS) were analysed in detail between 6 and 40Hz and classified with two support vector machines. The ERD was detected in ongoing EEG (simulation of asynchronous BCI) with a true positive rate (TPR) of 28.4%+/-13.5 and the beta rebound with a TPR of 59.2%+/-20.3. In single runs with 30 cues each, the TPR for beta rebound detection was 78.6%+/-12.8. The false positive rate was always kept below 10%. The findings suggest that the beta rebound at Cz during foot motor imagery is a relatively stable and reproducible phenomenon detectable in single EEG trials. Our results indicate that the beta rebound is a suitable feature to realize a "brain switch" with one single EEG (Laplacian) channel only.

  12. Kinesthetic perception based on integration of motor imagery and afferent inputs from antagonistic muscles with tendon vibration.

    Science.gov (United States)

    Shibata, E; Kaneko, F

    2013-04-29

    The perceptual integration of afferent inputs from two antagonistic muscles, or the perceptual integration of afferent input and motor imagery are related to the generation of a kinesthetic sensation. However, it has not been clarified how, or indeed whether, a kinesthetic perception would be generated by motor imagery if afferent inputs from two antagonistic muscles were simultaneously induced by tendon vibration. The purpose of this study was to investigate how a kinesthetic perception would be generated by motor imagery during co-vibration of the two antagonistic muscles at the same frequency. Healthy subjects participated in this experiment. Illusory movement was evoked by tendon vibration. Next, the subjects imaged wrist flexion movement simultaneously with tendon vibration. Wrist flexor and extensor muscles were vibrated according to 4 patterns such that the difference between the two vibration frequencies was zero. After each trial, the perceived movement sensations were quantified on the basis of the velocity and direction of the ipsilateral hand-tracking movements. When the difference in frequency applied to the wrist flexor and the extensor was 0Hz, no subjects perceived movements without motor imagery. However, during motor imagery, the flexion velocity of the perceived movement was higher than the flexion velocity without motor imagery. This study clarified that the afferent inputs from the muscle spindle interact with motor imagery, to evoke a kinesthetic perception, even when the difference in frequency applied to the wrist flexor and extensor was 0Hz. Furthermore, the kinesthetic perception resulting from integrations of vibration and motor imagery increased depending on the vibration frequency to the two antagonistic muscles. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  13. User Experience May be Producing Greater Heart Rate Variability than Motor Imagery Related Control Tasks during the User-System Adaptation in Brain-Computer Interfaces

    Science.gov (United States)

    Alonso-Valerdi, Luz M.; Gutiérrez-Begovich, David A.; Argüello-García, Janet; Sepulveda, Francisco; Ramírez-Mendoza, Ricardo A.

    2016-01-01

    Brain-computer interface (BCI) is technology that is developing fast, but it remains inaccurate, unreliable and slow due to the difficulty to obtain precise information from the brain. Consequently, the involvement of other biosignals to decode the user control tasks has risen in importance. A traditional way to operate a BCI system is via motor imagery (MI) tasks. As imaginary movements activate similar cortical structures and vegetative mechanisms as a voluntary movement does, heart rate variability (HRV) has been proposed as a parameter to improve the detection of MI related control tasks. However, HR is very susceptible to body needs and environmental demands, and as BCI systems require high levels of attention, perceptual processing and mental workload, it is important to assess the practical effectiveness of HRV. The present study aimed to determine if brain and heart electrical signals (HRV) are modulated by MI activity used to control a BCI system, or if HRV is modulated by the user perceptions and responses that result from the operation of a BCI system (i.e., user experience). For this purpose, a database of 11 participants who were exposed to eight different situations was used. The sensory-cognitive load (intake and rejection tasks) was controlled in those situations. Two electrophysiological signals were utilized: electroencephalography and electrocardiography. From those biosignals, event-related (de-)synchronization maps and event-related HR changes were respectively estimated. The maps and the HR changes were cross-correlated in order to verify if both biosignals were modulated due to MI activity. The results suggest that HR varies according to the experience undergone by the user in a BCI working environment, and not because of the MI activity used to operate the system. PMID:27458384

  14. Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata

    Directory of Open Access Journals (Sweden)

    Aiming Liu

    2017-11-01

    Full Text Available Motor Imagery (MI electroencephalography (EEG is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP and local characteristic-scale decomposition (LCD algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA classifier. Both the fourth brain–computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain–computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain–computer interface systems.

  15. Motor imagery training: Kinesthetic imagery strategy and inferior parietal fMRI activation.

    Science.gov (United States)

    Lebon, Florent; Horn, Ulrike; Domin, Martin; Lotze, Martin

    2018-04-01

    Motor imagery (MI) is the mental simulation of action frequently used by professionals in different fields. However, with respect to performance, well-controlled functional imaging studies on MI training are sparse. We investigated changes in fMRI representation going along with performance changes of a finger sequence (error and velocity) after MI training in 48 healthy young volunteers. Before training, we tested the vividness of kinesthetic and visual imagery. During tests, participants were instructed to move or to imagine moving the fingers of the right hand in a specific order. During MI training, participants repeatedly imagined the sequence for 15 min. Imaging analysis was performed using a full-factorial design to assess brain changes due to imagery training. We also used regression analyses to identify those who profited from training (performance outcome and gain) with initial imagery scores (vividness) and fMRI activation magnitude during MI at pre-test (MI pre ). After training, error rate decreased and velocity increased. We combined both parameters into a common performance index. FMRI activation in the left inferior parietal lobe (IPL) was associated with MI and increased over time. In addition, fMRI activation in the right IPL during MI pre was associated with high initial kinesthetic vividness. High kinesthetic imagery vividness predicted a high performance after training. In contrast, occipital activation, associated with visual imagery strategies, showed a negative predictive value for performance. Our data echo the importance of high kinesthetic vividness for MI training outcome and consider IPL as a key area during MI and through MI training. © 2018 Wiley Periodicals, Inc.

  16. Rehearsal strategies during motor-sequence learning in old age : Execution vs motor imagery

    NARCIS (Netherlands)

    Stoter, Arjan J. R.; Scherder, Erik J. A.; Kamsma, Yvo P. T.; Mulder, Theo

    Motor imagery and action-based rehearsal were compared during motor sequence-learning by young adults (M = 25 yr., SD = 3) and aged adults (M = 63 yr., SD = 7). General accuracy of aged adults was lower than that of young adults (F-1,F-28 = 7.37, p = .01) even though working-memory capacity was

  17. Development of a Novel Motor Imagery Control Technique and Application in a Gaming Environment.

    Science.gov (United States)

    Li, Ting; Zhang, Jinhua; Xue, Tao; Wang, Baozeng

    2017-01-01

    We present a methodology for a hybrid brain-computer interface (BCI) system, with the recognition of motor imagery (MI) based on EEG and blink EOG signals. We tested the BCI system in a 3D Tetris and an analogous 2D game playing environment. To enhance player's BCI control ability, the study focused on feature extraction from EEG and control strategy supporting Game-BCI system operation. We compared the numerical differences between spatial features extracted with common spatial pattern (CSP) and the proposed multifeature extraction. To demonstrate the effectiveness of 3D game environment at enhancing player's event-related desynchronization (ERD) and event-related synchronization (ERS) production ability, we set the 2D Screen Game as the comparison experiment. According to a series of statistical results, the group performing MI in the 3D Tetris environment showed more significant improvements in generating MI-associated ERD/ERS. Analysis results of game-score indicated that the players' scores presented an obvious uptrend in 3D Tetris environment but did not show an obvious downward trend in 2D Screen Game. It suggested that the immersive and rich-control environment for MI would improve the associated mental imagery and enhance MI-based BCI skills.

  18. Development of a Novel Motor Imagery Control Technique and Application in a Gaming Environment

    Directory of Open Access Journals (Sweden)

    Ting Li

    2017-01-01

    Full Text Available We present a methodology for a hybrid brain-computer interface (BCI system, with the recognition of motor imagery (MI based on EEG and blink EOG signals. We tested the BCI system in a 3D Tetris and an analogous 2D game playing environment. To enhance player’s BCI control ability, the study focused on feature extraction from EEG and control strategy supporting Game-BCI system operation. We compared the numerical differences between spatial features extracted with common spatial pattern (CSP and the proposed multifeature extraction. To demonstrate the effectiveness of 3D game environment at enhancing player’s event-related desynchronization (ERD and event-related synchronization (ERS production ability, we set the 2D Screen Game as the comparison experiment. According to a series of statistical results, the group performing MI in the 3D Tetris environment showed more significant improvements in generating MI-associated ERD/ERS. Analysis results of game-score indicated that the players’ scores presented an obvious uptrend in 3D Tetris environment but did not show an obvious downward trend in 2D Screen Game. It suggested that the immersive and rich-control environment for MI would improve the associated mental imagery and enhance MI-based BCI skills.

  19. Motor imagery: Lessons learned in movement science might be applicable for spaceflight

    Directory of Open Access Journals (Sweden)

    Otmar eBock

    2015-05-01

    Full Text Available Before participating in a space mission, astronauts undergo parabolic-flight and underwater training to facilitate their subsequent adaptation to weightlessness. Unfortunately, similar training methods can’t be used to prepare re-adaptation to planetary gravity. Here, we propose a quick, simple and inexpensive approach that could be used to prepare astronauts both for the absence and for the renewed presence of gravity. This approach is based on motor imagery (MI, a process in which actions are produced in working memory without any overt output. Training protocols based on MI have repeatedly been shown to modify brain circuitry and to improve motor performance in healthy young adults, healthy seniors and stroke victims, and are routinely used to optimize performance of elite athletes. We propose to use similar protocols preflight, to prepare for weightlessness, and late inflight, to prepare for landing.

  20. Motor imagery: lessons learned in movement science might be applicable for spaceflight

    Science.gov (United States)

    Bock, Otmar; Schott, Nadja; Papaxanthis, Charalambos

    2015-01-01

    Before participating in a space mission, astronauts undergo parabolic-flight and underwater training to facilitate their subsequent adaptation to weightlessness. Unfortunately, similar training methods can’t be used to prepare re-adaptation to planetary gravity. Here, we propose a quick, simple and inexpensive approach that could be used to prepare astronauts both for the absence and for the renewed presence of gravity. This approach is based on motor imagery (MI), a process in which actions are produced in working memory without any overt output. Training protocols based on MI have repeatedly been shown to modify brain circuitry and to improve motor performance in healthy young adults, healthy seniors and stroke victims, and are routinely used to optimize performance of elite athletes. We propose to use similar protocols preflight, to prepare for weightlessness, and late inflight, to prepare for landing. PMID:26042004

  1. The effect of motor imagery with specific implement in expert badminton player.

    Science.gov (United States)

    Wang, Z; Wang, S; Shi, F-Y; Guan, Y; Wu, Y; Zhang, L-L; Shen, C; Zeng, Y-W; Wang, D-H; Zhang, J

    2014-09-05

    Motor skill can be improved with mental simulation. Implements are widely used in daily life and in various sports. However, it is unclear whether the utilization of implements enhances the effect of mental simulation. The present study was designed to investigate the different effects of motor imagery in athletes and novices when they handled a specific implement. We hypothesize that athletes have better motor imagery ability than novices when they hold a specific implement for the sport. This is manifested as higher motor cortical excitability in athletes than novices during motor imagery with the specific implement. Sixteen expert badminton players and 16 novices were compared when they held a specific implement such as a badminton racket and a non-specific implement such as a a plastic bar. Motor imagery ability was measured with a self-evaluation questionnaire. Transcranial magnetic stimulation was used to test the motor cortical excitability during motor imagery. Motor-evoked potentials (MEPs) in the first dorsal interosseous (FDI) and extensor carpi radialis muscles were recorded. Athletes reported better motor imagery than novices when they held a specific implement. Athletes exhibited more MEP facilitation than novices in the FDI muscle with the specific implement applied during motor imagery. The MEP facilitation is correlated with motor imagery ability in athletes. We conclude that the effects of motor imagery with a specific implement are enhanced in athletes compared to novices and the difference between two groups is caused by long-term physical training of athletes with the specific implement. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

  2. The Self-Paced Graz Brain-Computer Interface: Methods and Applications

    Directory of Open Access Journals (Sweden)

    Reinhold Scherer

    2007-01-01

    Full Text Available We present the self-paced 3-class Graz brain-computer interface (BCI which is based on the detection of sensorimotor electroencephalogram (EEG rhythms induced by motor imagery. Self-paced operation means that the BCI is able to determine whether the ongoing brain activity is intended as control signal (intentional control or not (non-control state. The presented system is able to automatically reduce electrooculogram (EOG artifacts, to detect electromyographic (EMG activity, and uses only three bipolar EEG channels. Two applications are presented: the freeSpace virtual environment (VE and the Brainloop interface. The freeSpace is a computer-game-like application where subjects have to navigate through the environment and collect coins by autonomously selecting navigation commands. Three subjects participated in these feedback experiments and each learned to navigate through the VE and collect coins. Two out of the three succeeded in collecting all three coins. The Brainloop interface provides an interface between the Graz-BCI and Google Earth.

  3. Studying Action Representation in Children via Motor Imagery

    Science.gov (United States)

    Gabbard, Carl

    2009-01-01

    The use of motor imagery is a widely used experimental paradigm for the study of cognitive aspects of action planning and control in adults. Furthermore, there are indications that motor imagery provides a window into the process of action representation. These notions complement internal model theory suggesting that such representations allow…

  4. Haptic, Virtual Interaction and Motor Imagery: Entertainment Tools and Psychophysiological Testing

    Directory of Open Access Journals (Sweden)

    Sara Invitto

    2016-03-01

    Full Text Available In this work, the perception of affordances was analysed in terms of cognitive neuroscience during an interactive experience in a virtual reality environment. In particular, we chose a virtual reality scenario based on the Leap Motion controller: this sensor device captures the movements of the user’s hand and fingers, which are reproduced on a computer screen by the proper software applications. For our experiment, we employed a sample of 10 subjects matched by age and sex and chosen among university students. The subjects took part in motor imagery training and immersive affordance condition (a virtual training with Leap Motion and a haptic training with real objects. After each training sessions the subject performed a recognition task, in order to investigate event-related potential (ERP components. The results revealed significant differences in the attentional components during the Leap Motion training. During Leap Motion session, latencies increased in the occipital lobes, which are entrusted to visual sensory; in contrast, latencies decreased in the frontal lobe, where the brain is mainly activated for attention and action planning.

  5. Haptic, Virtual Interaction and Motor Imagery: Entertainment Tools and Psychophysiological Testing.

    Science.gov (United States)

    Invitto, Sara; Faggiano, Chiara; Sammarco, Silvia; De Luca, Valerio; De Paolis, Lucio T

    2016-03-18

    In this work, the perception of affordances was analysed in terms of cognitive neuroscience during an interactive experience in a virtual reality environment. In particular, we chose a virtual reality scenario based on the Leap Motion controller: this sensor device captures the movements of the user's hand and fingers, which are reproduced on a computer screen by the proper software applications. For our experiment, we employed a sample of 10 subjects matched by age and sex and chosen among university students. The subjects took part in motor imagery training and immersive affordance condition (a virtual training with Leap Motion and a haptic training with real objects). After each training sessions the subject performed a recognition task, in order to investigate event-related potential (ERP) components. The results revealed significant differences in the attentional components during the Leap Motion training. During Leap Motion session, latencies increased in the occipital lobes, which are entrusted to visual sensory; in contrast, latencies decreased in the frontal lobe, where the brain is mainly activated for attention and action planning.

  6. Haptic, Virtual Interaction and Motor Imagery: Entertainment Tools and Psychophysiological Testing

    Science.gov (United States)

    Invitto, Sara; Faggiano, Chiara; Sammarco, Silvia; De Luca, Valerio; De Paolis, Lucio T.

    2016-01-01

    In this work, the perception of affordances was analysed in terms of cognitive neuroscience during an interactive experience in a virtual reality environment. In particular, we chose a virtual reality scenario based on the Leap Motion controller: this sensor device captures the movements of the user’s hand and fingers, which are reproduced on a computer screen by the proper software applications. For our experiment, we employed a sample of 10 subjects matched by age and sex and chosen among university students. The subjects took part in motor imagery training and immersive affordance condition (a virtual training with Leap Motion and a haptic training with real objects). After each training sessions the subject performed a recognition task, in order to investigate event-related potential (ERP) components. The results revealed significant differences in the attentional components during the Leap Motion training. During Leap Motion session, latencies increased in the occipital lobes, which are entrusted to visual sensory; in contrast, latencies decreased in the frontal lobe, where the brain is mainly activated for attention and action planning. PMID:26999151

  7. Kinesthetic Imagery Provides Additive Benefits to Internal Visual Imagery on Slalom Task Performance.

    Science.gov (United States)

    Callow, Nichola; Jiang, Dan; Roberts, Ross; Edwards, Martin G

    2017-02-01

    Recent brain imaging research demonstrates that the use of internal visual imagery (IVI) or kinesthetic imagery (KIN) activates common and distinct brain areas. In this paper, we argue that combining the imagery modalities (IVI and KIN) will lead to a greater cognitive representation (with more brain areas activated), and this will cause a greater slalom-based motor performance compared with using IVI alone. To examine this assertion, we randomly allocated 56 participants to one of the three groups: IVI, IVI and KIN, or a math control group. Participants performed a slalom-based driving task in a driving simulator, with average lap time used as a measure of performance. Results revealed that the IVI and KIN group achieved significantly quicker lap times than the IVI and the control groups. The discussion includes a theoretical advancement on why the combination of imagery modalities might facilitate performance, with links made to the cognitive neuroscience literature and applied practice.

  8. A novel deep learning approach for classification of EEG motor imagery signals.

    Science.gov (United States)

    Tabar, Yousef Rezaei; Halici, Ugur

    2017-02-01

    Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals. A new form of input is introduced to combine time, frequency and location information extracted from EEG signal and it is used in CNN having one 1D convolutional and one max-pooling layers. We also proposed a new deep network by combining CNN and SAE. In this network, the features that are extracted in CNN are classified through the deep network SAE. The classification performance obtained by the proposed method on BCI competition IV dataset 2b in terms of kappa value is 0.547. Our approach yields 9% improvement over the winner algorithm of the competition. Our results show that deep learning methods provide better classification performance compared to other state of art approaches. These methods can be applied successfully to BCI systems where the amount of data is large due to daily recording.

  9. Performance predictors of brain-computer interfaces in patients with amyotrophic lateral sclerosis

    Science.gov (United States)

    Geronimo, A.; Simmons, Z.; Schiff, S. J.

    2016-04-01

    Objective. Patients with amyotrophic lateral sclerosis (ALS) may benefit from brain-computer interfaces (BCI), but the utility of such devices likely will have to account for the functional, cognitive, and behavioral heterogeneity of this neurodegenerative disorder. Approach. In this study, a heterogeneous group of patients with ALS participated in a study on BCI based on the P300 event related potential and motor-imagery. Results. The presence of cognitive impairment in these patients significantly reduced the quality of the control signals required to use these communication systems, subsequently impairing performance, regardless of progression of physical symptoms. Loss in performance among the cognitively impaired was accompanied by a decrease in the signal-to-noise ratio of task-relevant EEG band power. There was also evidence that behavioral dysfunction negatively affects P300 speller performance. Finally, older participants achieved better performance on the P300 system than the motor-imagery system, indicating a preference of BCI paradigm with age. Significance. These findings highlight the importance of considering the heterogeneity of disease when designing BCI augmentative and alternative communication devices for clinical applications.

  10. Testing the distinctiveness of visual imagery and motor imagery in a reach paradigm.

    Science.gov (United States)

    Gabbard, Carl; Ammar, Diala; Cordova, Alberto

    2009-01-01

    We examined the distinctiveness of motor imagery (MI) and visual imagery (VI) in the context of perceived reachability. The aim was to explore the notion that the two visual modes have distinctive processing properties tied to the two-visual-system hypothesis. The experiment included an interference tactic whereby participants completed two tasks at the same time: a visual or motor-interference task combined with a MI or VI-reaching task. We expected increased error would occur when the imaged task and the interference task were matched (e.g., MI with the motor task), suggesting an association based on the assumption that the two tasks were in competition for space on the same processing pathway. Alternatively, if there were no differences, dissociation could be inferred. Significant increases in the number of errors were found when the modalities for the imaged (both MI and VI) task and the interference task were matched. Therefore, it appears that MI and VI in the context of perceived reachability recruit different processing mechanisms.

  11. Training leads to increased auditory brain-computer interface performance of end-users with motor impairments.

    Science.gov (United States)

    Halder, S; Käthner, I; Kübler, A

    2016-02-01

    Auditory brain-computer interfaces are an assistive technology that can restore communication for motor impaired end-users. Such non-visual brain-computer interface paradigms are of particular importance for end-users that may lose or have lost gaze control. We attempted to show that motor impaired end-users can learn to control an auditory speller on the basis of event-related potentials. Five end-users with motor impairments, two of whom with additional visual impairments, participated in five sessions. We applied a newly developed auditory brain-computer interface paradigm with natural sounds and directional cues. Three of five end-users learned to select symbols using this method. Averaged over all five end-users the information transfer rate increased by more than 1800% from the first session (0.17 bits/min) to the last session (3.08 bits/min). The two best end-users achieved information transfer rates of 5.78 bits/min and accuracies of 92%. Our results show that an auditory BCI with a combination of natural sounds and directional cues, can be controlled by end-users with motor impairment. Training improves the performance of end-users to the level of healthy controls. To our knowledge, this is the first time end-users with motor impairments controlled an auditory brain-computer interface speller with such high accuracy and information transfer rates. Further, our results demonstrate that operating a BCI with event-related potentials benefits from training and specifically end-users may require more than one session to develop their full potential. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  12. Coupling brain-machine interfaces with cortical stimulation for brain-state dependent stimulation: enhancing motor cortex excitability for neurorehabilitation

    Directory of Open Access Journals (Sweden)

    Alireza eGharabaghi

    2014-03-01

    Full Text Available Motor recovery after stroke is an unsolved challenge despite intensive rehabilitation training programs. Brain stimulation techniques have been explored in addition to traditional rehabilitation training to increase the excitability of the stimulated motor cortex. This modulation of cortical excitability augments the response to afferent input during motor exercises, thereby enhancing skilled motor learning by long-term potentiation-like plasticity. Recent approaches examined brain stimulation applied concurrently with voluntary movements to induce more specific use-dependent neural plasticity during motor training for neurorehabilitation. Unfortunately, such approaches are not applicable for the many severely affected stroke patients lacking residual hand function. These patients require novel activity-dependent stimulation paradigms based on intrinsic brain activity. Here, we report on such brain state-dependent stimulation (BSDS combined with haptic feedback provided by a robotic hand orthosis. Transcranial magnetic stimulation of the motor cortex and haptic feedback to the hand were controlled by sensorimotor desynchronization during motor-imagery and applied within a brain-machine interface environment in one healthy subject and one patient with severe hand paresis in the chronic phase after stroke. BSDS significantly increased the excitability of the stimulated motor cortex in both healthy and post-stroke conditions, an effect not observed in non-BSDS protocols. This feasibility study suggests that closing the loop between intrinsic brain state, cortical stimulation and haptic feedback provides a novel neurorehabilitation strategy for stroke patients lacking residual hand function, a proposal that warrants further investigation in a larger cohort of stroke patients.

  13. User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of Distributed Algorithms

    Directory of Open Access Journals (Sweden)

    Aitzol Astigarraga

    2016-01-01

    Full Text Available Brain-Computer Interfaces (BCIs have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram- (EEG- based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range; and, then, an Estimation of Distribution Algorithm (EDA is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels.

  14. Enhanced Motor Imagery-Based BCI Performance via Tactile Stimulation on Unilateral Hand

    Directory of Open Access Journals (Sweden)

    Xiaokang Shu

    2017-12-01

    Full Text Available Brain-computer interface (BCI has attracted great interests for its effectiveness in assisting disabled people. However, due to the poor BCI performance, this technique is still far from daily-life applications. One of critical issues confronting BCI research is how to enhance BCI performance. This study aimed at improving the motor imagery (MI based BCI accuracy by integrating MI tasks with unilateral tactile stimulation (Uni-TS. The effects were tested on both healthy subjects and stroke patients in a controlled study. Twenty-two healthy subjects and four stroke patients were recruited and randomly divided into a control-group and an enhanced-group. In the control-group, subjects performed two blocks of conventional MI tasks (left hand vs. right hand, with 80 trials in each block. In the enhanced-group, subjects also performed two blocks of MI tasks, but constant tactile stimulation was applied on the non-dominant/paretic hand during MI tasks in the second block. We found the Uni-TS significantly enhanced the contralateral cortical activations during MI of the stimulated hand, whereas it had no influence on activation patterns during MI of the non-stimulated hand. The two-class BCI decoding accuracy was significantly increased from 72.5% (MI without Uni-TS to 84.7% (MI with Uni-TS in the enhanced-group (p < 0.001, paired t-test. Moreover, stroke patients in the enhanced-group achieved an accuracy >80% during MI with Uni-TS. This novel approach complements the conventional methods for BCI enhancement without increasing source information or complexity of signal processing. This enhancement via Uni-TS may facilitate clinical applications of MI-BCI.

  15. Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification.

    Science.gov (United States)

    She, Qingshan; Ma, Yuliang; Meng, Ming; Luo, Zhizeng

    2015-01-01

    Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt's estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively.

  16. Effect of Different Mental Imagery Speeds on the Motor Performance: Investigation of the Role of Mirror Neurons

    Directory of Open Access Journals (Sweden)

    Sajad Parsaei

    2017-09-01

    Conclusion: The results of this study showed that mirror neurons within the premotor cortex are an important neural mechanism in the brain activity pattern, which causes the effectiveness of imagery in the improvement of motor skills.  

  17. User-customized brain computer interfaces using Bayesian optimization.

    Science.gov (United States)

    Bashashati, Hossein; Ward, Rabab K; Bashashati, Ali

    2016-04-01

    The brain characteristics of different people are not the same. Brain computer interfaces (BCIs) should thus be customized for each individual person. In motor-imagery based synchronous BCIs, a number of parameters (referred to as hyper-parameters) including the EEG frequency bands, the channels and the time intervals from which the features are extracted should be pre-determined based on each subject's brain characteristics. To determine the hyper-parameter values, previous work has relied on manual or semi-automatic methods that are not applicable to high-dimensional search spaces. In this paper, we propose a fully automatic, scalable and computationally inexpensive algorithm that uses Bayesian optimization to tune these hyper-parameters. We then build different classifiers trained on the sets of hyper-parameter values proposed by the Bayesian optimization. A final classifier aggregates the results of the different classifiers. We have applied our method to 21 subjects from three BCI competition datasets. We have conducted rigorous statistical tests, and have shown the positive impact of hyper-parameter optimization in improving the accuracy of BCIs. Furthermore, We have compared our results to those reported in the literature. Unlike the best reported results in the literature, which are based on more sophisticated feature extraction and classification methods, and rely on prestudies to determine the hyper-parameter values, our method has the advantage of being fully automated, uses less sophisticated feature extraction and classification methods, and yields similar or superior results compared to the best performing designs in the literature.

  18. Can motor imagery and hypnotic susceptibility explain Conversion Disorder with motor symptoms?

    Science.gov (United States)

    Srzich, Alexander J; Byblow, Winston D; Stinear, James W; Cirillo, John; Anson, J Greg

    2016-08-01

    Marked distortions in sense of agency can be induced by hypnosis in susceptible individuals, including alterations in subjective awareness of movement initiation and control. These distortions, with associated disability, are similar to those experienced with Conversion Disorder (CD), an observation that has led to the hypothesis that hypnosis and CD share causal mechanisms. The purpose of this review is to explore the relationships among motor imagery (MI), hypnotic susceptibility, and CD, then to propose how MI ability may contribute to hypnotic responding and CD. Studies employing subjective assessments of mental imagery have found little association between imagery abilities and hypnotic susceptibility. A positive association between imagery abilities and hypnotic susceptibility becomes apparent when objective measures of imagery ability are employed. A candidate mechanism to explain motor responses during hypnosis is kinaesthetic MI, which engages a strategy that involves proprioception or the "feel" of movement when no movement occurs. Motor suppression imagery (MSI), a strategy involving inhibition of movement, may provide an alternate objective measurable phenomenon that underlies both hypnotic susceptibility and CD. Evidence to date supports the idea that there may be a positive association between kinaesthetic MI ability and hypnotic susceptibility. Additional evidence supports a positive association between hypnotic susceptibility and CD. Disturbances in kinaesthetic MI performance in CD patients indicate that MI mechanisms may also underlie CD symptoms. Further investigation of the above relationships is warranted to explain these phenomena, and establish theoretical explanations underlying sense of agency. Copyright © 2016. Published by Elsevier Ltd.

  19. Rehabilitation of the elbow extension with motor imagery in a patient with quadriplegia after tendon transfer.

    Science.gov (United States)

    Grangeon, Murielle; Guillot, Aymeric; Sancho, Pierre-Olivier; Picot, Marion; Revol, Patrice; Rode, Gilles; Collet, Christian

    2010-07-01

    To test the effect of a postsurgical motor imagery program in the rehabilitation of a patient with quadriplegia. Crossover design with kinematic analysis. Rehabilitation Hospital of Lyon. Study approved by the local Human Research Ethics Committee. C6-level injured patient (American Spinal Injury Association Impairment Scale grade A) with no voluntary elbow extension (triceps brachialis score 1). The surgical procedure was to transfer the distal insertion of the biceps brachii onto the triceps tendon of both arms. The postsurgical intervention on the left arm included 10 sessions of physical rehabilitation followed by 10 motor imagery sessions of 30 minutes each. The patient underwent 5 sessions a week during 2 consecutive weeks. The motor imagery content included mental representations based on elbow extension involved in goal-directed movements. The rehabilitation period of the right arm was reversed, with motor imagery performed first, followed by physical therapy. The kinematics of upper-limb movements was recorded (movement time and variability) before and after each type of rehabilitation period. A long-term retention test was performed 1 month later. Motor imagery training enhanced motor recovery by reducing hand trajectory variability-that is, improving smoothness. Motor performance then remained stable over 1 month. Motor imagery improved motor recovery when associated with physical therapy, with motor performance remaining stable over the 1-month period. We concluded that motor imagery should be successfully associated with classic rehabilitation procedure after tendon transfer. Physical sessions may thus be shortened if too stressful or painful. Copyright 2010 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  20. Neurofeedback fMRI-mediated learning and consolidation of regional brain activation during motor imagery

    Science.gov (United States)

    Yoo, Seung-Schik; Lee, Jong-Hwan; O’Leary, Heather; Panych, Lawrence P.; Jolesz, Ferenc A.

    2009-01-01

    We report the long-term effect of real-time functional MRI (rtfMRI) training on voluntary regulation of the level of activation from a hand motor area. During the performance of a motor imagery task of a right hand, blood-oxygenation-level-dependent (BOLD) signal originating from a primary motor area was presented back to the subject in real-time. Demographically matched individuals also received the same procedure without valid feedback information. Followed by the initial rtfMRI sessions, both groups underwent two-week long, daily-practice of the task. Off-line data analysis revealed that the individuals in the experimental group were able to increase the level of BOLD signal from the regulatory target to a greater degree compared to the control group. Furthermore, the learned level of activation was maintained after the two-week period, with the recruitment of additional neural circuitries such as the hippocampus and the limbo-thalamo-cortical pathway. The activation obtained from the control group, in the absence of proper feedback, was indifferent across the training conditions. The level of BOLD activity from the target regulatory region was positively correlated with a self evaluative score within the experimental group, while the majority of control subjects had difficulty adopting a strategy to attain the desired level of functional regulation. Our results suggest that rtfMRI helped individuals learn how to increase region-specific cortical activity associated with a motor imagery task, and the level of increased activation in motor areas was consolidated after the two-week self-practice period, with the involvement of neural circuitries implicated in motor skill learning. PMID:19526048

  1. Does transcranial direct current stimulation affect the learning of a fine sequential hand motor skill with motor imagery?

    NARCIS (Netherlands)

    Sobierajewicz, Jagna; Jaskowski, Wojciech; van der Lubbe, Robert Henricus Johannes

    2017-01-01

    Learning a fine sequential hand motor skill, comparable to playing the piano or learning to type, improves not only due to physical practice, but also due to motor imagery. Previous studies revealed that transcranial direct current stimulation (tDCS) and motor imagery independently affect motor

  2. On the role of cost-sensitive learning in multi-class brain-computer interfaces.

    Science.gov (United States)

    Devlaminck, Dieter; Waegeman, Willem; Wyns, Bart; Otte, Georges; Santens, Patrick

    2010-06-01

    Brain-computer interfaces (BCIs) present an alternative way of communication for people with severe disabilities. One of the shortcomings in current BCI systems, recently put forward in the fourth BCI competition, is the asynchronous detection of motor imagery versus resting state. We investigated this extension to the three-class case, in which the resting state is considered virtually lying between two motor classes, resulting in a large penalty when one motor task is misclassified into the other motor class. We particularly focus on the behavior of different machine-learning techniques and on the role of multi-class cost-sensitive learning in such a context. To this end, four different kernel methods are empirically compared, namely pairwise multi-class support vector machines (SVMs), two cost-sensitive multi-class SVMs and kernel-based ordinal regression. The experimental results illustrate that ordinal regression performs better than the other three approaches when a cost-sensitive performance measure such as the mean-squared error is considered. By contrast, multi-class cost-sensitive learning enables us to control the number of large errors made between two motor tasks.

  3. Target-directed motor imagery of the lower limb enhances event-related desynchronization.

    Directory of Open Access Journals (Sweden)

    Kosuke Kitahara

    Full Text Available Event-related desynchronization/synchronization (ERD/S is an electroencephalogram (EEG feature widely used as control signals for Brain-Computer Interfaces (BCIs. Nevertheless, the underlying neural mechanisms and functions of ERD/S are largely unknown, thus investigating them is crucial to improve the reliability of ERD/S-based BCIs. This study aimed to identify Motor Imagery (MI conditions that enhance ERD/S. We investigated following three questions: 1 whether target-directed MI affects ERD/S, 2 whether MI with sound imagery affects ERD/S, and 3 whether ERD/S has a body part dependency of MI. Nine participants took part in the experiments of four MI conditions; they were asked to imagine right foot dorsiflexion (F, right foot dorsiflexion and the sound of a bass drum when the sole touched the floor (FS, right leg extension (L, and right leg extension directed toward a soccer ball (LT. Statistical comparison revealed that there were significant differences between conditions L and LT in beta-band ERD and conditions F and L in beta-band ERS. These results suggest that mental rehearsal of target-directed lower limb movement without real sensory stimuli can enhance beta-band ERD; furthermore, MI of foot dorsiflexion induces significantly larger beta-band ERS than that of leg extension. These findings could be exploited for the training of BCIs such as powered prosthetics for disabled person and neurorehabilitation system for stroke patients.

  4. Improving the accessibility at home: implementation of a domotic application using a p300-based brain computer interface system

    Directory of Open Access Journals (Sweden)

    Rebeca Corralejo Palacios

    2012-05-01

    Full Text Available The aim of this study was to develop a Brain Computer Interface (BCI application to control domotic devices usually present at home. Previous studies have shown that people with severe disabilities, both physical and cognitive ones, do not achieve high accuracy results using motor imagery-based BCIs. To overcome this limitation, we propose the implementation of a BCI application using P300 evoked potentials, because neither extensive training nor extremely high concentration level are required for this kind of BCIs. The implemented BCI application allows to control several devices as TV, DVD player, mini Hi-Fi system, multimedia hard drive, telephone, heater, fan and lights. Our aim is that potential users, i.e. people with severe disabilities, are able to achieve high accuracy. Therefore, this domotic BCI application is useful to increase

  5. Action observation and motor imagery for rehabilitation in Parkinson's disease: A systematic review and an integrative hypothesis.

    Science.gov (United States)

    Caligiore, Daniele; Mustile, Magda; Spalletta, Gianfranco; Baldassarre, Gianluca

    2017-01-01

    This article discusses recent evidence supporting the use of action observation therapy and motor imagery practice for rehabilitation of Parkinson's disease. A main question that emerges from the review regards the different effectiveness of these approaches and the possibility of integrating them into a single method to enhance motor behaviour in subjects with Parkinson's disease. In particular, the reviewed studies suggest that action observation therapy can have a positive effect on motor facilitation of patients and that a long-term rehabilitation program based on action observation therapy or motor imagery practice can bring some benefit on their motor recovery. Moreover, the paper discusses how the research on the combined use of action observation and motor imagery for motor improvements in healthy subjects may encourage the combined use of action observation therapy and motor imagery practice for therapeutic aims in Parkinson's disease. To date, this hypothesis has never been experimented. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  6. Pure visual imagery as a potential approach to achieve three classes of control for implementation of BCI in non-motor disorders

    Science.gov (United States)

    Sousa, Teresa; Amaral, Carlos; Andrade, João; Pires, Gabriel; Nunes, Urbano J.; Castelo-Branco, Miguel

    2017-08-01

    Objective. The achievement of multiple instances of control with the same type of mental strategy represents a way to improve flexibility of brain-computer interface (BCI) systems. Here we test the hypothesis that pure visual motion imagery of an external actuator can be used as a tool to achieve three classes of electroencephalographic (EEG) based control, which might be useful in attention disorders. Approach. We hypothesize that different numbers of imagined motion alternations lead to distinctive signals, as predicted by distinct motion patterns. Accordingly, a distinct number of alternating sensory/perceptual signals would lead to distinct neural responses as previously demonstrated using functional magnetic resonance imaging (fMRI). We anticipate that differential modulations should also be observed in the EEG domain. EEG recordings were obtained from twelve participants using three imagery tasks: imagery of a static dot, imagery of a dot with two opposing motions in the vertical axis (two motion directions) and imagery of a dot with four opposing motions in vertical or horizontal axes (four directions). The data were analysed offline. Main results. An increase of alpha-band power was found in frontal and central channels as a result of visual motion imagery tasks when compared with static dot imagery, in contrast with the expected posterior alpha decreases found during simple visual stimulation. The successful classification and discrimination between the three imagery tasks confirmed that three different classes of control based on visual motion imagery can be achieved. The classification approach was based on a support vector machine (SVM) and on the alpha-band relative spectral power of a small group of six frontal and central channels. Patterns of alpha activity, as captured by single-trial SVM closely reflected imagery properties, in particular the number of imagined motion alternations. Significance. We found a new mental task based on visual motion

  7. Brain-computer interfaces in neurological rehabilitation.

    Science.gov (United States)

    Daly, Janis J; Wolpaw, Jonathan R

    2008-11-01

    Recent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain-computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more effective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the efficacy of a rehabilitation protocol and thus improve muscle control for the patient.

  8. Body-specific motor imagery of hand actions: neural evidence from right- and left-handers

    Directory of Open Access Journals (Sweden)

    Roel M Willems

    2009-11-01

    Full Text Available If motor imagery uses neural structures involved in action execution, then the neural correlates of imagining an action should differ between individuals who tend to execute the action differently. Here we report fMRI data showing that motor imagery is influenced by the way people habitually perform motor actions with their particular bodies; that is, motor imagery is ‘body-specific’ (Casasanto, 2009. During mental imagery for complex hand actions, activation of cortical areas involved in motor planning and execution was left-lateralized in right-handers but right-lateralized in left-handers. We conclude that motor imagery involves the generation of an action plan that is grounded in the participant’s motor habits, not just an abstract representation at the level of the action’s goal. People with different patterns of motor experience form correspondingly different neurocognitive representations of imagined actions.

  9. User-customized brain computer interfaces using Bayesian optimization

    Science.gov (United States)

    Bashashati, Hossein; Ward, Rabab K.; Bashashati, Ali

    2016-04-01

    Objective. The brain characteristics of different people are not the same. Brain computer interfaces (BCIs) should thus be customized for each individual person. In motor-imagery based synchronous BCIs, a number of parameters (referred to as hyper-parameters) including the EEG frequency bands, the channels and the time intervals from which the features are extracted should be pre-determined based on each subject’s brain characteristics. Approach. To determine the hyper-parameter values, previous work has relied on manual or semi-automatic methods that are not applicable to high-dimensional search spaces. In this paper, we propose a fully automatic, scalable and computationally inexpensive algorithm that uses Bayesian optimization to tune these hyper-parameters. We then build different classifiers trained on the sets of hyper-parameter values proposed by the Bayesian optimization. A final classifier aggregates the results of the different classifiers. Main Results. We have applied our method to 21 subjects from three BCI competition datasets. We have conducted rigorous statistical tests, and have shown the positive impact of hyper-parameter optimization in improving the accuracy of BCIs. Furthermore, We have compared our results to those reported in the literature. Significance. Unlike the best reported results in the literature, which are based on more sophisticated feature extraction and classification methods, and rely on prestudies to determine the hyper-parameter values, our method has the advantage of being fully automated, uses less sophisticated feature extraction and classification methods, and yields similar or superior results compared to the best performing designs in the literature.

  10. An embedded implementation based on adaptive filter bank for brain-computer interface systems.

    Science.gov (United States)

    Belwafi, Kais; Romain, Olivier; Gannouni, Sofien; Ghaffari, Fakhreddine; Djemal, Ridha; Ouni, Bouraoui

    2018-07-15

    Brain-computer interface (BCI) is a new communication pathway for users with neurological deficiencies. The implementation of a BCI system requires complex electroencephalography (EEG) signal processing including filtering, feature extraction and classification algorithms. Most of current BCI systems are implemented on personal computers. Therefore, there is a great interest in implementing BCI on embedded platforms to meet system specifications in terms of time response, cost effectiveness, power consumption, and accuracy. This article presents an embedded-BCI (EBCI) system based on a Stratix-IV field programmable gate array. The proposed system relays on the weighted overlap-add (WOLA) algorithm to perform dynamic filtering of EEG-signals by analyzing the event-related desynchronization/synchronization (ERD/ERS). The EEG-signals are classified, using the linear discriminant analysis algorithm, based on their spatial features. The proposed system performs fast classification within a time delay of 0.430 s/trial, achieving an average accuracy of 76.80% according to an offline approach and 80.25% using our own recording. The estimated power consumption of the prototype is approximately 0.7 W. Results show that the proposed EBCI system reduces the overall classification error rate for the three datasets of the BCI-competition by 5% compared to other similar implementations. Moreover, experiment shows that the proposed system maintains a high accuracy rate with a short processing time, a low power consumption, and a low cost. Performing dynamic filtering of EEG-signals using WOLA increases the recognition rate of ERD/ERS patterns of motor imagery brain activity. This approach allows to develop a complete prototype of a EBCI system that achieves excellent accuracy rates. Copyright © 2018 Elsevier B.V. All rights reserved.

  11. Monitoring Local Regional Hemodynamic Signal Changes during Motor Execution and Motor Imagery Using Near-Infrared Spectroscopy

    Directory of Open Access Journals (Sweden)

    Naoki eIso

    2016-01-01

    Full Text Available The aim of this study was to clarify the topographical localization of motor-related regional hemodynamic signal changes during motor execution (ME and motor imagery (MI by using near-infrared spectroscopy (NIRS, as this technique is more clinically expedient than established methods (e.g. fMRI. Twenty right-handed healthy subjects participated in this study. The experimental protocol was a blocked design consisting of 3 cycles of 20 s of task performance and 30 s of rest. The tapping sequence task was performed with their fingers under 4 conditions: ME and MI with the right or left hand. Hemodynamic brain activity was measured with NIRS to monitor changes in oxygenated hemoglobin (oxy-Hb concentration. Oxy-Hb in the somatosensory motor cortex (SMC increased significantly only during contralateral ME and showed a significant interaction between task and hand. There was a main effect of hand in the left SMC. Although there were no significant main effects or interactions in the supplemental motor area (SMA and premotor area (PMA, oxy-Hb increased substantially under all conditions. These results clarified the topographical localization by motor-related regional hemodynamic signal changes during ME and MI by using NIRS.

  12. Training to use a commercial brain-computer interface as access technology: a case study.

    Science.gov (United States)

    Taherian, Sarvnaz; Selitskiy, Dmitry; Pau, James; Davies, T Claire; Owens, R Glynn

    2016-01-01

    This case study describes how an individual with spastic quadriplegic cerebral palsy was trained over a period of four weeks to use a commercial electroencephalography (EEG)-based brain-computer interface (BCI). The participant spent three sessions exploring the system, and seven sessions playing a game focused on EEG feedback training of left and right arm motor imagery and a customised, training game paradigm was employed. The participant showed improvement in the production of two distinct EEG patterns. The participant's performance was influenced by motivation, fatigue and concentration. Six weeks post-training the participant could still control the BCI and used this to type a sentence using an augmentative and alternative communication application on a wirelessly linked device. The results from this case study highlight the importance of creating a dynamic, relevant and engaging training environment for BCIs. Implications for Rehabilitation Customising a training paradigm to suit the users' interests can influence adherence to assistive technology training. Mood, fatigue, physical illness and motivation influence the usability of a brain-computer interface. Commercial brain-computer interfaces, which require little set up time, may be used as access technology for individuals with severe disabilities.

  13. Index finger motor imagery EEG pattern recognition in BCI applications using dictionary cleaned sparse representation-based classification for healthy people

    Science.gov (United States)

    Miao, Minmin; Zeng, Hong; Wang, Aimin; Zhao, Fengkui; Liu, Feixiang

    2017-09-01

    Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface (BCI) has shown its effectiveness for the control of rehabilitation devices designed for large body parts of the patients with neurologic impairments. In order to validate the feasibility of using EEG to decode the MI of a single index finger and constructing a BCI-enhanced finger rehabilitation system, we collected EEG data during right hand index finger MI and rest state for five healthy subjects and proposed a pattern recognition approach for classifying these two mental states. First, Fisher's linear discriminant criteria and power spectral density analysis were used to analyze the event-related desynchronization patterns. Second, both band power and approximate entropy were extracted as features. Third, aiming to eliminate the abnormal samples in the dictionary and improve the classification performance of the conventional sparse representation-based classification (SRC) method, we proposed a novel dictionary cleaned sparse representation-based classification (DCSRC) method for final classification. The experimental results show that the proposed DCSRC method gives better classification accuracies than SRC and an average classification accuracy of 81.32% is obtained for five subjects. Thus, it is demonstrated that single right hand index finger MI can be decoded from the sensorimotor rhythms, and the feature patterns of index finger MI and rest state can be well recognized for robotic exoskeleton initiation.

  14. Motor imagery training improves precision of an upper limb movement in patients with hemiparesis.

    Science.gov (United States)

    Grabherr, Luzia; Jola, Corinne; Berra, Gilberto; Theiler, Robert; Mast, Fred W

    2015-01-01

    In healthy participants, beneficial effects of motor imagery training on movement execution have been shown for precision, strength, and speed. In the clinical context, it is still debated whether motor imagery provides an effective rehabilitation technique in patients with motor deficits. To compare the effectiveness of two different types of movement training: motor imagery vs. motor execution. Twenty-five patients with hemiparesis were assigned to one of two training groups: the imagery or the execution-training group. Both groups completed a baseline test before they received six training sessions, each of which was followed by a test session. Using a novel and precisely quantifiable test, we assessed how accurately patients performed an upper limb movement. Both training groups improved performance over the six test sessions but the improvement was significantly larger in the imagery group. That is, the imagery group was able to perform more precise movements than the execution group after the sixth training session while there was no difference at the beginning of the training. The results provide evidence for the benefit of motor imagery training in patients with hemiparesis and thus suggest the integration of cognitive training in conventional physiotherapy practice.

  15. Dynamic Neuro-Cognitive Imagery Improves Mental Imagery Ability, Disease Severity, and Motor and Cognitive Functions in People with Parkinson’s Disease

    Directory of Open Access Journals (Sweden)

    Amit Abraham

    2018-01-01

    Full Text Available People with Parkinson’s disease (PD experience kinesthetic deficits, which affect motor and nonmotor functions, including mental imagery. Imagery training is a recommended, yet underresearched, approach in PD rehabilitation. Dynamic Neuro-Cognitive Imagery (DNI™ is a codified method for imagery training. Twenty subjects with idiopathic PD (Hoehn and Yahr stages I–III were randomly allocated into DNI training (experimental; n=10 or in-home learning and exercise program (control; n=10. Both groups completed at least 16 hours of training within two weeks. DNI training focused on anatomical embodiment and kinesthetic awareness. Imagery abilities, disease severity, and motor and nonmotor functions were assessed pre- and postintervention. The DNI participants improved (p<.05 in mental imagery abilities, disease severity, and motor and spatial cognitive functions. Participants also reported improvements in balance, walking, mood, and coordination, and they were more physically active. Both groups strongly agreed they enjoyed their program and were more mentally active. DNI training is a promising rehabilitation method for improving imagery ability, disease severity, and motor and nonmotor functions in people with PD. This training might serve as a complementary PD therapeutic approach. Future studies should explore the effect of DNI on motor learning and control strategies.

  16. Enhancement of motor-imagery ability via combined action observation and motor-imagery training with proprioceptive neurofeedback.

    Science.gov (United States)

    Ono, Yumie; Wada, Kenya; Kurata, Masaya; Seki, Naoto

    2018-04-23

    Varied individual ability to control the sensory-motor rhythms may limit the potential use of motor-imagery (MI) in neurorehabilitation and neuroprosthetics. We employed neurofeedback training of MI under action observation (AO: AOMI) with proprioceptive feedback and examined whether it could enhance MI-induced event-related desynchronization (ERD). Twenty-eight healthy young adults participated in the neurofeedback training. They performed MI while watching a video of hand-squeezing motion from a first-person perspective. Eleven participants received correct proprioceptive feedback of the same hand motion with the video, via an exoskeleton robot attached to their hand, upon their successful generation of ERD. Another nine participants received random feedback. The training lasted for approximately 20 min per day and continued for 6 days within an interval of 2 weeks. MI-ERD power was evaluated separately, without AO, on each experimental day. The MI-ERD power of the participants receiving correct feedback, as opposed to random feedback, was significantly increased after training. An additional experiment in which the remaining eight participants were trained with auditory instead of proprioceptive feedback failed to show statistically significant increase in MI-ERD power. The significant training effect obtained in shorter training time relative to previously proposed methods suggests the superiority of AOMI training and physiologically-congruent proprioceptive feedback to enhance the MI-ERD power. The proposed neurofeedback training could help patients with motor deficits to attain better use of brain-machine interfaces for rehabilitation and/or prosthesis. Copyright © 2018 Elsevier Ltd. All rights reserved.

  17. Assessment of motor imagery ability and training

    Directory of Open Access Journals (Sweden)

    André Luiz Felix Rodacki

    2010-09-01

    Full Text Available The aim of this study was to evaluate changes in motor imagery ability in response to a specific dart throwing training. Twelve subjects (17-22 years with no previous experience in dart throwing or imagery agreed to participate. Changes in imagery ability were assessed using the Sports Imagery Questionnaire before (pretreatment and after (post-treatment an imagery training program consisting of 10 sessions. Retention (RET was assessed 2 weeks after training. The program included mental exercises designed to develop vivid images, to control one’s own images, and to increase perception about performance. Comparison of the imagery training conditions (training alone, training accompanied, observing a colleague, and during assessment showed no differences between the pretreatment, post-treatment and RET evaluations. Although imagery ability did not respond to training, significant differences between imagery domains (visual, auditory, kinesthetic, and animic were found (p<0.05, except between the visual and animic domains (p=0.58. These differences might be related to subject’s domain preference subject during the imagery process and to the nature of the task in which the skill technique used seems to be a relevant aspect.

  18. Brain Computer Interface: Assessment of Spinal Cord Injury Patient towards Motor Movement through EEG application

    Directory of Open Access Journals (Sweden)

    Syam Syahrull Hi-Fi

    2017-01-01

    Full Text Available Electroencephalography (EEG associated with motor task have been comprehensively investigated and it can also describe the brain activities while spinal cord injury (SCI patient with para/tetraplegia performing movement with their limbs. This paper reviews on conducted research regarding application of brain computer interface (BCI that offer alternative for neural impairments community such as spinal cord injury patient (SCI which include the experimental design, signal analysis of EEG band signal and data processing methods. The findings claim that the EEG signals of SCI patients associated with movement tasks can be stimulated through mental and motor task. Other than that EEG signal component such as alpha and beta frequency bands indicate significance for analysing the brain activity of subjects with SCI during movements.

  19. A standardized motor imagery introduction program (MIIP for neuro-rehabilitation: development and evaluation

    Directory of Open Access Journals (Sweden)

    Christine eWondrusch

    2013-08-01

    Full Text Available Background: For patients with central nervous system lesions and sensorimotor impairments a solid motor imagery (MI introduction is crucial to understand and use MI to improve motor performance. The study’s aim was to develop and evaluate a standardized MI group introduction program (MIIP for patients after stroke, multiple sclerosis, Parkinson’s disease, and traumatic brain injury. Methods:Phase 1: Based on literature a MIIP was developed comprising MI theory (definition, type, mode, perspective, planning and MI practice (performance, control. Phase 2: Development of a 27-item self-administered MIIP evaluation questionnaire, assessing MI knowledge self-evaluation of the ability to perform MI and patient satisfaction with the MIIP. Phase 3: Evaluation of MIIP and MI questionnaire by 2 independent MI experts based on predefined criteria and 2 patients using semi-structured interviews.Phase 4: Case series with a pre-post design to evaluate MIIP (3x30minutes using the MI questionnaire, Imaprax, Kinaesthetic and Visual Imagery Questionnaire, and Mental Chronometry. The paired t-test and the Wilcoxon signed-rank test were used to determine significant changes.Results: Data of eleven patients were analysed (5 females; age 62.3±14.1 years. Declarative MI knowledge improved significantly from 5.4±2.2 to 8.8±2.9 (p=0.010. Patients demonstrated good satisfaction with MIIP (mean satisfaction score: 83.2±11.4 %. MI ability remained on a high level but showed no significant change, except a significant decrease in the Kinaesthetic and Visual Imagery Questionnaire score.Conclusion: The presented MIIP seems to be valid and feasible for patients with central nervous system lesions and sensorimotor impairments resulting in improved MI knowledge. MIIP sessions can be held in groups of four or less. MI ability and Mental Chronometry remained unchanged after 3 training sessions.

  20. A standardized motor imagery introduction program (MIIP) for neuro-rehabilitation: development and evaluation.

    Science.gov (United States)

    Wondrusch, C; Schuster-Amft, C

    2013-01-01

    For patients with central nervous system (CNS) lesions and sensorimotor impairments a solid motor imagery (MI) introduction is crucial to understand and use MI to improve motor performance. The study's aim was to develop and evaluate a standardized MI group introduction program (MIIP) for patients after stroke, multiple sclerosis (MS), Parkinson's disease (PD), and traumatic brain injury (TBI). Phase 1: Based on literature a MIIP was developed comprising MI theory (definition, type, mode, perspective, planning) and MI practice (performance, control). Phase 2: Development of a 27-item self-administered MIIP evaluation questionnaire, assessing MI knowledge self-evaluation of the ability to perform MI and patient satisfaction with the MIIP. Phase 3: Evaluation of MIIP and MI questionnaire by 2 independent MI experts based on predefined criteria and 2 patients using semi-structured interviews. Phase 4: Case series with a pre-post design to evaluate MIIP (3 × 30 min) using the MI questionnaire, Imaprax, Kinaesthetic and Visual Imagery Questionnaire, and Mental Chronometry. The paired t-test and the Wilcoxon signed-rank test were used to determine significant changes. Data of eleven patients were analysed (5 females; age 62.3 ± 14.1 years). Declarative MI knowledge improved significantly from 5.4 ± 2.2 to 8.8 ± 2.9 (p = 0.010). Patients demonstrated good satisfaction with MIIP (mean satisfaction score: 83.2 ± 11.4%). MI ability remained on a high level but showed no significant change, except a significant decrease in the Kinaesthetic and Visual Imagery Questionnaire score. The presented MIIP seems to be valid and feasible for patients with CNS lesions and sensorimotor impairments resulting in improved MI knowledge. MIIP sessions can be held in groups of four or less. MI ability and Mental Chronometry remained unchanged after 3 training sessions.

  1. Motor imagery group practice for gait rehabilitation in individuals with post-stroke hemiparesis: a pilot study.

    Science.gov (United States)

    Dickstein, Ruth; Levy, Sandra; Shefi, Sara; Holtzman, Sarit; Peleg, Sara; Vatine, Jean-Jacques

    2014-01-01

    Stroke is the leading cause of adult disability, with walking impairment being a devastating indicator of chronic post-stroke hemiparesis. Limited resources exist for individual treatments; therefore, the delivery of safe group exercise therapy is highly desired. To examine whether the application of group-based motor imagery practice to community-dwelling individuals with chronic hemiparesis improves gait. Sixteen individuals with chronic hemiparesis from two community centers participated in the study, with eight from each center. Four participants in each center received five weeks of the experimental intervention, consisting of group-based motor imagery exercises of gait tasks, followed by five weeks of control treatment of motor imagery exercises for the affected upper extremity. Four other subjects in each center received the same treatments in reverse order. Pre- and post intervention measurements included clinical and biomechanical gait parameters. Comparisons within (pre- vs. post) and between treatments (experimental vs. control) indicated no significant change in any gait variable. Nevertheless, the verbal reports of most participants alluded to satisfaction with the experimental intervention and to an increase in self-confidence. Despite the lack of evidence for the effectiveness of group-based motor imagery practice in improving gait among individuals with chronic hemiparesis, the contrast between the measured outcomes and the positive verbal reports merits further inquiry.

  2. The correlation between motor impairments and event-related desynchronization during motor imagery in ALS patients

    Directory of Open Access Journals (Sweden)

    Kasahara Takashi

    2012-06-01

    Full Text Available Abstract Background The event-related desynchronization (ERD in EEG is known to appear during motor imagery, and is thought to reflect cortical processing for motor preparation. The aim of this study is to examine the modulation of ERD with motor impairment in ALS patients. ERD during hand motor imagery was obtained from 8 ALS patients with a variety of motor impairments. ERD was also obtained from age-matched 11 healthy control subjects with the same motor task. The magnitude and frequency of ERD were compared between groups for characterization of ALS specific changes. Results The ERD of ALS patients were significantly smaller than those of control subjects. Bulbar function and ERD were negatively correlated in ALS patients. Motor function of the upper extremities did was uncorrelated with ERD. Conclusions ALS patients with worsened bulbar scales may show smaller ERD. Motor function of the upper extremities did was uncorrelated with ERD.

  3. Efficacy of motor imagery in post-stroke rehabilitation: a systematic review

    Directory of Open Access Journals (Sweden)

    Puhan Milo A

    2008-03-01

    Full Text Available Abstract Background Evaluation of how Motor Imagery and conventional therapy (physiotherapy or occupational therapy compare to conventional therapy only in their effects on clinically relevant outcomes during rehabilitation of persons with stroke. Design Systematic review of the literature Methods We conducted an electronic database search in seven databases in August 2005 and also hand-searched the bibliographies of studies that we selected for the review. Two reviewers independently screened and selected all randomized controlled trials that compare the effects of conventional therapy plus Motor Imagery to those of only conventional therapy on stroke patients. The outcome measurements were: Fugl-Meyer Stroke Assessment upper extremity score (66 points and Action Research Arm Test upper extremity score (57 points. Due to the high variability in the outcomes, we could not pool the data statistically. Results We identified four randomized controlled trials from Asia and North America. The quality of the included studies was poor to moderate. Two different Motor imagery techniques were used (three studies used audiotapes and one study had occupational therapists apply the intervention. Two studies found significant effects of Motor Imagery in the Fugl-Meyer Stroke Assessment: Differences between groups amounted to 11.0 (1.0 to 21.0 and 3.2 (-4 to 10.3 respectively and in the Action Research Arm Test 6.1 (-6.2 to 18.4 and 15.8 (0.5 to 31.0 respectively. One study did not find a significant effect in the Fugl-Meyer Stroke Assessment and Color trail Test (p = 0.28 but in the task-related outcomes (p > 0.001. Conclusion Current evidence suggests that Motor imagery provides additional benefits to conventional physiotherapy or occupational therapy. However, larger and methodologically sounder studies should be conducted to assess the benefits of Motor imagery.

  4. To what extent does motor imagery resemble motor preparation?

    NARCIS (Netherlands)

    van der Lubbe, Rob; Sobierajewicz, Jagna; Jongsma, Marijtje; Przekoracka-Krawczyk, Anna

    2017-01-01

    Motor imagery may be defined as the generation of an image of the acting self that lacks the final execution of a movement. This image is thought to be a simulation of the intended action from a first-person perspective. Recent studies with a Go/NoGo version of the discrete sequence production

  5. Corticospinal excitability during observation and imagery of simple and complex hand tasks : Implications for motor rehabilitation

    NARCIS (Netherlands)

    Roosink, Meyke; Zijdewind, Inge

    2010-01-01

    Movement observation and imagery are increasingly propagandized for motor rehabilitation. Both observation and imagery are thought to improve motor function through repeated activation of mental motor representations. However, it is unknown what stimulation parameters or imagery conditions are

  6. Effects of hand orientation on motor imagery: Event related potentials suggest kinesthetic motor imagery to solve the hand laterality judgment task

    NARCIS (Netherlands)

    Jongsma, M.L.A.; Meulenbroek, R.G.J.; Okely, J.; Baas, C.M.; Lubbe, R.H.J. van der; Steenbergen, B.

    2013-01-01

    Motor imagery (MI) refers to the process of imagining the execution of a specific motor action without actually producing an overt movement. Two forms of MI have been distinguished: visual MI and kinesthetic MI. To distinguish between these forms of MI we employed an event related potential (ERP)

  7. Effects of hand orientation on motor imagery--event related potentials suggest kinesthetic motor imagery to solve the hand laterality judgment task.

    NARCIS (Netherlands)

    Jongsma, M.L.A.; Meulenbroek, R.G.; Okely, J.; Baas, C.M.; Lubbe, R.H. van der; Steenbergen, B.

    2013-01-01

    Motor imagery (MI) refers to the process of imagining the execution of a specific motor action without actually producing an overt movement. Two forms of MI have been distinguished: visual MI and kinesthetic MI. To distinguish between these forms of MI we employed an event related potential (ERP)

  8. Effects of hand orientation on motor imagery - event related potentials suggest kinesthetic motor imagery to solve the hand laterality judgment task

    NARCIS (Netherlands)

    Jongsma, M.A.; Meulenbroek, R.G.J.; Okely, J.; Baas, M.; Baas, M.; van der Lubbe, Robert Henricus Johannes; Steenbergen, B.

    2013-01-01

    Motor imagery (MI) refers to the process of imagining the execution of a specific motor action without actually producing an overt movement. Two forms of MI have been distinguished: visual MI and kinesthetic MI. To distinguish between these forms of MI we employed an event related potential (ERP)

  9. How Kinesthetic Motor Imagery works: A predictive-processing theory of visualization in sports and motor expertise

    NARCIS (Netherlands)

    Ridderinkhof, K.R.; Brass, M.

    2015-01-01

    Kinesthetic Motor Imagery (KMI) is an important technique to acquire and refine motor skills. KMI is widely used by professional athletes as an effective way to improve motor performance without overt motor output. Despite this obvious relevance, the functional mechanisms and neural circuits

  10. Effects of Soft Drinks on Resting State EEG and Brain-Computer Interface Performance.

    Science.gov (United States)

    Meng, Jianjun; Mundahl, John; Streitz, Taylor; Maile, Kaitlin; Gulachek, Nicholas; He, Jeffrey; He, Bin

    2017-01-01

    Motor imagery-based (MI based) brain-computer interface (BCI) using electroencephalography (EEG) allows users to directly control a computer or external device by modulating and decoding the brain waves. A variety of factors could potentially affect the performance of BCI such as the health status of subjects or the environment. In this study, we investigated the effects of soft drinks and regular coffee on EEG signals under resting state and on the performance of MI based BCI. Twenty-six healthy human subjects participated in three or four BCI sessions with a resting period in each session. During each session, the subjects drank an unlabeled soft drink with either sugar (Caffeine Free Coca-Cola), caffeine (Diet Coke), neither ingredient (Caffeine Free Diet Coke), or a regular coffee if there was a fourth session. The resting state spectral power in each condition was compared; the analysis showed that power in alpha and beta band after caffeine consumption were decreased substantially compared to control and sugar condition. Although the attenuation of powers in the frequency range used for the online BCI control signal was shown, group averaged BCI online performance after consuming caffeine was similar to those of other conditions. This work, for the first time, shows the effect of caffeine, sugar intake on the online BCI performance and resting state brain signal.

  11. Action observation versus motor imagery in learning a complex motor task: a short review of literature and a kinematics study.

    Science.gov (United States)

    Gatti, R; Tettamanti, A; Gough, P M; Riboldi, E; Marinoni, L; Buccino, G

    2013-04-12

    Both motor imagery and action observation have been shown to play a role in learning or re-learning complex motor tasks. According to a well accepted view they share a common neurophysiological basis in the mirror neuron system. Neurons within this system discharge when individuals perform a specific action and when they look at another individual performing the same or a motorically related action. In the present paper, after a short review of literature on the role of action observation and motor imagery in motor learning, we report the results of a kinematics study where we directly compared motor imagery and action observation in learning a novel complex motor task. This involved movement of the right hand and foot in the same angular direction (in-phase movement), while at the same time moving the left hand and foot in an opposite angular direction (anti-phase movement), all at a frequency of 1Hz. Motor learning was assessed through kinematics recording of wrists and ankles. The results showed that action observation is better than motor imagery as a strategy for learning a novel complex motor task, at least in the fast early phase of motor learning. We forward that these results may have important implications in educational activities, sport training and neurorehabilitation. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  12. Conscious brain-to-brain communication in humans using non-invasive technologies.

    Science.gov (United States)

    Grau, Carles; Ginhoux, Romuald; Riera, Alejandro; Nguyen, Thanh Lam; Chauvat, Hubert; Berg, Michel; Amengual, Julià L; Pascual-Leone, Alvaro; Ruffini, Giulio

    2014-01-01

    Human sensory and motor systems provide the natural means for the exchange of information between individuals, and, hence, the basis for human civilization. The recent development of brain-computer interfaces (BCI) has provided an important element for the creation of brain-to-brain communication systems, and precise brain stimulation techniques are now available for the realization of non-invasive computer-brain interfaces (CBI). These technologies, BCI and CBI, can be combined to realize the vision of non-invasive, computer-mediated brain-to-brain (B2B) communication between subjects (hyperinteraction). Here we demonstrate the conscious transmission of information between human brains through the intact scalp and without intervention of motor or peripheral sensory systems. Pseudo-random binary streams encoding words were transmitted between the minds of emitter and receiver subjects separated by great distances, representing the realization of the first human brain-to-brain interface. In a series of experiments, we established internet-mediated B2B communication by combining a BCI based on voluntary motor imagery-controlled electroencephalographic (EEG) changes with a CBI inducing the conscious perception of phosphenes (light flashes) through neuronavigated, robotized transcranial magnetic stimulation (TMS), with special care taken to block sensory (tactile, visual or auditory) cues. Our results provide a critical proof-of-principle demonstration for the development of conscious B2B communication technologies. More fully developed, related implementations will open new research venues in cognitive, social and clinical neuroscience and the scientific study of consciousness. We envision that hyperinteraction technologies will eventually have a profound impact on the social structure of our civilization and raise important ethical issues.

  13. Conscious brain-to-brain communication in humans using non-invasive technologies.

    Directory of Open Access Journals (Sweden)

    Carles Grau

    Full Text Available Human sensory and motor systems provide the natural means for the exchange of information between individuals, and, hence, the basis for human civilization. The recent development of brain-computer interfaces (BCI has provided an important element for the creation of brain-to-brain communication systems, and precise brain stimulation techniques are now available for the realization of non-invasive computer-brain interfaces (CBI. These technologies, BCI and CBI, can be combined to realize the vision of non-invasive, computer-mediated brain-to-brain (B2B communication between subjects (hyperinteraction. Here we demonstrate the conscious transmission of information between human brains through the intact scalp and without intervention of motor or peripheral sensory systems. Pseudo-random binary streams encoding words were transmitted between the minds of emitter and receiver subjects separated by great distances, representing the realization of the first human brain-to-brain interface. In a series of experiments, we established internet-mediated B2B communication by combining a BCI based on voluntary motor imagery-controlled electroencephalographic (EEG changes with a CBI inducing the conscious perception of phosphenes (light flashes through neuronavigated, robotized transcranial magnetic stimulation (TMS, with special care taken to block sensory (tactile, visual or auditory cues. Our results provide a critical proof-of-principle demonstration for the development of conscious B2B communication technologies. More fully developed, related implementations will open new research venues in cognitive, social and clinical neuroscience and the scientific study of consciousness. We envision that hyperinteraction technologies will eventually have a profound impact on the social structure of our civilization and raise important ethical issues.

  14. Kinesthetic imagery of musical performance.

    Science.gov (United States)

    Lotze, Martin

    2013-01-01

    Musicians use different kinds of imagery. This review focuses on kinesthetic imagery, which has been shown to be an effective complement to actively playing an instrument. However, experience in actual movement performance seems to be a requirement for a recruitment of those brain areas representing movement ideation during imagery. An internal model of movement performance might be more differentiated when training has been more intense or simply performed more often. Therefore, with respect to kinesthetic imagery, these strategies are predominantly found in professional musicians. There are a few possible reasons as to why kinesthetic imagery is used in addition to active training; one example is the need for mental rehearsal of the technically most difficult passages. Another reason for mental practice is that mental rehearsal of the piece helps to improve performance if the instrument is not available for actual training as is the case for professional musicians when they are traveling to various appearances. Overall, mental imagery in musicians is not necessarily specific to motor, somatosensory, auditory, or visual aspects of imagery, but integrates them all. In particular, the audiomotor loop is highly important, since auditory aspects are crucial for guiding motor performance. All these aspects result in a distinctive representation map for the mental imagery of musical performance. This review summarizes behavioral data, and findings from functional brain imaging studies of mental imagery of musical performance.

  15. Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays.

    Science.gov (United States)

    Hiremath, Shivayogi V; Chen, Weidong; Wang, Wei; Foldes, Stephen; Yang, Ying; Tyler-Kabara, Elizabeth C; Collinger, Jennifer L; Boninger, Michael L

    2015-01-01

    A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

  16. Parallel Alterations of Functional Connectivity during Execution and Imagination after Motor Imagery Learning

    Science.gov (United States)

    Zhang, Rushao; Hui, Mingqi; Long, Zhiying; Zhao, Xiaojie; Yao, Li

    2012-01-01

    Background Neural substrates underlying motor learning have been widely investigated with neuroimaging technologies. Investigations have illustrated the critical regions of motor learning and further revealed parallel alterations of functional activation during imagination and execution after learning. However, little is known about the functional connectivity associated with motor learning, especially motor imagery learning, although benefits from functional connectivity analysis attract more attention to the related explorations. We explored whether motor imagery (MI) and motor execution (ME) shared parallel alterations of functional connectivity after MI learning. Methodology/Principal Findings Graph theory analysis, which is widely used in functional connectivity exploration, was performed on the functional magnetic resonance imaging (fMRI) data of MI and ME tasks before and after 14 days of consecutive MI learning. The control group had no learning. Two measures, connectivity degree and interregional connectivity, were calculated and further assessed at a statistical level. Two interesting results were obtained: (1) The connectivity degree of the right posterior parietal lobe decreased in both MI and ME tasks after MI learning in the experimental group; (2) The parallel alterations of interregional connectivity related to the right posterior parietal lobe occurred in the supplementary motor area for both tasks. Conclusions/Significance These computational results may provide the following insights: (1) The establishment of motor schema through MI learning may induce the significant decrease of connectivity degree in the posterior parietal lobe; (2) The decreased interregional connectivity between the supplementary motor area and the right posterior parietal lobe in post-test implicates the dissociation between motor learning and task performing. These findings and explanations further revealed the neural substrates underpinning MI learning and supported that

  17. Motor Imagery Ability in Children with Congenital Hemiplegia: Effect of Lesion Side and Functional Level

    Science.gov (United States)

    Williams, Jacqueline; Reid, Susan M.; Reddihough, Dinah S.; Anderson, Vicki

    2011-01-01

    In addition to motor execution problems, children with hemiplegia have motor planning deficits, which may stem from poor motor imagery ability. This study aimed to provide a greater understanding of motor imagery ability in children with hemiplegia using the hand rotation task. Three groups of children, aged 8-12 years, participated: right…

  18. Application of tripolar concentric electrodes and prefeature selection algorithm for brain-computer interface.

    Science.gov (United States)

    Besio, Walter G; Cao, Hongbao; Zhou, Peng

    2008-04-01

    For persons with severe disabilities, a brain-computer interface (BCI) may be a viable means of communication. Lapalacian electroencephalogram (EEG) has been shown to improve classification in EEG recognition. In this work, the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. Two sets of left/right hand motor imagery EEG signals were acquired. An autoregressive (AR) model was developed for feature extraction with a Mahalanobis distance based linear classifier for classification. An exhaust selection algorithm was employed to analyze three factors before feature extraction. The factors analyzed were 1) length of data in each trial to be used, 2) start position of data, and 3) the order of the AR model. The results showed that tripolar concentric electrodes generated significantly higher classification accuracy than disc electrodes.

  19. Voluntary Modulation of Hemodynamic Responses in Swallowing Related Motor Areas: A Near-Infrared Spectroscopy-Based Neurofeedback Study.

    Directory of Open Access Journals (Sweden)

    Silvia Erika Kober

    Full Text Available In the present study, we show for the first time that motor imagery of swallowing, which is defined as the mental imagination of a specific motor act without overt movements by muscular activity, can be successfully used as mental strategy in a neurofeedback training paradigm. Furthermore, we demonstrate its effects on cortical correlates of swallowing function. Therefore, N = 20 healthy young adults were trained to voluntarily increase their hemodynamic response in swallowing related brain areas as assessed with near-infrared spectroscopy (NIRS. During seven training sessions, participants received either feedback of concentration changes in oxygenated hemoglobin (oxy-Hb group, N = 10 or deoxygenated hemoglobin (deoxy-Hb group, N = 10 over the inferior frontal gyrus (IFG during motor imagery of swallowing. Before and after the training, we assessed cortical activation patterns during motor execution and imagery of swallowing. The deoxy-Hb group was able to voluntarily increase deoxy-Hb over the IFG during imagery of swallowing. Furthermore, swallowing related cortical activation patterns were more pronounced during motor execution and imagery after the training compared to the pre-test, indicating cortical reorganization due to neurofeedback training. The oxy-Hb group could neither control oxy-Hb during neurofeedback training nor showed any cortical changes. Hence, successful modulation of deoxy-Hb over swallowing related brain areas led to cortical reorganization and might be useful for future treatments of swallowing dysfunction.

  20. A Link between the Increase in Electroencephalographic Coherence and Performance Improvement in Operating a Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Irma Nayeli Angulo-Sherman

    2015-01-01

    Full Text Available We study the relationship between electroencephalographic (EEG coherence and accuracy in operating a brain-computer interface (BCI. In our case, the BCI is controlled through motor imagery. Hence, a number of volunteers were trained using different training paradigms: classical visual feedback, auditory stimulation, and functional electrical stimulation (FES. After each training session, the volunteers’ accuracy in operating the BCI was assessed, and the event-related coherence (ErCoh was calculated for all possible combinations of pairs of EEG sensors. After at least four training sessions, we searched for significant differences in accuracy and ErCoh using one-way analysis of variance (ANOVA and multiple comparison tests. Our results show that there exists a high correlation between an increase in ErCoh and performance improvement, and this effect is mainly localized in the centrofrontal and centroparietal brain regions for the case of our motor imagery task. This result has a direct implication with the development of new techniques to evaluate BCI performance and the process of selecting a feedback modality that better enhances the volunteer’s capacity to operate a BCI system.

  1. Analysis of Time-Dependent Brain Network on Active and MI Tasks for Chronic Stroke Patients.

    Directory of Open Access Journals (Sweden)

    Da-Hye Kim

    Full Text Available Several researchers have analyzed brain activities by investigating brain networks. However, there is a lack of the research on the temporal characteristics of the brain network during a stroke by EEG and the comparative studies between motor execution and imagery, which became known to have similar motor functions and pathways. In this study, we proposed the possibility of temporal characteristics on the brain networks of a stroke. We analyzed the temporal properties of the brain networks for nine chronic stroke patients by the active and motor imagery tasks by EEG. High beta band has a specific role in the brain network during motor tasks. In the high beta band, for the active task, there were significant characteristics of centrality and small-worldness on bilateral primary motor cortices at the initial motor execution. The degree centrality significantly increased on the contralateral primary motor cortex, and local efficiency increased on the ipsilateral primary motor cortex. These results indicate that the ipsilateral primary motor cortex constructed a powerful subnetwork by influencing the linked channels as compensatory effect, although the contralateral primary motor cortex organized an inefficient network by using the connected channels due to lesions. For the MI task, degree centrality and local efficiency significantly decreased on the somatosensory area at the initial motor imagery. Then, there were significant correlations between the properties of brain networks and motor function on the contralateral primary motor cortex and somatosensory area for each motor execution/imagery task. Our results represented that the active and MI tasks have different mechanisms of motor acts. Based on these results, we indicated the possibility of customized rehabilitation according to different motor tasks. We expect these results to help in the construction of the customized rehabilitation system depending on motor tasks by understanding temporal

  2. Analyzing power spectral of electroencephalogram (EEG) signal to identify motoric arm movement using EMOTIV EPOC+

    Science.gov (United States)

    Bustomi, A.; Wijaya, S. K.; Prawito

    2017-07-01

    Rehabilitation of motoric dysfunction from the body becomes the main objective of developing Brain Computer Interface (BCI) technique, especially in the field of medical rehabilitation technology. BCI technology based on electrical activity of the brain, allow patient to be able to restore motoric disfunction of the body and help them to overcome the shortcomings mobility. In this study, EEG signal phenomenon was obtained from EMOTIV EPOC+, the signals were generated from the imagery of lifting arm, and look for any correlation between the imagery of motoric muscle movement against the recorded signals. The signals processing were done in the time-frequency domain, using Wavelet relative power (WRP) as feature extraction, and Support vector machine (SVM) as the classifier. In this study, it was obtained the result of maximum accuracy of 81.3 % using 8 channel (AF3, F7, F3, FC5, FC6, F4, F8, and AF4), 6 channel remaining on EMOTIV EPOC + does not contribute to the improvement of the accuracy of the classification system

  3. Enhanced inter-subject brain computer interface with associative sensorimotor oscillations.

    Science.gov (United States)

    Saha, Simanto; Ahmed, Khawza I; Mostafa, Raqibul; Khandoker, Ahsan H; Hadjileontiadis, Leontios

    2017-02-01

    Electroencephalography (EEG) captures electrophysiological signatures of cortical events from the scalp with high-dimensional electrode montages. Usually, excessive sources produce outliers and potentially affect the actual event related sources. Besides, EEG manifests inherent inter-subject variability of the brain dynamics, at the resting state and/or under the performance of task(s), caused probably due to the instantaneous fluctuation of psychophysiological states. A wavelet coherence (WC) analysis for optimally selecting associative inter-subject channels is proposed here and is being used to boost performances of motor imagery (MI)-based inter-subject brain computer interface (BCI). The underlying hypothesis is that optimally associative inter-subject channels can reduce the effects of outliers and, thus, eliminate dissimilar cortical patterns. The proposed approach has been tested on the dataset IVa from BCI competition III, including EEG data acquired from five healthy subjects who were given visual cues to perform 280 trials of MI for the right hand and right foot. Experimental results have shown increased classification accuracy (81.79%) using the WC-based selected 16 channels compared to the one (56.79%) achieved using all the available 118 channels. The associative channels lie mostly around the sensorimotor regions of the brain, reinforced by the previous literature, describing spatial brain dynamics during sensorimotor oscillations. Apparently, the proposed approach paves the way for optimised EEG channel selection that could boost further the efficiency and real-time performance of BCI systems.

  4. An online three-class Transcranial Doppler ultrasound brain computer interface.

    Science.gov (United States)

    Goyal, Anuja; Samadani, Ali-Akbar; Guerguerian, Anne-Marie; Chau, Tom

    2016-06-01

    Brain computer interfaces (BCI) can provide communication opportunities for individuals with severe motor disabilities. Transcranial Doppler ultrasound (TCD) measures cerebral blood flow velocities and can be used to develop a BCI. A previously implemented TCD BCI system used verbal and spatial tasks as control signals; however, the spatial task involved a visual cue that awkwardly diverted the user's attention away from the communication interface. Therefore, vision-independent right-lateralized tasks were investigated. Using a bilateral TCD BCI, ten participants controlled online, an on-screen keyboard using a left-lateralized task (verbal fluency), a right-lateralized task (fist motor imagery or 3D-shape tracing), and unconstrained rest. 3D-shape tracing was generally more discernible from other tasks than was fist motor imagery. Verbal fluency, 3D-shape tracing and unconstrained rest were distinguished from each other using a linear discriminant classifier, achieving a mean agreement of κ=0.43±0.17. These rates are comparable to the best offline three-class TCD BCI accuracies reported thus far. The online communication system achieved a mean information transfer rate (ITR) of 1.08±0.69bits/min with values reaching up to 2.46bits/min, thereby exceeding the ITR of previous online TCD BCIs. These findings demonstrate the potential of a three-class online TCD BCI that does not require visual task cues. Copyright © 2016 Elsevier Ireland Ltd and Japan Neuroscience Society. All rights reserved.

  5. Brain-computer interfacing under distraction: an evaluation study

    Science.gov (United States)

    Brandl, Stephanie; Frølich, Laura; Höhne, Johannes; Müller, Klaus-Robert; Samek, Wojciech

    2016-10-01

    Objective. While motor-imagery based brain-computer interfaces (BCIs) have been studied over many years by now, most of these studies have taken place in controlled lab settings. Bringing BCI technology into everyday life is still one of the main challenges in this field of research. Approach. This paper systematically investigates BCI performance under 6 types of distractions that mimic out-of-lab environments. Main results. We report results of 16 participants and show that the performance of the standard common spatial patterns (CSP) + regularized linear discriminant analysis classification pipeline drops significantly in this ‘simulated’ out-of-lab setting. We then investigate three methods for improving the performance: (1) artifact removal, (2) ensemble classification, and (3) a 2-step classification approach. While artifact removal does not enhance the BCI performance significantly, both ensemble classification and the 2-step classification combined with CSP significantly improve the performance compared to the standard procedure. Significance. Systematically analyzing out-of-lab scenarios is crucial when bringing BCI into everyday life. Algorithms must be adapted to overcome nonstationary environments in order to tackle real-world challenges.

  6. Brain Computer Interface Learning for Systems Based on Electrocorticography and Intracortical Microelectrode Arrays

    Directory of Open Access Journals (Sweden)

    Shivayogi V Hiremath

    2015-06-01

    Full Text Available A brain-computer interface (BCI system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

  7. Conventional Microscopy vs. Computer Imagery in Chiropractic Education.

    Science.gov (United States)

    Cunningham, Christine M; Larzelere, Elizabeth D; Arar, Ilija

    2008-01-01

    As human tissue pathology slides become increasingly difficult to obtain, other methods of teaching microscopy in educational laboratories must be considered. The purpose of this study was to evaluate our students' satisfaction with newly implemented computer imagery based laboratory instruction and to obtain input from their perspective on the advantages and disadvantages of computerized vs. traditional microscope laboratories. This undertaking involved the creation of a new computer laboratory. Robbins and Cotran Pathologic Basis of Disease, 7(th)ed, was chosen as the required text which gave students access to the Robbins Pathology website, including complete content of text, Interactive Case Study Companion, and Virtual Microscope. Students had experience with traditional microscopes in their histology and microbiology laboratory courses. Student satisfaction with computer based learning was assessed using a 28 question survey which was administered to three successive trimesters of pathology students (n=193) using the computer survey website Zoomerang. Answers were given on a scale of 1-5 and statistically analyzed using weighted averages. The survey data indicated that students were satisfied with computer based learning activities during pathology laboratory instruction. The most favorable aspect to computer imagery was 24-7 availability (weighted avg. 4.16), followed by clarification offered by accompanying text and captions (weighted avg. 4.08). Although advantages and disadvantages exist in using conventional microscopy and computer imagery, current pathology teaching environments warrant investigation of replacing traditional microscope exercises with computer applications. Chiropractic students supported the adoption of computer-assisted instruction in pathology laboratories.

  8. Solving a mental rotation task in congenital hemiparesis: Motor imagery versus visual imagery

    NARCIS (Netherlands)

    Steenbergen, B.; Nimwegen, M.L. van; Crajé, M.C.

    2007-01-01

    A recent study showed that motor imagery was compromised after right congenital hemiparesis. In that study, posture of the displayed stimuli and the actual posture of the hand making the response were incongruent. Ample evidence exists that such an incongruency may negatively influence laterality

  9. Higher-order Brain Areas Associated with Real-time Functional MRI Neurofeedback Training of the Somato-motor Cortex.

    Science.gov (United States)

    Auer, Tibor; Dewiputri, Wan Ilma; Frahm, Jens; Schweizer, Renate

    2018-05-15

    Neurofeedback (NFB) allows subjects to learn self-regulation of neuronal brain activation based on information about the ongoing activation. The implementation of real-time functional magnetic resonance imaging (rt-fMRI) for NFB training now facilitates the investigation into underlying processes. Our study involved 16 control and 16 training right-handed subjects, the latter performing an extensive rt-fMRI NFB training using motor imagery. A previous analysis focused on the targeted primary somato-motor cortex (SMC). The present study extends the analysis to the supplementary motor area (SMA), the next higher brain area within the hierarchy of the motor system. We also examined transfer-related functional connectivity using a whole-volume psycho-physiological interaction (PPI) analysis to reveal brain areas associated with learning. The ROI analysis of the pre- and post-training fMRI data for motor imagery without NFB (transfer) resulted in a significant training-specific increase in the SMA. It could also be shown that the contralateral SMA exhibited a larger increase than the ipsilateral SMA in the training and the transfer runs, and that the right-hand training elicited a larger increase in the transfer runs than the left-hand training. The PPI analysis revealed a training-specific increase in transfer-related functional connectivity between the left SMA and frontal areas as well as the anterior midcingulate cortex (aMCC) for right- and left-hand trainings. Moreover, the transfer success was related with training-specific increase in functional connectivity between the left SMA and the target area SMC. Our study demonstrates that NFB training increases functional connectivity with non-targeted brain areas. These are associated with the training strategy (i.e., SMA) as well as with learning the NFB skill (i.e., aMCC and frontal areas). This detailed description of both the system to be trained and the areas involved in learning can provide valuable information

  10. A Brazilian-Portuguese version of the Kinesthetic and Visual Motor Imagery Questionnaire.

    Science.gov (United States)

    Demanboro, Alan; Sterr, Annette; Anjos, Sarah Monteiro Dos; Conforto, Adriana Bastos

    2018-01-01

    Motor imagery has emerged as a potential rehabilitation tool in stroke. The goals of this study were: 1) to develop a translated and culturally-adapted Brazilian-Portugese version of the Kinesthetic and Visual Motor Imagery Questionnaire (KVIQ20-P); 2) to evaluate the psychometric characteristics of the scale in a group of patients with stroke and in an age-matched control group; 3) to compare the KVIQ20 performance between the two groups. Test-retest, inter-rater reliabilities, and internal consistencies were evaluated in 40 patients with stroke and 31 healthy participants. In the stroke group, ICC confidence intervals showed excellent test-retest and inter-rater reliabilities. Cronbach's alpha also indicated excellent internal consistency. Results for controls were comparable to those obtained in persons with stroke. The excellent psychometric properties of the KVIQ20-P should be considered during the design of studies of motor imagery interventions for stroke rehabilitation.

  11. Latent variable method for automatic adaptation to background states in motor imagery BCI

    Science.gov (United States)

    Dagaev, Nikolay; Volkova, Ksenia; Ossadtchi, Alexei

    2018-02-01

    Objective. Brain-computer interface (BCI) systems are known to be vulnerable to variabilities in background states of a user. Usually, no detailed information on these states is available even during the training stage. Thus there is a need in a method which is capable of taking background states into account in an unsupervised way. Approach. We propose a latent variable method that is based on a probabilistic model with a discrete latent variable. In order to estimate the model’s parameters, we suggest to use the expectation maximization algorithm. The proposed method is aimed at assessing characteristics of background states without any corresponding data labeling. In the context of asynchronous motor imagery paradigm, we applied this method to the real data from twelve able-bodied subjects with open/closed eyes serving as background states. Main results. We found that the latent variable method improved classification of target states compared to the baseline method (in seven of twelve subjects). In addition, we found that our method was also capable of background states recognition (in six of twelve subjects). Significance. Without any supervised information on background states, the latent variable method provides a way to improve classification in BCI by taking background states into account at the training stage and then by making decisions on target states weighted by posterior probabilities of background states at the prediction stage.

  12. Motor imagery during action observation increases eccentric hamstring force: an acute non-physical intervention.

    Science.gov (United States)

    Scott, Matthew; Taylor, Stephen; Chesterton, Paul; Vogt, Stefan; Eaves, Daniel Lloyd

    2018-06-01

    Rehabilitation professionals typically use motor imagery (MI) or action observation (AO) to increase physical strength for injury prevention and recovery. Here we compared hamstring force gains for MI during AO (AO + MI) against two pure MI training groups. Over a 3-week intervention physically fit adults imagined Nordic hamstring exercises in both legs and synchronized this with a demonstration of the same action (AO + MI), or they purely imagined this action (pure MI), or imagined upper-limb actions (pure MI-control). Eccentric hamstring strength gains were assessed using ANOVAs, and magnitude-based inference (MBI) analyses determined the likelihood of clinical/practical benefits for the interventions. Hamstring strength only increased significantly following AO + MI training. This effect was lateralized to the right leg, potentially reflecting a left-hemispheric dominance in motor simulation. MBIs: The right leg within-group treatment effect size for AO + MI was moderate and likely beneficial (d = 0.36), and only small and possibly beneficial for pure MI (0.23). Relative to pure MI-control, effects were possibly beneficial and moderate for AO + MI (0.72), although small for pure MI (0.39). Since hamstring strength predicts injury prevalence, our findings point to the advantage of combined AO + MI interventions, over and above pure MI, for injury prevention and rehabilitation. Implications for rehabilitation While hamstring strains are the most common injury across the many sports involving sprinting and jumping, Nordic hamstring exercises are among the most effective methods for building eccentric hamstring strength, for injury prevention and rehabilitation. In the acute injury phase it is crucial not to overload damaged soft tissues, and so non-physical rehabilitation techniques are well suited to this phase. Rehabilitation professionals typically use either motor imagery or action observation techniques to safely improve physical

  13. The Movement Imagery Questionnaire-Revised, Second Edition (MIQ-RS Is a Reliable and Valid Tool for Evaluating Motor Imagery in Stroke Populations

    Directory of Open Access Journals (Sweden)

    Andrew J. Butler

    2012-01-01

    Full Text Available Mental imagery can improve motor performance in stroke populations when combined with physical therapy. Valid and reliable instruments to evaluate the imagery ability of stroke survivors are needed to maximize the benefits of mental imagery therapy. The purposes of this study were to: examine and compare the test-retest intra-rate reliability of the Movement Imagery Questionnaire-Revised, Second Edition (MIQ-RS in stroke survivors and able-bodied controls, examine internal consistency of the visual and kinesthetic items of the MIQ-RS, determine if the MIQ-RS includes both the visual and kinesthetic dimensions of mental imagery, correlate impairment and motor imagery scores, and investigate the criterion validity of the MIQ-RS in stroke survivors by comparing the results to the KVIQ-10. Test-retest analysis indicated good levels of reliability (ICC range: .83–.99 and internal consistency (Cronbach α: .95–.98 of the visual and kinesthetic subscales in both groups. The two-factor structure of the MIQ-RS was supported by factor analysis, with the visual and kinesthetic components accounting for 88.6% and 83.4% of the total variance in the able-bodied and stroke groups, respectively. The MIQ-RS is a valid and reliable instrument in the stroke population examined and able-bodied populations and therefore useful as an outcome measure for motor imagery ability.

  14. Inhibition or facilitation? Modulation of corticospinal excitability during motor imagery.

    Science.gov (United States)

    Bruno, Valentina; Fossataro, Carlotta; Garbarini, Francesca

    2018-03-01

    Motor imagery (MI) is the mental simulation of an action without any overt movement. Functional evidences show that brain activity during MI and motor execution (ME) largely overlaps. However, the role of the primary motor cortex (M1) during MI is controversial. Effective connectivity techniques show a facilitation on M1 during ME and an inhibition during MI, depending on whether an action should be performed or suppressed. Conversely, Transcranial Magnetic Stimulation (TMS) studies report facilitatory effects during both ME and MI. The present TMS study shed light on MI mechanisms, by manipulating the instructions given to the participants. In both Experimental and Control groups, participants were asked to mentally simulate a finger-thumb opposition task, but only the Experimental group received the explicit instruction to avoid any unwanted fingers movements. The amplitude of motor evoked potentials (MEPs) to TMS during MI was compared between the two groups. If the M1 facilitation actually pertains to MI per se, we should have expected to find it, irrespective of the instructions. Contrariwise, we found opposite results, showing facilitatory effects (increased MEPs amplitude) in the Control group and inhibitory effects (decreased MEPs amplitude) in the Experimental group. Control experiments demonstrated that the inhibitory effect was specific for the M1 contralateral to the hand performing the MI task and that the given instructions did not compromise the subjects' MI abilities. The present findings suggest a crucial role of motor inhibition when a "pure" MI task is performed and the subjects are explicitly instructed to avoid overt movements. Copyright © 2018 Elsevier Ltd. All rights reserved.

  15. Evaluation of a modified Fitts law brain-computer interface target acquisition task in able and motor disabled individuals

    Science.gov (United States)

    Felton, E. A.; Radwin, R. G.; Wilson, J. A.; Williams, J. C.

    2009-10-01

    A brain-computer interface (BCI) is a communication system that takes recorded brain signals and translates them into real-time actions, in this case movement of a cursor on a computer screen. This work applied Fitts' law to the evaluation of performance on a target acquisition task during sensorimotor rhythm-based BCI training. Fitts' law, which has been used as a predictor of movement time in studies of human movement, was used here to determine the information transfer rate, which was based on target acquisition time and target difficulty. The information transfer rate was used to make comparisons between control modalities and subject groups on the same task. Data were analyzed from eight able-bodied and five motor disabled participants who wore an electrode cap that recorded and translated their electroencephalogram (EEG) signals into computer cursor movements. Direct comparisons were made between able-bodied and disabled subjects, and between EEG and joystick cursor control in able-bodied subjects. Fitts' law aptly described the relationship between movement time and index of difficulty for each task movement direction when evaluated separately and averaged together. This study showed that Fitts' law can be successfully applied to computer cursor movement controlled by neural signals.

  16. Affective Aspects of Perceived Loss of Control and Potential Implications for Brain-Computer Interfaces.

    Science.gov (United States)

    Grissmann, Sebastian; Zander, Thorsten O; Faller, Josef; Brönstrup, Jonas; Kelava, Augustin; Gramann, Klaus; Gerjets, Peter

    2017-01-01

    Most brain-computer interfaces (BCIs) focus on detecting single aspects of user states (e.g., motor imagery) in the electroencephalogram (EEG) in order to use these aspects as control input for external systems. This communication can be effective, but unaccounted mental processes can interfere with signals used for classification and thereby introduce changes in the signal properties which could potentially impede BCI classification performance. To improve BCI performance, we propose deploying an approach that potentially allows to describe different mental states that could influence BCI performance. To test this approach, we analyzed neural signatures of potential affective states in data collected in a paradigm where the complex user state of perceived loss of control (LOC) was induced. In this article, source localization methods were used to identify brain dynamics with source located outside but affecting the signal of interest originating from the primary motor areas, pointing to interfering processes in the brain during natural human-machine interaction. In particular, we found affective correlates which were related to perceived LOC. We conclude that additional context information about the ongoing user state might help to improve the applicability of BCIs to real-world scenarios.

  17. Affective Aspects of Perceived Loss of Control and Potential Implications for Brain-Computer Interfaces

    Directory of Open Access Journals (Sweden)

    Sebastian Grissmann

    2017-07-01

    Full Text Available Most brain-computer interfaces (BCIs focus on detecting single aspects of user states (e.g., motor imagery in the electroencephalogram (EEG in order to use these aspects as control input for external systems. This communication can be effective, but unaccounted mental processes can interfere with signals used for classification and thereby introduce changes in the signal properties which could potentially impede BCI classification performance. To improve BCI performance, we propose deploying an approach that potentially allows to describe different mental states that could influence BCI performance. To test this approach, we analyzed neural signatures of potential affective states in data collected in a paradigm where the complex user state of perceived loss of control (LOC was induced. In this article, source localization methods were used to identify brain dynamics with source located outside but affecting the signal of interest originating from the primary motor areas, pointing to interfering processes in the brain during natural human-machine interaction. In particular, we found affective correlates which were related to perceived LOC. We conclude that additional context information about the ongoing user state might help to improve the applicability of BCIs to real-world scenarios.

  18. Our actions in my mind: Motor imagery of joint action

    DEFF Research Database (Denmark)

    Vesper, Cordula; Knoblich, Günther; Sebanz, Natalie

    2014-01-01

    How do people imagine performing actions together? The present study investigated motor imagery of joint actions that requires integrating one's own and another's part of an action. In two experiments, individual participants imagined jumping alone or jointly next to an imagined partner. The joint...... condition required coordinating one's own imagined actions with an imagined partner's actions to synchronize landing times. We investigated whether the timing of participants' own imagined jumps would reflect the difference in jump distance to their imagined partner's jumps. The results showed...... of joint jumping. These findings link research on motor imagery and joint action, demonstrating that individuals are able to integrate simulations of different parts of a joint action....

  19. [Arm Motor Function Recovery during Rehabilitation with the Use of Hand Exoskeleton Controlled by Brain-Computer Interface: a Patient with Severe Brain Damage].

    Science.gov (United States)

    Biryukova, E V; Pavlova, O G; Kurganskaya, M E; Bobrov, P D; Turbina, L G; Frolov, A A; Davydov, V I; Sil'tchenko, A V; Mokienko, O A

    2016-01-01

    We studied the dynamics of motor function recovery in a patient with severe brain damage in the course of neurorehabilitation using hand exoskeleton controlled by brain-computer interface. For estimating the motor function of paretic arm, we used the biomechanical analysis of movements registered during the course of rehabilitation. After 15 weekly sessions of hand exoskeleton control, the following results were obtained: a) the velocity profile of goal-directed movements of paretic hand became bell-shaped, b) the patient began to extend and abduct the hand which was flexed and adducted in the beginning of rehabilitation, and c) the patient began to supinate the forearm which was pronated in the beginning of rehabilitation. The first result is an evidence of the general improvement of the quality of motor control, while the second and third results prove that the spasticity of paretic arm has decreased.

  20. Dual-echo ASL based assessment of motor networks: a feasibility study

    Science.gov (United States)

    Storti, Silvia Francesca; Boscolo Galazzo, Ilaria; Pizzini, Francesca B.; Menegaz, Gloria

    2018-04-01

    Objective. Dual-echo arterial spin labeling (DE-ASL) technique has been recently proposed for the simultaneous acquisition of ASL and blood-oxygenation-level-dependent (BOLD)-functional magnetic resonance imaging (fMRI) data. The assessment of this technique in detecting functional connectivity at rest or during motor and motor imagery tasks is still unexplored both per-se and in comparison with conventional methods. The purpose is to quantify the sensitivity of the DE-ASL sequence with respect to the conventional fMRI sequence (cvBOLD) in detecting brain activations, and to assess and compare the relevance of node features in decoding the network structure. Approach. Thirteen volunteers were scanned acquiring a pseudo-continuous DE-ASL sequence from which the concomitant BOLD (ccBOLD) simultaneously to the ASL can be extracted. The approach consists of two steps: (i) model-based analyses for assessing brain activations at individual and group levels, followed by statistical analysis for comparing the activation elicited by the three sequences under two conditions (motor and motor imagery), respectively; (ii) brain connectivity graph-theoretical analysis for assessing and comparing the network models properties. Main results. Our results suggest that cvBOLD and ccBOLD have comparable sensitivity in detecting the regions involved in the active task, whereas ASL offers a higher degree of co-localization with smaller activation volumes. The connectivity results and the comparative analysis of node features across sequences revealed that there are no strong changes between rest and tasks and that the differences between the sequences are limited to few connections. Significance. Considering the comparable sensitivity of the ccBOLD and cvBOLD sequences in detecting activated brain regions, the results demonstrate that DE-ASL can be successfully applied in functional studies allowing to obtain both ASL and BOLD information within a single sequence. Further, DE-ASL is a

  1. Real-time changes in corticospinal excitability related to motor imagery of a force control task

    DEFF Research Database (Denmark)

    Tatemoto, Tsuyoshi; Tsuchiya, Junko; Numata, Atsuki

    2017-01-01

    Objective To investigate real-time excitability changes in corticospinal pathways related to motor imagery in a changing force control task, using transcranial magnetic stimulation (TMS). Methods Ten healthy volunteers learnt to control the contractile force of isometric right wrist dorsiflexion...... in order to track an on-screen sine wave form. Participants performed the trained task 40 times with actual muscle contraction in order to construct the motor image. They were then instructed to execute the task without actual muscle contraction, but by imagining contraction of the right wrist...... in dorsiflexion. Motor evoked potentials (MEPs), induced by TMS in the right extensor carpi radialis muscle (ECR) and flexor carpi radialis muscle (FCR), were measured during motor imagery. MEPs were induced at five time points: prior to imagery, during the gradual generation of the imaged wrist dorsiflexion...

  2. Spatially defined disruption of motor imagery performance in people with osteoarthritis

    NARCIS (Netherlands)

    Stanton, T.R.; Lin, C.W.; Smeets, R.J.P.; Taylor, D.; Law, R.; Lorimer Moseley, G.

    2012-01-01

    OBJECTIVES: To determine whether motor imagery performance is disrupted in patients with painful knee OA and if this disruption is specific to the location of the pain. METHODS: Twenty patients with painful knee OA, 20 patients with arm pain and 20 healthy pain-free controls undertook a motor

  3. Factors of Influence on the Performance of a Short-Latency Non-Invasive Brain Switch: Evidence in Healthy Individuals and Implication for Motor Function Rehabilitation

    Directory of Open Access Journals (Sweden)

    Ren eXu

    2016-01-01

    Full Text Available Brain-computer interfacing (BCI has recently been applied as a rehabilitation approach for patients with motor disorders, such as stroke. In these closed-loop applications, a brain switch detects the motor intention from brain signals, e.g. scalp EEG, and triggers a neuroprosthetic device, either to deliver sensory feedback or to mimic real movements, thus re-establishing the compromised sensory-motor control loop and promoting neural plasticity. In this context, single trial detection of motor intention with short latency is a prerequisite. The performance of the event detection from EEG recordings is mainly determined by three factors: the type of motor imagery (e.g., repetitive, ballistic, the frequency band (or signal modality used for discrimination (e.g., alpha, beta, gamma, and MRCP, i.e. movement-related cortical potential, and the processing technique (e.g., time-series analysis, sub-band power estimation. In this study, we investigated single trial EEG traces during movement imagination on healthy individuals, and provided a comprehensive analysis of the performance of a short-latency brain switch when varying these three factors. The morphological investigation showed a cross-subject consistency of a prolonged negative phase in MRCP, and a delayed beta rebound in sensory-motor rhythms during repetitive tasks. The detection performance had the greatest accuracy when using ballistic MRCP with time-series analysis. In this case, the true positive rate was ~70% for a detection latency of ~200 ms. The results presented here are of practical relevance for designing BCI systems for motor function rehabilitation.

  4. Artifact suppression and analysis of brain activities with electroencephalography signals.

    Science.gov (United States)

    Rashed-Al-Mahfuz, Md; Islam, Md Rabiul; Hirose, Keikichi; Molla, Md Khademul Islam

    2013-06-05

    Brain-computer interface is a communication system that connects the brain with computer (or other devices) but is not dependent on the normal output of the brain (i.e., peripheral nerve and muscle). Electro-oculogram is a dominant artifact which has a significant negative influence on further analysis of real electroencephalography data. This paper presented a data adaptive technique for artifact suppression and brain wave extraction from electroencephalography signals to detect regional brain activities. Empirical mode decomposition based adaptive thresholding approach was employed here to suppress the electro-oculogram artifact. Fractional Gaussian noise was used to determine the threshold level derived from the analysis data without any training. The purified electroencephalography signal was composed of the brain waves also called rhythmic components which represent the brain activities. The rhythmic components were extracted from each electroencephalography channel using adaptive wiener filter with the original scale. The regional brain activities were mapped on the basis of the spatial distribution of rhythmic components, and the results showed that different regions of the brain are activated in response to different stimuli. This research analyzed the activities of a single rhythmic component, alpha with respect to different motor imaginations. The experimental results showed that the proposed method is very efficient in artifact suppression and identifying individual motor imagery based on the activities of alpha component.

  5. Goal selection versus process control in a brain-computer interface based on sensorimotor rhythms.

    Science.gov (United States)

    Royer, Audrey S; He, Bin

    2009-02-01

    In a brain-computer interface (BCI) utilizing a process control strategy, the signal from the cortex is used to control the fine motor details normally handled by other parts of the brain. In a BCI utilizing a goal selection strategy, the signal from the cortex is used to determine the overall end goal of the user, and the BCI controls the fine motor details. A BCI based on goal selection may be an easier and more natural system than one based on process control. Although goal selection in theory may surpass process control, the two have never been directly compared, as we are reporting here. Eight young healthy human subjects participated in the present study, three trained and five naïve in BCI usage. Scalp-recorded electroencephalograms (EEG) were used to control a computer cursor during five different paradigms. The paradigms were similar in their underlying signal processing and used the same control signal. However, three were based on goal selection, and two on process control. For both the trained and naïve populations, goal selection had more hits per run, was faster, more accurate (for seven out of eight subjects) and had a higher information transfer rate than process control. Goal selection outperformed process control in every measure studied in the present investigation.

  6. Impaired motor imagery in right hemiparetic cerebral palsy

    NARCIS (Netherlands)

    Mutsaarts, M.J.H.; Steenbergen, B.; Bekkering, H.

    2007-01-01

    It is generally assumed that movements of a part of the body (e.g., hands) are simulated in motor imagery (MI) tasks. This is evidenced by a linear increase in reaction time as a function of the angular rotation of the stimulus. Under the assumption that MI plays a critical role for anticipatory

  7. Kinesthetic imagery of musical performance

    Directory of Open Access Journals (Sweden)

    Martin eLotze

    2013-06-01

    Full Text Available Musicians use different kinds of imagery. This review focuses on kinesthetic imagery, which has been shown to be an effective complement to actively playing an instrument. However, experience in actual movement performance seems to be a requirement for a recruitment of those brain areas representing movement ideation during imagery. An internal model of movement performance might be more differentiated when training has been more intense or simply performed more often. Therefore, with respect to kinesthetic imagery, these strategies are predominantly found in professional musicians. There are a few possible reasons as to why kinesthetic imagery is used in addition to active training; one example is the need for mental rehearsal of the technically most difficult passages. Training difficult passages repeatedly has the potential to induce fatigue in tendons and muscles and can ultimately result in the development of dystonia. Another reason for mental practice is that mental rehearsal of the piece helps to improve performance if the instrument is not available for actual training as is the case for professional musicians when they are travelling to various appearances. Overall, mental imagery in musicians is not necessarily specific to motor, somatosensory, auditory or visual aspects of imagery, but integrates them all. In particular, the audiomotor loop is highly important, since auditory aspects are crucial for guiding motor performance. Furthermore, slight co-movement, for instance of the fingers, usually occurs when imagining musical performance, a situation different to the laboratory condition where movement execution is strictly controlled. All these aspects result in a distinctive representation map for the mental imagery of musical performance. This review summarizes behavioral data, and findings from functional brain imaging studies of mental imagery of musical performance.

  8. Modulation of mu rhythm desynchronization during motor imagery by transcranial direct current stimulation

    Directory of Open Access Journals (Sweden)

    Kimura Akio

    2010-06-01

    Full Text Available Abstract Background The mu event-related desynchronization (ERD is supposed to reflect motor preparation and appear during motor imagery. The aim of this study is to examine the modulation of ERD with transcranial direct current stimulation (tDCS. Methods Six healthy subjects were asked to imagine their right hand grasping something after receiving a visual cue. Electroencephalograms (EEGs were recorded near the left M1. ERD of the mu rhythm (mu ERD by right hand motor imagery was measured. tDCS (10 min, 1 mA was used to modulate the cortical excitability of M1. Anodal, cathodal, and sham tDCS were tested in each subject with a randomized sequence on different days. Each condition was separated from the preceding one by more than 1 week in the same subject. Before and after tDCS, mu ERD was assessed. The motor thresholds (MT of the left M1 were also measured with transcranial magnetic stimulation. Results Mu ERD significantly increased after anodal stimulation, whereas it significantly decreased after cathodal stimulation. There was a significant correlation between mu ERD and MT. Conclusions Opposing effects on mu ERD based on the orientation of the stimulation suggest that mu ERD is affected by cortical excitability.

  9. Effects of motor imagery combined with functional electrical stimulation on upper limb motor function of patients with acute ischemic stroke

    Directory of Open Access Journals (Sweden)

    Shou-feng LIU

    2015-03-01

    Full Text Available Objective To explore the effects of motor imagery (MI combined with the third generation functional electrical stimulation (FES on upper limb motor function in acute ischemic stroke patients with hemiplegia.  Methods Forty acute ischemic stroke patients, within 48 h of onset, were randomly divided into FES group (N = 20 and combination group (FES combined with motor imagery, N = 20. All patients received basic routine rehabilitation training, for example, good limb positioning, accepting braces, balance training and training in the activities of daily living (ADL. FES group received the third generation FES therapy and the combination group also received motor imagery for 2 weeks. All of the patients were assessed with Fugl-Meyer Assessment (FMA, Action Research Arm Test (ARAT and active range of motion (AROM of wrist dorsiflexion before and after 2 weeks of treatment.  Results After 2 weeks of treatment, the 2 groups had significantly higher FMA score, ARAT score and AROM of wrist dorsiflexion than that in pre-treatment (P = 0.000, for all. Besides, the FMA score (t = - 2.528, P = 0.016, ARAT score (t = - 2.562, P = 0.014 and AROM of wrist dorsiflexion (t = - 2.469, P = 0.018 in the combination group were significantly higher than that in the FES group. There were interactions of treatment methods with observation time points (P < 0.05, for all.  Conclusions Motor imagery combined with the third generation FES can effectively promote the recovery of upper limb motor function and motion range of wrist dorsiflexion in patients with acute ischemic stroke. DOI: 10.3969/j.issn.1672-6731.2015.03.008

  10. Motor imagery and its effect on complex regional pain syndrome: an integrative review

    Directory of Open Access Journals (Sweden)

    Nélio Silva de Souza

    2015-12-01

    Full Text Available The motor imagery (MI has been proposed as a treatment in the complex regional pain syndrome type 1 (CRPS-1, since it seems to promote a brain reorganization effect on sensory- motor areas of pain perception. The aim of this paper is to investigate, through an integrative critical review, the influence of MI on the CRPS-1, correlating their evidence to clinical practice. Research in PEDro, Medline, Bireme and Google Scholar databases was conducted. Nine randomized controlled trials (level 2, 1 non-controlled clinical study (level 3, 1 case study (level 4, 1 systematic review (level 1, 2 review articles and 1 comment (level 5 were found. We can conclude that MI has shown effect in reducing pain and functionality that remains after 6 months of treatment. However, the difference between the MI strategies for CRPS-1 is unknown as well as the intensity of mental stress influences the painful response or effect of MI or other peripheral neuropathies.

  11. When thoughts become action: an fMRI paradigm to study volitional brain activity in non-communicative brain injured patients.

    Science.gov (United States)

    Boly, M; Coleman, M R; Davis, M H; Hampshire, A; Bor, D; Moonen, G; Maquet, P A; Pickard, J D; Laureys, S; Owen, A M

    2007-07-01

    The assessment of voluntary behavior in non-communicative brain injured patients is often challenging due to the existence of profound motor impairment. In the absence of a full understanding of the neural correlates of consciousness, even a normal activation in response to passive sensory stimulation cannot be considered as proof of the presence of awareness in these patients. In contrast, predicted activation in response to the instruction to perform a mental imagery task would provide evidence of voluntary task-dependent brain activity, and hence of consciousness, in non-communicative patients. However, no data yet exist to indicate which imagery instructions would yield reliable single subject activation. The aim of the present study was to establish such a paradigm in healthy volunteers. Two exploratory experiments evaluated the reproducibility of individual brain activation elicited by four distinct mental imagery tasks. The two most robust mental imagery tasks were found to be spatial navigation and motor imagery. In a third experiment, where these two tasks were directly compared, differentiation of each task from one another and from rest periods was assessed blindly using a priori criteria and was correct for every volunteer. The spatial navigation and motor imagery tasks described here permit the identification of volitional brain activation at the single subject level, without a motor response. Volunteer as well as patient data [Owen, A.M., Coleman, M.R., Boly, M., Davis, M.H., Laureys, S., Pickard J.D., 2006. Detecting awareness in the vegetative state. Science 313, 1402] strongly suggest that this paradigm may provide a method for assessing the presence of volitional brain activity, and thus of consciousness, in non-communicative brain-injured patients.

  12. Touch-based Brain Computer Interfaces: State of the art

    NARCIS (Netherlands)

    Erp, J.B.F. van; Brouwer, A.M.

    2014-01-01

    Brain Computer Interfaces (BCIs) rely on the user's brain activity to control equipment or computer devices. Many BCIs are based on imagined movement (called active BCIs) or the fact that brain patterns differ in reaction to relevant or attended stimuli in comparison to irrelevant or unattended

  13. Rehabilitation of patients with motor disabilities using computer vision based techniques

    Directory of Open Access Journals (Sweden)

    Alejandro Reyes-Amaro

    2012-05-01

    Full Text Available In this paper we present details about the implementation of computer vision based applications for the rehabilitation of patients with motor disabilities. The applications are conceived as serious games, where the computer-patient interaction during playing contributes to the development of different motor skills. The use of computer vision methods allows the automatic guidance of the patient’s movements making constant specialized supervision unnecessary. The hardware requirements are limited to low-cost devices like usual webcams and Netbooks.

  14. Feature Selection Strategy for Classification of Single-Trial EEG Elicited by Motor Imagery

    DEFF Research Database (Denmark)

    Prasad, Swati; Tan, Zheng-Hua; Prasad, Ramjee

    2011-01-01

    Brain-Computer Interface (BCI) provides new means of communication for people with motor disabilities by utilizing electroencephalographic activity. Selection of features from Electroencephalogram (EEG) signals for classification plays a key part in the development of BCI systems. In this paper, we...

  15. Motor imagery training promotes motor learning in adolescents with cerebral palsy: comparison between left and right hemiparesis.

    Science.gov (United States)

    Cabral-Sequeira, Audrey Sartori; Coelho, Daniel Boari; Teixeira, Luis Augusto

    2016-06-01

    This experiment was designed to evaluate the effects of pure motor imagery training (MIT) and its combination with physical practice on learning an aiming task with the more affected arm in adolescents suffering from cerebral palsy. Effect of MIT was evaluated as a function of side of hemiparesis. The experiment was accomplished by 11- to 16-year-old participants (M = 13.58 years), who suffered left (n = 16) or right (n = 15) mild hemiparesis. They were exposed to pure MIT (day 1) followed by physical practice (day 2) on an aiming task demanding movement accuracy and speed. Posttraining movement kinematics of the group receiving MIT were compared with movement kinematics of the control group after receiving recreational activities (day 1) and physical practice (day 2). Kinematic analysis showed that MIT led to decreased movement time and straighter hand displacements to the target. Performance achievements from MIT were increased with further physical practice, leading to enhanced effects on motor learning. Retention evaluation indicated that performance improvement from pure MIT and its combination with physical practice were stable over time. Performance achievements were equivalent between adolescents with either right or left hemiparesis, suggesting similar capacity between these groups to achieve performance improvement from pure imagery training and from its association with physical practice. Our results suggest that motor imagery training is a procedure potentially useful to increase motor learning achievements in individuals suffering from cerebral palsy.

  16. Real-time changes in corticospinal excitability related to motor imagery of a force control task.

    Science.gov (United States)

    Tatemoto, Tsuyoshi; Tsuchiya, Junko; Numata, Atsuki; Osawa, Ryuji; Yamaguchi, Tomofumi; Tanabe, Shigeo; Kondo, Kunitsugu; Otaka, Yohei; Sugawara, Kenichi

    2017-09-29

    To investigate real-time excitability changes in corticospinal pathways related to motor imagery in a changing force control task, using transcranial magnetic stimulation (TMS). Ten healthy volunteers learnt to control the contractile force of isometric right wrist dorsiflexion in order to track an on-screen sine wave form. Participants performed the trained task 40 times with actual muscle contraction in order to construct the motor image. They were then instructed to execute the task without actual muscle contraction, but by imagining contraction of the right wrist in dorsiflexion. Motor evoked potentials (MEPs), induced by TMS in the right extensor carpi radialis muscle (ECR) and flexor carpi radialis muscle (FCR), were measured during motor imagery. MEPs were induced at five time points: prior to imagery, during the gradual generation of the imaged wrist dorsiflexion (Increasing phase), the peak value of the sine wave, during the gradual reduction (Decreasing phase), and after completion of the task. The MEP ratio, as the ratio of imaged MEPs to resting-state, was compared between pre- and post-training at each time point. In the ECR muscle, the MEP ratio significantly increased during the Increasing phase and at the peak force of dorsiflexion imagery after training. Moreover, the MEP ratio was significantly greater in the Increasing phase than in the Decreasing phase. In the FCR, there were no significant consistent changes. Corticospinal excitability during motor imagery in an isometric contraction task was modulated in relation to the phase of force control after image construction. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Brain-Computer Interface Spellers: A Review.

    Science.gov (United States)

    Rezeika, Aya; Benda, Mihaly; Stawicki, Piotr; Gembler, Felix; Saboor, Abdul; Volosyak, Ivan

    2018-03-30

    A Brain-Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.

  18. Hybrid EEG-fNIRS Asynchronous Brain-Computer Interface for Multiple Motor Tasks.

    Directory of Open Access Journals (Sweden)

    Alessio Paolo Buccino

    Full Text Available Non-invasive Brain-Computer Interfaces (BCI have demonstrated great promise for neuroprosthetics and assistive devices. Here we aim to investigate methods to combine Electroencephalography (EEG and functional Near-Infrared Spectroscopy (fNIRS in an asynchronous Sensory Motor rhythm (SMR-based BCI. We attempted to classify 4 different executed movements, namely, Right-Arm-Left-Arm-Right-Hand-Left-Hand tasks. Previous studies demonstrated the benefit of EEG-fNIRS combination. However, since normally fNIRS hemodynamic response shows a long delay, we investigated new features, involving slope indicators, in order to immediately detect changes in the signals. Moreover, Common Spatial Patterns (CSPs have been applied to both EEG and fNIRS signals. 15 healthy subjects took part in the experiments and since 25 trials per class were available, CSPs have been regularized with information from the entire population of participants and optimized using genetic algorithms. The different features have been compared in terms of performance and the dynamic accuracy over trials shows that the introduced methods diminish the fNIRS delay in the detection of changes.

  19. Discrimination of Motor Imagery-Induced EEG Patterns in Patients with Complete Spinal Cord Injury

    Directory of Open Access Journals (Sweden)

    G. Pfurtscheller

    2009-01-01

    Full Text Available EEG-based discrimination between different motor imagery states has been subject of a number of studies in healthy subjects. We investigated the EEG of 15 patients with complete spinal cord injury during imagined right hand, left hand, and feet movements. In detail we studied pair-wise discrimination functions between the 3 types of motor imagery. The following classification accuracies (mean ± SD were obtained: left versus right hand 65.03% ± 8.52, left hand versus feet 68.19% ± 11.08, and right hand versus feet 65.05% ± 9.25. In 5 out of 8 paralegic patients, the discrimination accuracy was greater than 70% but in only 1 out of 7 tetraplagic patients. The present findings provide evidence that in the majority of paraplegic patients an EEG-based BCI could achieve satisfied results. In tetraplegic patients, however, it is expected that extensive training-sessions are necessary to achieve a good BCI performance at least in some subjects.

  20. Brain-Computer Interfaces With Multi-Sensory Feedback for Stroke Rehabilitation: A Case Study.

    Science.gov (United States)

    Irimia, Danut C; Cho, Woosang; Ortner, Rupert; Allison, Brendan Z; Ignat, Bogdan E; Edlinger, Guenter; Guger, Christoph

    2017-11-01

    Conventional therapies do not provide paralyzed patients with closed-loop sensorimotor integration for motor rehabilitation. This work presents the recoveriX system, a hardware and software platform that combines a motor imagery (MI)-based brain-computer interface (BCI), functional electrical stimulation (FES), and visual feedback technologies for a complete sensorimotor closed-loop therapy system for poststroke rehabilitation. The proposed system was tested on two chronic stroke patients in a clinical environment. The patients were instructed to imagine the movement of either the left or right hand in random order. During these two MI tasks, two types of feedback were provided: a bar extending to the left or right side of a monitor as visual feedback and passive hand opening stimulated from FES as proprioceptive feedback. Both types of feedback relied on the BCI classification result achieved using common spatial patterns and a linear discriminant analysis classifier. After 10 sessions of recoveriX training, one patient partially regained control of wrist extension in her paretic wrist and the other patient increased the range of middle finger movement by 1 cm. A controlled group study is planned with a new version of the recoveriX system, which will have several improvements. © 2017 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

  1. Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface.

    Science.gov (United States)

    Lajoie, Guillaume; Krouchev, Nedialko I; Kalaska, John F; Fairhall, Adrienne L; Fetz, Eberhard E

    2017-02-01

    Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP) rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity.

  2. Effects of truncal motor imagery practice on trunk performance, functional balance, and daily activities in acute stroke

    Directory of Open Access Journals (Sweden)

    Priyanka Shah

    2016-01-01

    Full Text Available Background: Motor imagery is beneficial to treat upper and lower limbs motor impairments in stroke patients, but the effects of imagery in the trunk recovery have not been reported. Hence, the aim is to test the effects of truncal motor imagery practice on trunk performance, functional balance, and daily activities in acute stroke patients. Methods: This pilot randomized clinical trial was conducted in acute stroke unit. Acute stroke patients with hemodynamic stability, aged between 30 and 70 years, first time stroke, and scoring <20 on trunk impairment scale (TIS were included in the study. Patients in the experimental group practiced trunk motor imagery in addition to physical training. Control group was given conventional physical therapy. The treatment intensity was 90 min/day, 6 days a week for 3 weeks duration. Trunk control test, TIS, brunel balance assessment (BBA, and Barthel index (BI were considered as the outcome measures. Results: Among 23 patients included in the study, 12 and 11 patients, respectively, in the control and experimental groups completed the intervention. Repeated measures ANOVA, i.e., timeFNx01 group factor analysis and effect size showed statistically significant improvements (P = 0.001 in the scores of TIS (1.64, BBA (1.83, and BI (0.67. Conclusion: Motor imagery of trunk in addition to the physical practice showed benefits in improving trunk performance, functional balance, and daily living in acute stroke.

  3. Changes in Electroencephalography Complexity using a Brain Computer Interface-Motor Observation Training in Chronic Stroke Patients: A Fuzzy Approximate Entropy Analysis

    Directory of Open Access Journals (Sweden)

    Rui Sun

    2017-09-01

    Full Text Available Entropy-based algorithms have been suggested as robust estimators of electroencephalography (EEG predictability or regularity. This study aimed to examine possible disturbances in EEG complexity as a means to elucidate the pathophysiological mechanisms in chronic stroke, before and after a brain computer interface (BCI-motor observation intervention. Eleven chronic stroke subjects and nine unimpaired subjects were recruited to examine the differences in their EEG complexity. The BCI-motor observation intervention was designed to promote functional recovery of the hand in stroke subjects. Fuzzy approximate entropy (fApEn, a novel entropy-based algorithm designed to evaluate complexity in physiological systems, was applied to assess the EEG signals acquired from unimpaired subjects and stroke subjects, both before and after training. The results showed that stroke subjects had significantly lower EEG fApEn than unimpaired subjects (p < 0.05 in the motor cortex area of the brain (C3, C4, FC3, FC4, CP3, and CP4 in both hemispheres before training. After training, motor function of the paretic upper limb, assessed by the Fugl-Meyer Assessment-Upper Limb (FMA-UL, Action Research Arm Test (ARAT, and Wolf Motor Function Test (WMFT improved significantly (p < 0.05. Furthermore, the EEG fApEn in stroke subjects increased considerably in the central area of the contralesional hemisphere after training (p < 0.05. A significant correlation was noted between clinical scales (FMA-UL, ARAT, and WMFT and EEG fApEn in C3/C4 in the contralesional hemisphere (p < 0.05. This finding suggests that the increase in EEG fApEn could be an estimator of the variance in upper limb motor function improvement. In summary, fApEn can be used to identify abnormal EEG complexity in chronic stroke, when used with BCI-motor observation training. Moreover, these findings based on the fApEn of EEG signals also expand the existing interpretation of training-induced functional

  4. Theory and practice in sport psychology and motor behaviour needs to be constrained by integrative modelling of brain and behaviour.

    Science.gov (United States)

    Keil, D; Holmes, P; Bennett, S; Davids, K; Smith, N

    2000-06-01

    Because of advances in technology, the non-invasive study of the human brain has enhanced the knowledge base within the neurosciences, resulting in an increased impact on the psychological study of human behaviour. We argue that application of this knowledge base should be considered in theoretical modelling within sport psychology and motor behaviour alongside existing ideas. We propose that interventions founded on current theoretical and empirical understanding in both psychology and the neurosciences may ultimately lead to greater benefits for athletes during practice and performance. As vehicles for exploring the arguments of a greater integration of psychology and neurosciences research, imagery and perception-action within the sport psychology and motor behaviour domains will serve as exemplars. Current neuroscience evidence will be discussed in relation to theoretical developments; the implications for sport scientists will be considered.

  5. A small, portable, battery-powered brain-computer interface system for motor rehabilitation.

    Science.gov (United States)

    McCrimmon, Colin M; Ming Wang; Silva Lopes, Lucas; Wang, Po T; Karimi-Bidhendi, Alireza; Liu, Charles Y; Heydari, Payam; Nenadic, Zoran; Do, An H

    2016-08-01

    Motor rehabilitation using brain-computer interface (BCI) systems may facilitate functional recovery in individuals after stroke or spinal cord injury. Nevertheless, these systems are typically ill-suited for widespread adoption due to their size, cost, and complexity. In this paper, a small, portable, and extremely cost-efficient (microcontroller and touchscreen. The system's performance was tested using a movement-related BCI task in 3 able-bodied subjects with minimal previous BCI experience. Specifically, subjects were instructed to alternate between relaxing and dorsiflexing their right foot, while their EEG was acquired and analyzed in real-time by the BCI system to decode their underlying movement state. The EEG signals acquired by the custom amplifier array were similar to those acquired by a commercial amplifier (maximum correlation coefficient ρ=0.85). During real-time BCI operation, the average correlation between instructional cues and decoded BCI states across all subjects (ρ=0.70) was comparable to that of full-size BCI systems. Small, portable, and inexpensive BCI systems such as the one reported here may promote a widespread adoption of BCI-based movement rehabilitation devices in stroke and spinal cord injury populations.

  6. The Effect of Visual and Auditory Enhancements on Excitability of the Primary Motor Cortex during Motor Imagery: A Pilot Study

    Science.gov (United States)

    Ikeda, Kohei; Higashi, Toshio; Sugawara, Kenichi; Tomori, Kounosuke; Kinoshita, Hiroshi; Kasai, Tatsuya

    2012-01-01

    The effect of visual and auditory enhancements of finger movement on corticospinal excitability during motor imagery (MI) was investigated using the transcranial magnetic stimulation technique. Motor-evoked potentials were elicited from the abductor digit minimi muscle during MI with auditory, visual and, auditory and visual information, and no…

  7. Imagining is Not Doing but Involves Specific Motor Commands: A Review of Experimental Data Related to Motor Inhibition.

    Science.gov (United States)

    Guillot, Aymeric; Di Rienzo, Franck; Macintyre, Tadhg; Moran, Aidan; Collet, Christian

    2012-01-01

    There is now compelling evidence that motor imagery (MI) and actual movement share common neural substrate. However, the question of how MI inhibits the transmission of motor commands into the efferent pathways in order to prevent any movement is largely unresolved. Similarly, little is known about the nature of the electromyographic activity that is apparent during MI. In addressing these gaps in the literature, the present paper argues that MI includes motor execution commands for muscle contractions which are blocked at some level of the motor system by inhibitory mechanisms. We first assemble data from neuroimaging studies that demonstrate that the neural networks mediating MI and motor performance are not totally overlapping, thereby highlighting potential differences between MI and actual motor execution. We then review MI data indicating the presence of subliminal muscular activity reflecting the intrinsic characteristics of the motor command as well as increased corticomotor excitability. The third section not only considers the inhibitory mechanisms involved during MI but also examines how the brain resolves the problem of issuing the motor command for action while supervising motor inhibition when people engage in voluntary movement during MI. The last part of the paper draws on imagery research in clinical contexts to suggest that some patients move while imagining an action, although they are not aware of such movements. In particular, experimental data from amputees as well as from patients with Parkinson's disease are discussed. We also review recent studies based on comparing brain activity in tetraplegic patients with that from healthy matched controls that provide insights into inhibitory processes during MI. We conclude by arguing that based on available evidence, a multifactorial explanation of motor inhibition during MI is warranted.

  8. Imagining is not doing but involves specific motor commands: a review of experimental data related to motor inhibition

    Directory of Open Access Journals (Sweden)

    Aymeric eGuillot

    2012-09-01

    Full Text Available There is now compelling evidence that motor imagery (MI and actual movement share common neural substrate. However, the question of how MI inhibits the transmission of motor commands into the efferent pathways in order to prevent any movement is largely unresolved. Similarly, little is known about the nature of the electromyographic activity that is apparent during MI. In addressing these gaps in the literature, the present paper argues that MI includes motor execution commands for muscle contractions which are blocked at some level of the motor system by inhibitory mechanisms. We first assemble data from neuroimaging studies that demonstrate that the neural networks mediating MI and motor performance are not totally overlapping, thereby highlighting potential differences between MI and actual motor execution. We then review MI data indicating the presence of subliminal muscular activity reflecting the intrinsic characteristics of the motor command as well as increased corticomotor excitability. The third section not only considers the inhibitory mechanisms involved during MI but also examines how the brain resolves the problem of issuing the motor command for action while supervising motor inhibition when people engage in voluntary movement during MI. The last part of the paper draws on imagery research in clinical contexts to suggest that some patients move while imagining an action, although they are not aware of such movements. In particular, experimental data from amputees as well as from patients with Parkinson’s disease are discussed. We also review recent studies based on comparing brain activity in tetraplegic patients with that from healthy matched controls that provide insights into inhibitory processes during MI. We conclude by arguing that based on available evidence, a multifactorial explanation of motor inhibition during MI is warranted.

  9. Je pense donc je fais: transcranial direct current stimulation modulates brain oscillations associated with motor imagery and movement observation

    Directory of Open Access Journals (Sweden)

    Olivia Morgan Lapenta

    2013-06-01

    Full Text Available Motor system neural networks are activated during movement imagery, observation and execution, with a neural signature characterized by suppression of the Mu rhythm. In order to investigate the origin of this neurophysiological marker, we tested whether transcranial direct current stimulation (tDCS modifies Mu rhythm oscillations during tasks involving observation and imagery of biological and non-biological movements. We applied tDCS (anodal, cathodal and sham in 21 male participants (mean age 23.8+3.06, over the left M1 with a current of 2mA for 20 minutes. Following this, we recorded the EEG at C3, C4 and Cz and surrounding C3 and C4 electrodes. Analyses of C3 and C4 showed significant effects for biological vs. non-biological movement (p=0.005, and differential hemisphere effects according to the type of stimulation (p=0.04 and type of movement (p=0.02. Analyses of surrounding electrodes revealed significant interaction effects considering type of stimulation and imagery or observation of biological or non-biological movement (p=0.03. The main findings of this study were (i Mu desynchronization during biological movement of the hand region in the contralateral hemisphere after sham tDCS; (ii polarity-dependent modulation effects of tDCS on the Mu rhythm, i.e. anodal tDCS led to Mu synchronization while cathodal tDCS led to Mu desynchronization during movement observation and imagery (iii specific focal and opposite inter-hemispheric effects, i.e. contrary effects for the surrounding electrodes during imagery condition and also for inter-hemispheric electrodes (C3 vs. C4. These findings provide insights into the cortical oscillations during movement observation and imagery. Furthermore it shows that tDCS can be highly focal when guided by a behavioral task.

  10. Je pense donc je fais: transcranial direct current stimulation modulates brain oscillations associated with motor imagery and movement observation.

    Science.gov (United States)

    Lapenta, Olivia M; Minati, Ludovico; Fregni, Felipe; Boggio, Paulo S

    2013-01-01

    Motor system neural networks are activated during movement imagery, observation and execution, with a neural signature characterized by suppression of the Mu rhythm. In order to investigate the origin of this neurophysiological marker, we tested whether transcranial direct current stimulation (tDCS) modifies Mu rhythm oscillations during tasks involving observation and imagery of biological and non-biological movements. We applied tDCS (anodal, cathodal, and sham) in 21 male participants (mean age 23.8 ± 3.06), over the left M1 with a current of 2 mA for 20 min. Following this, we recorded the EEG at C3, C4, and Cz and surrounding C3 and C4 electrodes. Analyses of C3 and C4 showed significant effects for biological vs. non-biological movement (p = 0.005), and differential hemisphere effects according to the type of stimulation (p = 0.04) and type of movement (p = 0.02). Analyses of surrounding electrodes revealed significant interaction effects considering type of stimulation and imagery or observation of biological or non-biological movement (p = 0.03). The main findings of this study were (1) Mu desynchronization during biological movement of the hand region in the contralateral hemisphere after sham tDCS; (2) polarity-dependent modulation effects of tDCS on the Mu rhythm, i.e., anodal tDCS led to Mu synchronization while cathodal tDCS led to Mu desynchronization during movement observation and imagery (3) specific focal and opposite inter-hemispheric effects, i.e., contrary effects for the surrounding electrodes during imagery condition and also for inter-hemispheric electrodes (C3 vs. C4). These findings provide insights into the cortical oscillations during movement observation and imagery. Furthermore, it shows that tDCS can be highly focal when guided by a behavioral task.

  11. Clinical studies of brain functional images by motor activation using single photon emission computed tomography

    Energy Technology Data Exchange (ETDEWEB)

    Kawaguchi, Masahiro [Gifu Univ. (Japan). School of Medicine

    1998-09-01

    Thirty participants (10 normal controls; group A, 5 patients with brain tumors located near central sulcus without hemiparesis; group B, 10 patients with brain tumors located near central sulcus with hemiparesis; group C, and 5 patients with brain tumors besides the central regions with hemiparesis; group D) were enrolled. The images were performed by means of split-dose method with {sup 99m}Tc-ECD at rest condition (SPECT 1) and during hand grasping (SPECT 2). The activation SPECT were obtained by subtracting SPECT 1 from SPECT 2, and the functional mapping was made by the strict registration of the activation SPECT with 3D MRI. To evaluate the changes of CBF (%{Delta}CBF) of the sensorimotor and supplementary motor areas on the functional mapping, ratio of the average counts of SPECT 1 and SPECT 2 was calculated and statistically compared. The functional activation paradigms caused a significant increase of CBF in the sensorimotor area contra-lateral to the stimulated hand, although the sensorimotor area and the central sulcus in groups B and C were dislocated, compared with hemisphere of non-tumor side. The sensorimotor area ipsi-lateral to the stimulated hand could be detected in almost of all subjects. The supplementary motor area could be detected in all subjects. In group A, the average %{Delta}CBF were up 24.1{+-}4.3% in the contra-lateral sensorimotor area, and 22.3{+-}3.6% in the supplementary motor area, respectively. The average %{Delta}CBF in the contra-lateral sensorimotor area of group D was significantly higher than that of group A. The brain functional mapping by motor activation using SPECT could localize the area of cortical motor function in normal volunteers and patients with brain tumors. The changes of regional CBF by activation SPECT precisely assess the cortical motor function even in patients with brain tumors located near central sulcus. (K.H.)

  12. Clinical studies of brain functional images by motor activation using single photon emission computed tomography

    International Nuclear Information System (INIS)

    Kawaguchi, Masahiro

    1998-01-01

    Thirty participants (10 normal controls; group A, 5 patients with brain tumors located near central sulcus without hemiparesis; group B, 10 patients with brain tumors located near central sulcus with hemiparesis; group C, and 5 patients with brain tumors besides the central regions with hemiparesis; group D) were enrolled. The images were performed by means of split-dose method with 99m Tc-ECD at rest condition (SPECT 1) and during hand grasping (SPECT 2). The activation SPECT were obtained by subtracting SPECT 1 from SPECT 2, and the functional mapping was made by the strict registration of the activation SPECT with 3D MRI. To evaluate the changes of CBF (%ΔCBF) of the sensorimotor and supplementary motor areas on the functional mapping, ratio of the average counts of SPECT 1 and SPECT 2 was calculated and statistically compared. The functional activation paradigms caused a significant increase of CBF in the sensorimotor area contra-lateral to the stimulated hand, although the sensorimotor area and the central sulcus in groups B and C were dislocated, compared with hemisphere of non-tumor side. The sensorimotor area ipsi-lateral to the stimulated hand could be detected in almost of all subjects. The supplementary motor area could be detected in all subjects. In group A, the average %ΔCBF were up 24.1±4.3% in the contra-lateral sensorimotor area, and 22.3±3.6% in the supplementary motor area, respectively. The average %ΔCBF in the contra-lateral sensorimotor area of group D was significantly higher than that of group A. The brain functional mapping by motor activation using SPECT could localize the area of cortical motor function in normal volunteers and patients with brain tumors. The changes of regional CBF by activation SPECT precisely assess the cortical motor function even in patients with brain tumors located near central sulcus. (K.H.)

  13. Using motor imagery to study the neural substrates of dynamic balance

    NARCIS (Netherlands)

    Ferraye, M.U.; Debû, B.H.G.; Heil, L.; Carpenter, M.; Bloem, B.R.; Toni, I.

    2014-01-01

    This study examines the cerebral structures involved in dynamic balance using a motor imagery (MI) protocol. We recorded cerebral activity with functional magnetic resonance imaging while subjects imagined swaying on a balance board along the sagittal plane to point a laser at target pairs of

  14. Personal Computer Based Controller For Switched Reluctance Motor Drives

    Science.gov (United States)

    Mang, X.; Krishnan, R.; Adkar, S.; Chandramouli, G.

    1987-10-01

    Th9, switched reluctance motor (SRM) has recently gained considerable attention in the variable speed drive market. Two important factors that have contributed to this are, the simplicity of construction and the possibility of developing low cost con-trollers with minimum number of switching devices in the drive circuits. This is mainly due to the state-of-art of the present digital circuits technology and the low cost of switching devices. The control of this motor drive is under research. Optimized performance of the SRM motor drive is very dependent on the integration of the controller, converter and the motor. This research on system integration involves considerable changes in the control algorithms and their implementation. A Personal computer (PC) based controller is very appropriate for this purpose. Accordingly, the present paper is concerned with the design of a PC based controller for a SRM. The PC allows for real-time microprocessor control with the possibility of on-line system parameter modifications. Software reconfiguration of this controller is easier than a hardware based controller. User friendliness is a natural consequence of such a system. Considering the low cost of PCs, this controller will offer an excellent cost-effective means of studying the control strategies for the SRM drive intop greater detail than in the past.

  15. Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface.

    Directory of Open Access Journals (Sweden)

    Guillaume Lajoie

    2017-02-01

    Full Text Available Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI can artificially strengthen connections between separate neural sites in motor cortex (MC. When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity.

  16. Contribution of the primary motor cortex to motor imagery: a subthreshold TMS study.

    Science.gov (United States)

    Pelgrims, Barbara; Michaux, Nicolas; Olivier, Etienne; Andres, Michael

    2011-09-01

    Motor imagery (MI) mostly activates the same brain regions as movement execution (ME) including the primary motor cortex (Brodmann area 4, BA4). However, whether BA4 is functionally relevant for MI remains controversial. The finding that MI tasks are impaired by BA4 virtual lesions induced by transcranial magnetic stimulation (TMS) supports this view, though previous studies do not permit to exclude that BA4 is also involved in other processes such as hand recognition. Additionally, previous works largely underestimated the possible negative consequences of TMS-induced muscle twitches on MI task performance. Here we investigated the role of BA4 in MI by interfering with the function of the left or right BA4 in healthy subjects performing a MI task in which they had to make laterality judgements on rotated hand drawings. We used a subthreshold repetitive TMS protocol and monitored electromyographic activity to exclude undesirable effects of hand muscle twitches. We found that BA4 virtual lesions selectively increased reaction times in laterality judgments on hand drawings, leaving unaffected a task of equal difficulty, involving judgments on letters. Interestingly, the effects of virtual lesions of left and right BA4 on MI task performance were the same irrespective of the laterality (left/right) of hand drawings. A second experiment allowed us to rule out the possibility that BA4 lesions affect visual or semantic processing of hand drawings. Altogether, these results indicate that BA4 contribution to MI tasks is specifically related to the mental simulation process and further emphasize the functional coupling between ME and MI. Copyright © 2010 Wiley-Liss, Inc.

  17. Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

    Science.gov (United States)

    Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

    2016-01-01

    Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

  18. Interfaz cerebro computador basada en P300 para la comunicación alternativa: estudio de caso en dos adolescentes en situación de discapacidad motora [P300 based Brain Computer Interface for alternative communication: a case study with two teenagers with motor disabilities

    NARCIS (Netherlands)

    Garcia Cossio, E.; Fernandez, C.; Gaviria, M.E.; Palacio, C.; Alvaran, L.; Torres Villa, R.A.

    2011-01-01

    Brain computer interface systems use brain signals to enable the control of external devices, such as: wheelchairs, communicators, neuro-prosthesis, among others; in people with severe motor disabilities. In this study two young men with motor disabilities were trained to learn how to control a

  19. Brain death: close relatives' use of imagery as a descriptor of experience.

    Science.gov (United States)

    Frid, Ingvar; Haljamäe, Hengo; Ohlén, Joakim; Bergbom, Ingegerd

    2007-04-01

    This paper is a report of a study to explore the use of imagery to describe the experience of confronting brain death in a close relative. The brain death of a loved one has been described as an extremely difficult experience for close relatives, evoking feelings of anger, emotional pain, disbelief, guilt and suffering. It can also be difficult for relatives to distinguish brain death from the state of coma and thus difficult to apprehend information about the diagnosis. Narrative theory and a hermeneutic phenomenological method guided the interpretation of 17 narratives from close relatives of brain dead patients. All narratives were scrutinized for experiences of brain death. Data were primarily collected in 1999. The primary analysis related to close relatives' experience of brain death in a loved one. A secondary analysis of the imagery they used to describe their experience was carried out in 2003. Six categories of imagery used to describe the experience of confronting a diagnosis of brain death in a loved one emerged: chaotic unreality; inner collapse; sense of forlornness; clinging to the hope of survival; reconciliation with the reality of death; receiving care which gives comfort. Participants also identified two pairs of dimensions to describe their feelings about the relationship between their brain dead relative's body and personhood: presence-absence and divisibility-indivisibility. Being confronted with brain death meant entering into the anteroom of death, facing a loved one who is 'living-dead', and experiencing a chaotic drama of suffering. It is very important for members of the intensive care unit team to recognize, face and respond to these relatives' chaotic experiences, which cause them to need affirmation, comfort and caring. Relatives' use of imagery could be the starting point for a caring conversation about their experiences, either in conversations at the time of the death or when relatives are contacted in a later follow-up.

  20. Near-infrared spectroscopy (NIRS - electroencephalography (EEG based brain-state dependent electrotherapy (BSDE: A computational approach based on excitation-inhibition balance hypothesis

    Directory of Open Access Journals (Sweden)

    Snigdha Dagar

    2016-08-01

    Full Text Available Stroke is the leading cause of severe chronic disability and the second cause of death worldwide with 15 million new cases and 50 million stroke survivors. The post stroke chronic disability may be ameliorated with early neuro rehabilitation where non-invasive brain stimulation (NIBS techniques can be used as an adjuvant treatment to hasten the effects. However, the heterogeneity in the lesioned brain will require individualized NIBS intervention where innovative neuroimaging technologies of portable electroencephalography (EEG and functional-near-infrared spectroscopy (fNIRS can be leveraged for Brain State Dependent Electrotherapy (BSDE. In this hypothesis and theory article, we propose a computational approach based on excitation-inhibition (E-I balance hypothesis to objectively quantify the post stroke individual brain state using online fNIRS-EEG joint imaging. One of the key events that occurs following Stroke is the imbalance in local excitation-inhibition (that is the ratio of Glutamate/GABA which may be targeted with NIBS using a computational pipeline that includes individual forward models to predict current flow patterns through the lesioned brain or brain target region. The current flow will polarize the neurons which can be captured with excitation-inhibition based brain models. Furthermore, E-I balance hypothesis can be used to find the consequences of cellular polarization on neuronal information processing which can then be implicated in changes in function. We first review evidence that shows how this local imbalance between excitation-inhibition leading to functional dysfunction can be restored in targeted sites with NIBS (Motor Cortex, Somatosensory Cortex resulting in large scale plastic reorganization over the cortex, and probably facilitating recovery of functions. Secondly, we show evidence how BSDE based on inhibition–excitation balance hypothesis may target a specific brain site or network as an adjuvant treatment

  1. Motor timing deficits in sequential movements in Parkinson disease are related to action planning: a motor imagery study.

    Directory of Open Access Journals (Sweden)

    Laura Avanzino

    Full Text Available Timing of sequential movements is altered in Parkinson disease (PD. Whether timing deficits in internally generated sequential movements in PD depends also on difficulties in motor planning, rather than merely on a defective ability to materially perform the planned movement is still undefined. To unveil this issue, we adopted a modified version of an established test for motor timing, i.e. the synchronization-continuation paradigm, by introducing a motor imagery task. Motor imagery is thought to involve mainly processes of movement preparation, with reduced involvement of end-stage movement execution-related processes. Fourteen patients with PD and twelve matched healthy volunteers were asked to tap in synchrony with a metronome cue (SYNC and then, when the tone stopped, to keep tapping, trying to maintain the same rhythm (CONT-EXE or to imagine tapping at the same rhythm, rather than actually performing it (CONT-MI. We tested both a sub-second and a supra-second inter-stimulus interval between the cues. Performance was recorded using a sensor-engineered glove and analyzed measuring the temporal error and the interval reproduction accuracy index. PD patients were less accurate than healthy subjects in the supra-second time reproduction task when performing both continuation tasks (CONT-MI and CONT-EXE, whereas no difference was detected in the synchronization task and on all tasks involving a sub-second interval. Our findings suggest that PD patients exhibit a selective deficit in motor timing for sequential movements that are separated by a supra-second interval and that this deficit may be explained by a defect of motor planning. Further, we propose that difficulties in motor planning are of a sufficient degree of severity in PD to affect also the motor performance in the supra-second time reproduction task.

  2. Combined rTMS and virtual reality brain-computer interface training for motor recovery after stroke

    Science.gov (United States)

    Johnson, N. N.; Carey, J.; Edelman, B. J.; Doud, A.; Grande, A.; Lakshminarayan, K.; He, B.

    2018-02-01

    Objective. Combining repetitive transcranial magnetic stimulation (rTMS) with brain-computer interface (BCI) training can address motor impairment after stroke by down-regulating exaggerated inhibition from the contralesional hemisphere and encouraging ipsilesional activation. The objective was to evaluate the efficacy of combined rTMS  +  BCI, compared to sham rTMS  +  BCI, on motor recovery after stroke in subjects with lasting motor paresis. Approach. Three stroke subjects approximately one year post-stroke participated in three weeks of combined rTMS (real or sham) and BCI, followed by three weeks of BCI alone. Behavioral and electrophysiological differences were evaluated at baseline, after three weeks, and after six weeks of treatment. Main results. Motor improvements were observed in both real rTMS  +  BCI and sham groups, but only the former showed significant alterations in inter-hemispheric inhibition in the desired direction and increased relative ipsilesional cortical activation from fMRI. In addition, significant improvements in BCI performance over time and adequate control of the virtual reality BCI paradigm were observed only in the former group. Significance. When combined, the results highlight the feasibility and efficacy of combined rTMS  +  BCI for motor recovery, demonstrated by increased ipsilesional motor activity and improvements in behavioral function for the real rTMS  +  BCI condition in particular. Our findings also demonstrate the utility of BCI training alone, as shown by behavioral improvements for the sham rTMS  +  BCI condition. This study is the first to evaluate combined rTMS and BCI training for motor rehabilitation and provides a foundation for continued work to evaluate the potential of both rTMS and virtual reality BCI training for motor recovery after stroke.

  3. Action observation and mirror neuron network: a tool for motor stroke rehabilitation.

    Science.gov (United States)

    Sale, P; Franceschini, M

    2012-06-01

    Mirror neurons are a specific class of neurons that are activated and discharge both during observation of the same or similar motor act performed by another individual and during the execution of a motor act. Different studies based on non invasive neuroelectrophysiological assessment or functional brain imaging techniques have demonstrated the presence of the mirror neuron and their mechanism in humans. Various authors have demonstrated that in the human these networks are activated when individuals learn motor actions via execution (as in traditional motor learning), imitation, observation (as in observational learning) and motor imagery. Activation of these brain areas (inferior parietal lobe and the ventral premotor cortex, as well as the caudal part of the inferior frontal gyrus [IFG]) following observation or motor imagery may thereby facilitate subsequent movement execution by directly matching the observed or imagined action to the internal simulation of that action. It is therefore believed that this multi-sensory action-observation system enables individuals to (re) learn impaired motor functions through the activation of these internal action-related representations. In humans, the mirror mechanism is also located in various brain segment: in Broca's area, which is involved in language processing and speech production and not only in centres that mediate voluntary movement, but also in cortical areas that mediate visceromotor emotion-related behaviours. On basis of this finding, during the last 10 years various studies were carry out regarding the clinical use of action observation for motor rehabilitation of sub-acute and chronic stroke patients.

  4. Brain Plasticity and Motor Practice in Cognitive Aging

    Directory of Open Access Journals (Sweden)

    Liuyang eCai

    2014-03-01

    Full Text Available For more than two decades, there have been extensive studies of experience-based neural plasticity exploring effective applications of brain plasticity for cognitive and motor development. Research suggests that human brains continuously undergo structural reorganization and functional changes in response to stimulations or training. From a developmental point of view, the assumption of lifespan brain plasticity has been extended to older adults in terms of the benefits of cognitive training and physical therapy. To summarize recent developments, first, we introduce the concept of neural plasticity from a developmental perspective. Secondly, we note that motor learning often refers to deliberate practice and the resulting performance enhancement and adaptability. We discuss the close interplay between neural plasticity, motor learning and cognitive aging. Thirdly, we review research on motor skill acquisition in older adults with, and without, impairments relative to aging-related cognitive decline. Finally, to enhance future research and application, we highlight the implications of neural plasticity in skills learning and cognitive rehabilitation for the aging population.

  5. Muscular responses appear to be associated with existence of kinesthetic perception during combination of tendon co-vibration and motor imagery.

    Science.gov (United States)

    Shibata, Eriko; Kaneko, Fuminari; Katayose, Masaki

    2017-11-01

    The afferent inputs from peripheral sensory receptors and efferent signals from the central nervous system that underlie intentional movement can contribute to kinesthetic perception. Previous studies have revealed that tendon vibration to wrist muscles elicits an excitatory response-known as the antagonist vibratory response-in muscles antagonistic to the vibrated muscles. Therefore, the present study aimed to further investigate the effect of tendon vibration combined with motor imagery on kinesthetic perception and muscular activation. Two vibrators were applied to the tendons of the left flexor carpi radialis and extensor carpi radialis. When the vibration frequency was the same between flexors and extensors, no participant perceived movement and no muscle activity was induced. When participants imagined flexing their wrists during tendon vibration, the velocity of perceptual flexion movement increased. Furthermore, muscle activity of the flexor increased only during motor imagery. These results demonstrate that kinesthetic perception can be induced during the combination of motor imagery and co-vibration, even with no experience of kinesthetic perception from an afferent input with co-vibration at the same frequency. Although motor responses were observed during combined co-vibration and motor imagery, no such motor responses were recorded during either co-vibration alone or motor imagery alone, suggesting that muscular responses during the combined condition are associated with kinesthetic perception. Thus, the present findings indicate that kinesthetic perception is influenced by the interaction between afferent input from muscle spindles and the efferent signals that underlie intentional movement. We propose that the physiological behavior resulting from kinesthetic perception affects the process of modifying agonist muscle activity, which will be investigated in a future study.

  6. Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task

    Directory of Open Access Journals (Sweden)

    Lorraine Perronnet

    2017-04-01

    Full Text Available Neurofeedback is a promising tool for brain rehabilitation and peak performance training. Neurofeedback approaches usually rely on a single brain imaging modality such as EEG or fMRI. Combining these modalities for neurofeedback training could allow to provide richer information to the subject and could thus enable him/her to achieve faster and more specific self-regulation. Yet unimodal and multimodal neurofeedback have never been compared before. In the present work, we introduce a simultaneous EEG-fMRI experimental protocol in which participants performed a motor-imagery task in unimodal and bimodal NF conditions. With this protocol we were able to compare for the first time the effects of unimodal EEG-neurofeedback and fMRI-neurofeedback versus bimodal EEG-fMRI-neurofeedback by looking both at EEG and fMRI activations. We also propose a new feedback metaphor for bimodal EEG-fMRI-neurofeedback that integrates both EEG and fMRI signal in a single bi-dimensional feedback (a ball moving in 2D. Such a feedback is intended to relieve the cognitive load of the subject by presenting the bimodal neurofeedback task as a single regulation task instead of two. Additionally, this integrated feedback metaphor gives flexibility on defining a bimodal neurofeedback target. Participants were able to regulate activity in their motor regions in all NF conditions. Moreover, motor activations as revealed by offline fMRI analysis were stronger during EEG-fMRI-neurofeedback than during EEG-neurofeedback. This result suggests that EEG-fMRI-neurofeedback could be more specific or more engaging than EEG-neurofeedback. Our results also suggest that during EEG-fMRI-neurofeedback, participants tended to regulate more the modality that was harder to control. Taken together our results shed first light on the specific mechanisms of bimodal EEG-fMRI-neurofeedback and on its added-value as compared to unimodal EEG-neurofeedback and fMRI-neurofeedback.

  7. Enhancing motor network activity using real-time functional MRI neurofeedback of left premotor cortex

    Directory of Open Access Journals (Sweden)

    Theo Ferreira Marins

    2015-12-01

    Full Text Available Neurofeedback by functional Magnetic Resonance Imaging (fMRI is a technique of potential therapeutic relevance that allows individuals to be aware of their own neurophysiological responses and to voluntarily modulate the activity of specific brain regions, such as the premotor cortex (PMC, important for motor recovery after brain injury. We investigated (i whether healthy human volunteers are able to up-regulate the activity of the left PMC during a right hand finger tapping motor imagery (MI task while receiving continuous fMRI-neurofeedback, and (ii whether successful modulation of brain activity influenced non-targeted motor control regions. During the MI task, participants of the neurofeedback group (NFB received ongoing visual feedback representing the level of fMRI responses within their left PMC. Control (CTL group participants were shown similar visual stimuli, but these were non-contingent on brain activity. Both groups showed equivalent levels of behavioral ratings on arousal and motor imagery, before and during the fMRI protocol. In the NFB, but not in CLT group, brain activation during the last run compared to the first run revealed increased activation in the left PMC. In addition, the NFB group showed increased activation in motor control regions extending beyond the left PMC target area, including the supplementary motor area, basal ganglia and cerebellum. Moreover, in the last run, the NFB group showed stronger activation in the left PMC/inferior frontal gyrus when compared to the CTL group. Our results indicate that modulation of PMC and associated motor control areas can be achieved during a single neurofeedback-fMRI session. These results contribute to a better understanding of the underlying mechanisms of MI-based neurofeedback training, with direct implications for rehabilitation strategies in severe brain disorders, such as stroke.

  8. Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns

    Directory of Open Access Journals (Sweden)

    Andres M. Alvarez-Meza

    2017-10-01

    Full Text Available We introduce Enhanced Kernel-based Relevance Analysis (EKRA that aims to support the automatic identification of brain activity patterns using electroencephalographic recordings. EKRA is a data-driven strategy that incorporates two kernel functions to take advantage of the available joint information, associating neural responses to a given stimulus condition. Regarding this, a Centered Kernel Alignment functional is adjusted to learning the linear projection that best discriminates the input feature set, optimizing the required free parameters automatically. Our approach is carried out in two scenarios: (i feature selection by computing a relevance vector from extracted neural features to facilitating the physiological interpretation of a given brain activity task, and (ii enhanced feature selection to perform an additional transformation of relevant features aiming to improve the overall identification accuracy. Accordingly, we provide an alternative feature relevance analysis strategy that allows improving the system performance while favoring the data interpretability. For the validation purpose, EKRA is tested in two well-known tasks of brain activity: motor imagery discrimination and epileptic seizure detection. The obtained results show that the EKRA approach estimates a relevant representation space extracted from the provided supervised information, emphasizing the salient input features. As a result, our proposal outperforms the state-of-the-art methods regarding brain activity discrimination accuracy with the benefit of enhanced physiological interpretation about the task at hand.

  9. Computer- and Suggestion-based Cognitive Rehabilitation following Acquired Brain Injury

    DEFF Research Database (Denmark)

    Lindeløv, Jonas Kristoffer

    . That is, training does not cause cognitive transfer and thus does not constitute “brain training” or “brain exercise” of any clinical relevance. A larger study found more promising results for a suggestion-based treatment in a hypnotic procedure. Patients improved to above population average in a matter...... of 4-8 hours, making this by far the most effective treatment compared to computer-based training, physical exercise, phamaceuticals, meditation, and attention process training. The contrast between computer-based methods and the hypnotic suggestion treatment may be reflect a more general discrepancy...

  10. Near infrared spectroscopy based brain-computer interface

    Science.gov (United States)

    Ranganatha, Sitaram; Hoshi, Yoko; Guan, Cuntai

    2005-04-01

    A brain-computer interface (BCI) provides users with an alternative output channel other than the normal output path of the brain. BCI is being given much attention recently as an alternate mode of communication and control for the disabled, such as patients suffering from Amyotrophic Lateral Sclerosis (ALS) or "locked-in". BCI may also find applications in military, education and entertainment. Most of the existing BCI systems which rely on the brain's electrical activity use scalp EEG signals. The scalp EEG is an inherently noisy and non-linear signal. The signal is detrimentally affected by various artifacts such as the EOG, EMG, ECG and so forth. EEG is cumbersome to use in practice, because of the need for applying conductive gel, and the need for the subject to be immobile. There is an urgent need for a more accessible interface that uses a more direct measure of cognitive function to control an output device. The optical response of Near Infrared Spectroscopy (NIRS) denoting brain activation can be used as an alternative to electrical signals, with the intention of developing a more practical and user-friendly BCI. In this paper, a new method of brain-computer interface (BCI) based on NIRS is proposed. Preliminary results of our experiments towards developing this system are reported.

  11. Functional but Inefficient Kinesthetic Motor Imagery in Adolescents with Autism Spectrum Disorder

    Science.gov (United States)

    Chen, Ya-Ting; Tsou, Kuo-Su; Chen, Hao-Ling; Wong, Ching-Ching; Fan, Yang-Teng; Wu, Chien-Te

    2018-01-01

    Whether action representation in individuals with autism spectrum disorder (ASD) is deficient remains controversial, as previous studies of action observation or imitation report conflicting results. Here we investigated the characteristics of action representation in adolescents with ASD through motor imagery (MI) using a hand rotation and an…

  12. Multidimensional control using a mobile-phone based brain-muscle-computer interface.

    Science.gov (United States)

    Vernon, Scott; Joshi, Sanjay S

    2011-01-01

    Many well-known brain-computer interfaces measure signals at the brain, and then rely on the brain's ability to learn via operant conditioning in order to control objects in the environment. In our lab, we have been developing brain-muscle-computer interfaces, which measure signals at a single muscle and then rely on the brain's ability to learn neuromuscular skills via operant conditioning. Here, we report a new mobile-phone based brain-muscle-computer interface prototype for severely paralyzed persons, based on previous results from our group showing that humans may actively create specified power levels in two separate frequency bands of a single sEMG signal. Electromyographic activity on the surface of a single face muscle (Auricularis superior) is recorded with a standard electrode. This analog electrical signal is imported into an Android-based mobile phone. User-modulated power in two separate frequency band serves as two separate and simultaneous control channels for machine control. After signal processing, the Android phone sends commands to external devices via Bluetooth. Users are trained to use the device via biofeedback, with simple cursor-to-target activities on the phone screen.

  13. Steady State Visual Evoked Potential Based Brain-Computer Interface for Cognitive Assessment

    DEFF Research Database (Denmark)

    Westergren, Nicolai; Bendtsen, Rasmus L.; Kjær, Troels W.

    2016-01-01

    decline is important. Cognitive decline may be detected using fullyautomated computerized assessment. Such systems will provide inexpensive and widely available screenings of cognitive ability. The aim of this pilot study is to develop a real time steady state visual evoked potential (SSVEP) based brain-computer...... interface (BCI) for neurological cognitive assessment. It is intended for use by patients who suffer from diseases impairing their motor skills, but are still able to control their gaze. Results are based on 11 healthy test subjects. The system performance have an average accuracy of 100% ± 0%. The test...... subjects achieved an information transfer rate (ITR) of 14:64 bits/min ± 7:63 bits=min and a subject test performance of 47:22% ± 34:10%. This study suggests that BCI may be applicable in practice as a computerized cognitive assessment tool. However, many improvements are required for the system...

  14. Balance Training Reduces Brain Activity during Motor Simulation of a Challenging Balance Task in Older Adults: An fMRI Study.

    Science.gov (United States)

    Ruffieux, Jan; Mouthon, Audrey; Keller, Martin; Mouthon, Michaël; Annoni, Jean-Marie; Taube, Wolfgang

    2018-01-01

    Aging is associated with a shift from an automatic to a more cortical postural control strategy, which goes along with deteriorations in postural stability. Although balance training has been shown to effectively counteract these behavioral deteriorations, little is known about the effect of balance training on brain activity during postural tasks in older adults. We, therefore, assessed postural stability and brain activity using fMRI during motor imagery alone (MI) and in combination with action observation (AO; i.e., AO+MI) of a challenging balance task in older adults before and after 5 weeks of balance training. Results showed a nonsignificant trend toward improvements in postural stability after balance training, accompanied by reductions in brain activity during AO+MI of the balance task in areas relevant for postural control, which have been shown to be over-activated in older adults during (simulation of) motor performance, including motor, premotor, and multisensory vestibular areas. This suggests that balance training may reverse the age-related cortical over-activations and lead to changes in the control of upright posture toward the one observed in young adults.

  15. Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study

    Science.gov (United States)

    Jeunet, Camille; Jahanpour, Emilie; Lotte, Fabien

    2016-06-01

    Objective. While promising, electroencephaloraphy based brain-computer interfaces (BCIs) are barely used due to their lack of reliability: 15% to 30% of users are unable to control a BCI. Standard training protocols may be partly responsible as they do not satisfy recommendations from psychology. Our main objective was to determine in practice to what extent standard training protocols impact users’ motor imagery based BCI (MI-BCI) control performance. Approach. We performed two experiments. The first consisted in evaluating the efficiency of a standard BCI training protocol for the acquisition of non-BCI related skills in a BCI-free context, which enabled us to rule out the possible impact of BCIs on the training outcome. Thus, participants (N = 54) were asked to perform simple motor tasks. The second experiment was aimed at measuring the correlations between motor tasks and MI-BCI performance. The ten best and ten worst performers of the first study were recruited for an MI-BCI experiment during which they had to learn to perform two MI tasks. We also assessed users’ spatial ability and pre-training μ rhythm amplitude, as both have been related to MI-BCI performance in the literature. Main results. Around 17% of the participants were unable to learn to perform the motor tasks, which is close to the BCI illiteracy rate. This suggests that standard training protocols are suboptimal for skill teaching. No correlation was found between motor tasks and MI-BCI performance. However, spatial ability played an important role in MI-BCI performance. In addition, once the spatial ability covariable had been controlled for, using an ANCOVA, it appeared that participants who faced difficulty during the first experiment improved during the second while the others did not. Significance. These studies suggest that (1) standard MI-BCI training protocols are suboptimal for skill teaching, (2) spatial ability is confirmed as impacting on MI-BCI performance, and (3) when faced

  16. From assistance towards restoration with epidural brain-computer interfacing.

    Science.gov (United States)

    Gharabaghi, Alireza; Naros, Georgios; Walter, Armin; Grimm, Florian; Schuermeyer, Marc; Roth, Alexander; Bogdan, Martin; Rosenstiel, Wolfgang; Birbaumer, Niels

    2014-01-01

    Today's implanted brain-computer interfaces make direct contact with the brain or even penetrate the tissue, bearing additional risks with regard to safety and stability. What is more, these approaches aim to control prosthetic devices as assistive tools and do not yet strive to become rehabilitative tools for restoring lost motor function. We introduced a less invasive, implantable interface by applying epidural electrocorticography in a chronic stroke survivor with a persistent motor deficit. He was trained to modulate his natural motor-related oscillatory brain activity by receiving online feedback. Epidural recordings of field potentials in the beta-frequency band projecting onto the anatomical hand knob proved most successful in discriminating between the attempt to move the paralyzed hand and to rest. These spectral features allowed for fast and reliable control of the feedback device in an online closed-loop paradigm. Only seven training sessions were required to significantly improve maximum wrist extension. For patients suffering from severe motor deficits, epidural implants may decode and train the brain activity generated during attempts to move with high spatial resolution, thus facilitating specific and high-intensity practice even in the absence of motor control. This would thus transform them from pure assistive devices to restorative tools in the context of reinforcement learning and neurorehabilitation.

  17. "Like the palm of my hands": Motor imagery enhances implicit and explicit visual recognition of one's own hands.

    Science.gov (United States)

    Conson, Massimiliano; Volpicella, Francesco; De Bellis, Francesco; Orefice, Agnese; Trojano, Luigi

    2017-10-01

    A key point in motor imagery literature is that judging hands in palm view recruits sensory-motor information to a higher extent than judging hands in back view, due to the greater biomechanical complexity implied in rotating hands depicted from palm than from back. We took advantage from this solid evidence to test the nature of a phenomenon known as self-advantage, i.e. the advantage in implicitly recognizing self vs. others' hand images. The self-advantage has been actually found when implicitly but not explicitly judging self-hands, likely due to dissociation between implicit and explicit body representations. However, such a finding might be related to the extent to which motor imagery is recruited during implicit and explicit processing of hand images. We tested this hypothesis in two behavioural experiments. In Experiment 1, right-handed participants judged laterality of either self or others' hands, whereas in Experiment 2, an explicit recognition of one's own hands was required. Crucially, in both experiments participants were randomly presented with hand images viewed from back or from palm. The main result of both experiments was the self-advantage when participants judged hands from palm view. This novel finding demonstrate that increasing the "motor imagery load" during processing of self vs. others' hands can elicit a self-advantage in explicit recognition tasks as well. Future studies testing the possible dissociation between implicit and explicit visual body representations should take into account the modulatory effect of motor imagery load on self-hand processing. Copyright © 2017. Published by Elsevier B.V.

  18. Development of motor imagery and anticipatory action planning in children with developmental coordination disorder: A longitudinal approach

    NARCIS (Netherlands)

    Adams, I.L.J.; Lust, J.M.; Wilson, P.H.; Steenbergen, B.

    2017-01-01

    Children with impaired motor coordination (or Development Coordination Disorder - DCD) have difficulty with the predictive control of movements, evidenced by cross-sectional studies that show impaired motor imagery and action planning abilities. What remains unclear is whether this deficit in

  19. Imagery Data Base Facility

    Data.gov (United States)

    Federal Laboratory Consortium — The Imagery Data Base Facility supports AFRL and other government organizations by providing imagery interpretation and analysis to users for data selection, imagery...

  20. Separability of motor imagery of the self from interpretation of motor intentions of others at the single trial level: an EEG study.

    Science.gov (United States)

    Andrade, João; Cecílio, José; Simões, Marco; Sales, Francisco; Castelo-Branco, Miguel

    2017-06-26

    We aimed to investigate the separability of the neural correlates of 2 types of motor imagery, self and third person (actions owned by the participant himself vs. another individual). If possible this would allow for the development of BCI interfaces to train disorders of action and intention understanding beyond simple imitation, such as autism. We used EEG recordings from 20 healthy participants, as well as electrocorticography (ECoG) in one, based on a virtual reality setup. To test feasibility of discrimination between each type of imagery at the single trial level, time-frequency and source analysis were performed and further assessed by data-driven statistical classification using Support Vector Machines. The main observed differences between self-other imagery conditions in topographic maps were found in Frontal and Parieto-Occipital regions, in agreement with the presence of 2 independent non μ related contributions in the low alpha frequency range. ECOG corroborated such separability. Source analysis also showed differences near the temporo-parietal junction and single-trial average classification accuracy between both types of motor imagery was 67 ± 1%, and raised above 70% when 3 trials were used. The single-trial classification accuracy was significantly above chance level for all the participants of this study (p Person MI use distinct electrophysiological mechanisms detectable at the scalp (and ECOG) at the single trial level, with separable levels of involvement of the mirror neuron system in different regions. These observations provide a promising step to develop new BCI training/rehabilitation paradigms for patients with neurodevelopmental disorders of action understanding beyond simple imitation, such as autism, who would benefit from training and anticipation of the perceived intention of others as opposed to own intentions in social contexts.

  1. Performance improvements from imagery:evidence that internal visual imagery is superior to external visual imagery for slalom performance

    Directory of Open Access Journals (Sweden)

    Nichola eCallow

    2013-10-01

    Full Text Available We report three experiments investigating the hypothesis that use of internal visual imagery (IVI would be superior to external visual imagery (EVI for the performance of different slalom-based motor tasks. In Experiment 1, three groups of participants (IVI, EVI, and a control group performed a driving-simulation slalom task. The IVI group achieved significantly quicker lap times than EVI and the control group. In Experiment 2, participants performed a downhill running slalom task under both IVI and EVI conditions. Performance was again quickest in the IVI compared to EVI condition, with no differences in accuracy. Experiment 3 used the same group design as Experiment 1, but with participants performing a downhill ski-slalom task. Results revealed the IVI group to be significantly more accurate than the control group, with no significant differences in time taken to complete the task. These results support the beneficial effects of IVI for slalom-based tasks, and significantly advances our knowledge related to the differential effects of visual imagery perspectives on motor performance.

  2. The impact of goal-oriented task design on neurofeedback learning for brain-computer interface control.

    Science.gov (United States)

    McWhinney, S R; Tremblay, A; Boe, S G; Bardouille, T

    2018-02-01

    Neurofeedback training teaches individuals to modulate brain activity by providing real-time feedback and can be used for brain-computer interface control. The present study aimed to optimize training by maximizing engagement through goal-oriented task design. Participants were shown either a visual display or a robot, where each was manipulated using motor imagery (MI)-related electroencephalography signals. Those with the robot were instructed to quickly navigate grid spaces, as the potential for goal-oriented design to strengthen learning was central to our investigation. Both groups were hypothesized to show increased magnitude of these signals across 10 sessions, with the greatest gains being seen in those navigating the robot due to increased engagement. Participants demonstrated the predicted increase in magnitude, with no differentiation between hemispheres. Participants navigating the robot showed stronger left-hand MI increases than those with the computer display. This is likely due to success being reliant on maintaining strong MI-related signals. While older participants showed stronger signals in early sessions, this trend later reversed, suggesting greater natural proficiency but reduced flexibility. These results demonstrate capacity for modulating neurofeedback using MI over a series of training sessions, using tasks of varied design. Importantly, the more goal-oriented robot control task resulted in greater improvements.

  3. Dissecting hemisphere-specific contributions to visual spatial imagery using parametric brain mapping.

    Science.gov (United States)

    Bien, Nina; Sack, Alexander T

    2014-07-01

    In the current study we aimed to empirically test previously proposed accounts of a division of labour between the left and right posterior parietal cortices during visuospatial mental imagery. The representation of mental images in the brain has been a topic of debate for several decades. Although the posterior parietal cortex is involved bilaterally, previous studies have postulated that hemispheric specialisation might result in a division of labour between the left and right parietal cortices. In the current fMRI study, we used an elaborated version of a behaviourally-controlled spatial imagery paradigm, the mental clock task, which involves mental image generation and a subsequent spatial comparison between two angles. By systematically varying the difference between the two angles that are mentally compared, we induced a symbolic distance effect: smaller differences between the two angles result in higher task difficulty. We employed parametrically weighed brain imaging to reveal brain areas showing a graded activation pattern in accordance with the induced distance effect. The parametric difficulty manipulation influenced behavioural data and brain activation patterns in a similar matter. Moreover, since this difficulty manipulation only starts to play a role from the angle comparison phase onwards, it allows for a top-down dissociation between the initial mental image formation, and the subsequent angle comparison phase of the spatial imagery task. Employing parametrically weighed fMRI analysis enabled us to top-down disentangle brain activation related to mental image formation, and activation reflecting spatial angle comparison. The results provide first empirical evidence for the repeatedly proposed division of labour between the left and right posterior parietal cortices during spatial imagery. Copyright © 2014 Elsevier Inc. All rights reserved.

  4. Motor Imagery Impairment in Postacute Stroke Patients

    Directory of Open Access Journals (Sweden)

    Niclas Braun

    2017-01-01

    Full Text Available Not much is known about how well stroke patients are able to perform motor imagery (MI and which MI abilities are preserved after stroke. We therefore applied three different MI tasks (one mental chronometry task, one mental rotation task, and one EEG-based neurofeedback task to a sample of postacute stroke patients (n=20 and age-matched healthy controls (n=20 for addressing the following questions: First, which of the MI tasks indicate impairment in stroke patients and are impairments restricted to the paretic side? Second, is there a relationship between MI impairment and sensory loss or paresis severity? And third, do the results of the different MI tasks converge? Significant differences between the stroke and control groups were found in all three MI tasks. However, only the mental chronometry task and EEG analysis revealed paresis side-specific effects. Moreover, sensitivity loss contributed to a performance drop in the mental rotation task. The findings indicate that although MI abilities may be impaired after stroke, most patients retain their ability for MI EEG-based neurofeedback. Interestingly, performance in the different MI measures did not strongly correlate, neither in stroke patients nor in healthy controls. We conclude that one MI measure is not sufficient to fully assess an individual’s MI abilities.

  5. An Investigation of the Differences and Similarities between Generated Small-World Networks for Right- and Left-Hand Motor Imageries.

    Science.gov (United States)

    Zhang, Jiang; Li, Yuyao; Chen, Huafu; Ding, Jurong; Yuan, Zhen

    2016-11-04

    In this study, small-world network analysis was performed to identify the similarities and differences between functional brain networks for right- and left-hand motor imageries (MIs). First, Pearson correlation coefficients among the nodes within the functional brain networks from healthy subjects were calculated. Then, small-world network indicators, including the clustering coefficient, the average path length, the global efficiency, the local efficiency, the average node degree, and the small-world index, were generated for the functional brain networks during both right- and left-hand MIs. We identified large differences in the small-world network indicators between the functional networks during MI and in the random networks. More importantly, the functional brain networks underlying the right- and left-hand MIs exhibited similar small-world properties in terms of the clustering coefficient, the average path length, the global efficiency, and the local efficiency. By contrast, the right- and left-hand MI brain networks showed differences in small-world characteristics, including indicators such as the average node degree and the small-world index. Interestingly, our findings also suggested that the differences in the activity intensity and range, the average node degree, and the small-world index of brain networks between the right- and left-hand MIs were associated with the asymmetry of brain functions.

  6. Brain-Computer Interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke

    Directory of Open Access Journals (Sweden)

    Kai Keng eAng

    2014-07-01

    Full Text Available The objective of this study was to investigate the efficacy of an Electroencephalography (EEG-based Motor Imagery (MI Brain-Computer Interface (BCI coupled with a Haptic Knob (HK robot for arm rehabilitation in stroke patients. In this three-arm, single-blind, randomized controlled trial; 21 chronic hemiplegic stroke patients (Fugl-Meyer Motor Assessment (FMMA score 10-50, recruited after pre-screening for MI BCI ability, were randomly allocated to BCI-HK, HK or Standard Arm Therapy (SAT groups. All groups received 18 sessions of intervention over 6 weeks, 3 sessions per week, 90 minutes per session. The BCI-HK group received 1 hour of BCI coupled with HK intervention, and the HK group received 1 hour of HK intervention per session. Both BCI-HK and HK groups received 120 trials of robot-assisted hand grasping and knob manipulation followed by 30 minutes of therapist-assisted arm mobilization. The SAT group received 1.5 hours of therapist-assisted arm mobilization and forearm pronation-supination movements incorporating wrist control and grasp-release functions. In all, 14 males, 7 females, mean age 54.2 years, mean stroke duration 385.1 days, with baseline FMMA score 27.0 were recruited. The primary outcome measure was upper-extremity FMMA scores measured mid-intervention at week 3, end-intervention at week 6, and follow-up at weeks 12 and 24. Seven, 8 and 7 subjects underwent BCI-HK, HK and SAT interventions respectively. FMMA score improved in all groups, but no intergroup differences were found at any time points. Significantly larger motor gains were observed in the BCI-HK group compared to the SAT group at weeks 3, 12 and 24, but motor gains in the HK group did not differ from the SAT group at any time point. In conclusion, BCI-HK is effective, safe, and may have the potential for enhancing motor recovery in chronic stroke when combined with therapist-assisted arm mobilization.

  7. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke.

    Science.gov (United States)

    Ang, Kai Keng; Guan, Cuntai; Phua, Kok Soon; Wang, Chuanchu; Zhou, Longjiang; Tang, Ka Yin; Ephraim Joseph, Gopal J; Kuah, Christopher Wee Keong; Chua, Karen Sui Geok

    2014-01-01

    The objective of this study was to investigate the efficacy of an Electroencephalography (EEG)-based Motor Imagery (MI) Brain-Computer Interface (BCI) coupled with a Haptic Knob (HK) robot for arm rehabilitation in stroke patients. In this three-arm, single-blind, randomized controlled trial; 21 chronic hemiplegic stroke patients (Fugl-Meyer Motor Assessment (FMMA) score 10-50), recruited after pre-screening for MI BCI ability, were randomly allocated to BCI-HK, HK or Standard Arm Therapy (SAT) groups. All groups received 18 sessions of intervention over 6 weeks, 3 sessions per week, 90 min per session. The BCI-HK group received 1 h of BCI coupled with HK intervention, and the HK group received 1 h of HK intervention per session. Both BCI-HK and HK groups received 120 trials of robot-assisted hand grasping and knob manipulation followed by 30 min of therapist-assisted arm mobilization. The SAT group received 1.5 h of therapist-assisted arm mobilization and forearm pronation-supination movements incorporating wrist control and grasp-release functions. In all, 14 males, 7 females, mean age 54.2 years, mean stroke duration 385.1 days, with baseline FMMA score 27.0 were recruited. The primary outcome measure was upper extremity FMMA scores measured mid-intervention at week 3, end-intervention at week 6, and follow-up at weeks 12 and 24. Seven, 8 and 7 subjects underwent BCI-HK, HK and SAT interventions respectively. FMMA score improved in all groups, but no intergroup differences were found at any time points. Significantly larger motor gains were observed in the BCI-HK group compared to the SAT group at weeks 3, 12, and 24, but motor gains in the HK group did not differ from the SAT group at any time point. In conclusion, BCI-HK is effective, safe, and may have the potential for enhancing motor recovery in chronic stroke when combined with therapist-assisted arm mobilization.

  8. Motor areas of the frontal cortex in patients with motor eloquent brain lesions.

    Science.gov (United States)

    Bulubas, Lucia; Sabih, Jamil; Wohlschlaeger, Afra; Sollmann, Nico; Hauck, Theresa; Ille, Sebastian; Ringel, Florian; Meyer, Bernhard; Krieg, Sandro M

    2016-12-01

    OBJECTIVE Because of its huge clinical potential, the importance of premotor areas for motor function itself and plastic reshaping due to tumors or ischemic brain lesions has received increased attention. Thus, in this study the authors used navigated transcranial magnetic stimulation (nTMS) to investigate whether tumorous brain lesions induce a change in motor cortex localization in the human brain. METHODS Between 2010 and 2013, nTMS motor mapping was performed in a prospective cohort of 100 patients with brain tumors in or adjacent to the rolandic cortex. Spatial data analysis was performed by normalization of the individual motor maps and creation of overlays according to tumor location. Analysis of motor evoked potential (MEP) latencies was performed regarding mean overall latencies and potentially polysynaptic latencies, defined as latencies longer than 1 SD above the mean value. Hemispheric dominance, lesion location, and motor-function deficits were also considered. RESULTS Graphical analysis showed that motor areas were not restricted to the precentral gyrus. Instead, they spread widely in the anterior-posterior direction. An analysis of MEP latency showed that mean MEP latencies were shortest in the precentral gyrus and longest in the superior and middle frontal gyri. The percentage of latencies longer than 1 SD differed widely across gyri. The dominant hemisphere showed a greater number of longer latencies than the nondominant hemisphere (p < 0.0001). Moreover, tumor location-dependent changes in distribution of polysynaptic latencies were observed (p = 0.0002). Motor-function deficit did not show any statistically significant effect. CONCLUSIONS The distribution of primary and polysynaptic motor areas changes in patients with brain tumors and highly depends on tumor location. Thus, these data should be considered for resection planning.

  9. Task-induced frequency modulation features for brain-computer interfacing.

    Science.gov (United States)

    Jayaram, Vinay; Hohmann, Matthias; Just, Jennifer; Schölkopf, Bernhard; Grosse-Wentrup, Moritz

    2017-10-01

    Task-induced amplitude modulation of neural oscillations is routinely used in brain-computer interfaces (BCIs) for decoding subjects' intents, and underlies some of the most robust and common methods in the field, such as common spatial patterns and Riemannian geometry. While there has been some interest in phase-related features for classification, both techniques usually presuppose that the frequencies of neural oscillations remain stable across various tasks. We investigate here whether features based on task-induced modulation of the frequency of neural oscillations enable decoding of subjects' intents with an accuracy comparable to task-induced amplitude modulation. We compare cross-validated classification accuracies using the amplitude and frequency modulated features, as well as a joint feature space, across subjects in various paradigms and pre-processing conditions. We show results with a motor imagery task, a cognitive task, and also preliminary results in patients with amyotrophic lateral sclerosis (ALS), as well as using common spatial patterns and Laplacian filtering. The frequency features alone do not significantly out-perform traditional amplitude modulation features, and in some cases perform significantly worse. However, across both tasks and pre-processing in healthy subjects the joint space significantly out-performs either the frequency or amplitude features alone. This result only does not hold for ALS patients, for whom the dataset is of insufficient size to draw any statistically significant conclusions. Task-induced frequency modulation is robust and straight forward to compute, and increases performance when added to standard amplitude modulation features across paradigms. This allows more information to be extracted from the EEG signal cheaply and can be used throughout the field of BCIs.

  10. Task-induced frequency modulation features for brain-computer interfacing

    Science.gov (United States)

    Jayaram, Vinay; Hohmann, Matthias; Just, Jennifer; Schölkopf, Bernhard; Grosse-Wentrup, Moritz

    2017-10-01

    Objective. Task-induced amplitude modulation of neural oscillations is routinely used in brain-computer interfaces (BCIs) for decoding subjects’ intents, and underlies some of the most robust and common methods in the field, such as common spatial patterns and Riemannian geometry. While there has been some interest in phase-related features for classification, both techniques usually presuppose that the frequencies of neural oscillations remain stable across various tasks. We investigate here whether features based on task-induced modulation of the frequency of neural oscillations enable decoding of subjects’ intents with an accuracy comparable to task-induced amplitude modulation. Approach. We compare cross-validated classification accuracies using the amplitude and frequency modulated features, as well as a joint feature space, across subjects in various paradigms and pre-processing conditions. We show results with a motor imagery task, a cognitive task, and also preliminary results in patients with amyotrophic lateral sclerosis (ALS), as well as using common spatial patterns and Laplacian filtering. Main results. The frequency features alone do not significantly out-perform traditional amplitude modulation features, and in some cases perform significantly worse. However, across both tasks and pre-processing in healthy subjects the joint space significantly out-performs either the frequency or amplitude features alone. This result only does not hold for ALS patients, for whom the dataset is of insufficient size to draw any statistically significant conclusions. Significance. Task-induced frequency modulation is robust and straight forward to compute, and increases performance when added to standard amplitude modulation features across paradigms. This allows more information to be extracted from the EEG signal cheaply and can be used throughout the field of BCIs.

  11. A randomized trial of computer-based communications using imagery and text information to alter representations of heart disease risk and motivate protective behaviour.

    Science.gov (United States)

    Lee, Tarryn J; Cameron, Linda D; Wünsche, Burkhard; Stevens, Carey

    2011-02-01

    Advances in web-based animation technologies provide new opportunities to develop graphic health communications for dissemination throughout communities. We developed imagery and text contents of brief, computer-based programmes about heart disease risk, with both imagery and text contents guided by the common-sense model (CSM) of self-regulation. The imagery depicts a three-dimensional, beating heart tailored to user-specific information. A 2 × 2 × 4 factorial design was used to manipulate concrete imagery (imagery vs. no imagery) and conceptual information (text vs. no text) about heart disease risk in prevention-oriented programmes and assess changes in representations and behavioural motivations from baseline to 2 days, 2 weeks, and 4 weeks post-intervention. Sedentary young adults (N= 80) were randomized to view one of four programmes: imagery plus text, imagery only, text only, or control. Participants completed measures of risk representations, worry, and physical activity and healthy diet intentions and behaviours at baseline, 2 days post-intervention (except behaviours), and 2 weeks (intentions and behaviours only) and 4 weeks later. The imagery contents increased representational beliefs and mental imagery relating to heart disease, worry, and intentions at post-intervention. Increases in sense of coherence (understanding of heart disease) and worry were sustained after 1 month. The imagery contents also increased healthy diet efforts after 2 weeks. The text contents increased beliefs about causal factors, mental images of clogged arteries, and worry at post-intervention, and increased physical activity 2 weeks later and sense of coherence 1 month later. The CSM-based programmes induced short-term changes in risk representations and behaviour motivation. The combination of CSM-based text and imagery appears to be most effective in instilling risk representations that motivate protective behaviour. ©2010 The British Psychological Society.

  12. Design of an EEG-based brain-computer interface (BCI) from standard components running in real-time under Windows.

    Science.gov (United States)

    Guger, C; Schlögl, A; Walterspacher, D; Pfurtscheller, G

    1999-01-01

    An EEG-based brain-computer interface (BCI) is a direct connection between the human brain and the computer. Such a communication system is needed by patients with severe motor impairments (e.g. late stage of Amyotrophic Lateral Sclerosis) and has to operate in real-time. This paper describes the selection of the appropriate components to construct such a BCI and focuses also on the selection of a suitable programming language and operating system. The multichannel system runs under Windows 95, equipped with a real-time Kernel expansion to obtain reasonable real-time operations on a standard PC. Matlab controls the data acquisition and the presentation of the experimental paradigm, while Simulink is used to calculate the recursive least square (RLS) algorithm that describes the current state of the EEG in real-time. First results of the new low-cost BCI show that the accuracy of differentiating imagination of left and right hand movement is around 95%.

  13. Best practice for motor imagery: a systematic literature review on motor imagery training elements in five different disciplines

    Directory of Open Access Journals (Sweden)

    Scheidhauer Anne

    2011-06-01

    Full Text Available Abstract Background The literature suggests a beneficial effect of motor imagery (MI if combined with physical practice, but detailed descriptions of MI training session (MITS elements and temporal parameters are lacking. The aim of this review was to identify the characteristics of a successful MITS and compare these for different disciplines, MI session types, task focus, age, gender and MI modification during intervention. Methods An extended systematic literature search using 24 databases was performed for five disciplines: Education, Medicine, Music, Psychology and Sports. References that described an MI intervention that focused on motor skills, performance or strength improvement were included. Information describing 17 MITS elements was extracted based on the PETTLEP (physical, environment, timing, task, learning, emotion, perspective approach. Seven elements describing the MITS temporal parameters were calculated: study duration, intervention duration, MITS duration, total MITS count, MITS per week, MI trials per MITS and total MI training time. Results Both independent reviewers found 96% congruity, which was tested on a random sample of 20% of all references. After selection, 133 studies reporting 141 MI interventions were included. The locations of the MITS and position of the participants during MI were task-specific. Participants received acoustic detailed MI instructions, which were mostly standardised and live. During MI practice, participants kept their eyes closed. MI training was performed from an internal perspective with a kinaesthetic mode. Changes in MI content, duration and dosage were reported in 31 MI interventions. Familiarisation sessions before the start of the MI intervention were mentioned in 17 reports. MI interventions focused with decreasing relevance on motor-, cognitive- and strength-focused tasks. Average study intervention lasted 34 days, with participants practicing MI on average three times per week for 17

  14. Visual–motor integration and fine motor skills at 6½ years of age and associations with neonatal brain volumes in children born extremely preterm in Sweden: a population-based cohort study

    Science.gov (United States)

    Padilla, Nelly; Forsman, Lea; Broström, Lina; Hellgren, Kerstin; Åden, Ulrika

    2018-01-01

    Objectives This exploratory study aimed to investigate associations between neonatal brain volumes and visual–motor integration (VMI) and fine motor skills in children born extremely preterm (EPT) when they reached 6½ years of age. Setting Prospective population-based cohort study in Stockholm, Sweden, during 3 years. Participants All children born before gestational age, 27 weeks, during 2004–2007 in Stockholm, without major morbidities and impairments, and who underwent MRI at term-equivalent age. Main outcome measures Brain volumes were calculated using morphometric analyses in regions known to be involved in VMI and fine motor functions. VMI was assessed with The Beery-Buktenica Developmental Test of Visual–Motor Integration—sixth edition and fine motor skills were assessed with the manual dexterity subtest from the Movement Assessment Battery for Children—second edition, at 6½ years. Associations between the brain volumes and VMI and fine motor skills were evaluated using partial correlation, adjusted for total cerebral parenchyma and sex. Results Out of 107 children born at gestational age motor skills (r=0.54, P=0.01). Associations were also seen between fine motor skills and the volume of the cerebellum (r=0.42, P=0.02), brainstem (r=0.47, P=0.008) and grey matter (r=−0.38, P=0.04). Conclusions Neonatal brain volumes in areas known to be involved in VMI and fine motor skills were associated with scores for these two functions when children born EPT without major brain lesions or cerebral palsy were evaluated at 6½ years of age. Establishing clear associations between early brain volume alterations and later VMI and/or fine motor skills could make early interventions possible. PMID:29455171

  15. High-Intensity Chronic Stroke Motor Imagery Neurofeedback Training at Home: Three Case Reports.

    Science.gov (United States)

    Zich, Catharina; Debener, Stefan; Schweinitz, Clara; Sterr, Annette; Meekes, Joost; Kranczioch, Cornelia

    2017-11-01

    Motor imagery (MI) with neurofeedback has been suggested as promising for motor recovery after stroke. Evidence suggests that regular training facilitates compensatory plasticity, but frequent training is difficult to integrate into everyday life. Using a wireless electroencephalogram (EEG) system, we implemented a frequent and efficient neurofeedback training at the patients' home. Aiming to overcome maladaptive changes in cortical lateralization patterns we presented a visual feedback, representing the degree of contralateral sensorimotor cortical activity and the degree of sensorimotor cortex lateralization. Three stroke patients practiced every other day, over a period of 4 weeks. Training-related changes were evaluated on behavioral, functional, and structural levels. All 3 patients indicated that they enjoyed the training and were highly motivated throughout the entire training regime. EEG activity induced by MI of the affected hand became more lateralized over the course of training in all three patients. The patient with a significant functional change also showed increased white matter integrity as revealed by diffusion tensor imaging, and a substantial clinical improvement of upper limb motor functions. Our study provides evidence that regular, home-based practice of MI neurofeedback has the potential to facilitate cortical reorganization and may also increase associated improvements of upper limb motor function in chronic stroke patients.

  16. Towards Effective Non-Invasive Brain-Computer Interfaces Dedicated to Gait Rehabilitation Systems

    Directory of Open Access Journals (Sweden)

    Thierry Castermans

    2013-12-01

    Full Text Available In the last few years, significant progress has been made in the field of walk rehabilitation. Motor cortex signals in bipedal monkeys have been interpreted to predict walk kinematics. Epidural electrical stimulation in rats and in one young paraplegic has been realized to partially restore motor control after spinal cord injury. However, these experimental trials are far from being applicable to all patients suffering from motor impairments. Therefore, it is thought that more simple rehabilitation systems are desirable in the meanwhile. The goal of this review is to describe and summarize the progress made in the development of non-invasive brain-computer interfaces dedicated to motor rehabilitation systems. In the first part, the main principles of human locomotion control are presented. The paper then focuses on the mechanisms of supra-spinal centers active during gait, including results from electroencephalography, functional brain imaging technologies [near-infrared spectroscopy (NIRS, functional magnetic resonance imaging (fMRI, positron-emission tomography (PET, single-photon emission-computed tomography (SPECT] and invasive studies. The first brain-computer interface (BCI applications to gait rehabilitation are then presented, with a discussion about the different strategies developed in the field. The challenges to raise for future systems are identified and discussed. Finally, we present some proposals to address these challenges, in order to contribute to the improvement of BCI for gait rehabilitation.

  17. Efficient foot motor control by Neymar’s brain

    Directory of Open Access Journals (Sweden)

    Eiichi eNaito

    2014-08-01

    Full Text Available How very long-term (over many years motor skill training shapes internal motor representation remains poorly understood. We provide valuable evidence that the football brain of Neymar da Silva Santos Júnior (the Brasilian footballer recruits very limited neural resources in the motor-cortical foot regions during foot movements. We scanned his brain activity with a 3-tesla functional magnetic resonance imaging (fMRI while he rotated his right ankle at 1Hz. We also scanned brain activity when three other age-controlled professional footballers, two top-athlete swimmers and one amateur footballer performed the identical task. A comparison was made between Neymar’s brain activity with that obtained from the others. We found activations in the left medial-wall foot motor regions during the foot movements consistently across all participants. However, the size and intensity of medial-wall activity was smaller in the four professional footballers than in the three other participants, despite no difference in amount of foot movement. Surprisingly, the reduced recruitment of medial-wall foot motor regions became apparent in Neymar. His medial-wall activity was smallest among all participants with absolutely no difference in amount of foot movement. Neymar may efficiently control given foot movements probably by largely conserving motor-cortical neural resources. We discuss this possibility in terms of over-years motor skill training effect, use-dependent plasticity, and efficient motor control.

  18. Flashing characters with famous faces improves ERP-based brain-computer interface performance

    Science.gov (United States)

    Kaufmann, T.; Schulz, S. M.; Grünzinger, C.; Kübler, A.

    2011-10-01

    Currently, the event-related potential (ERP)-based spelling device, often referred to as P300-Speller, is the most commonly used brain-computer interface (BCI) for enhancing communication of patients with impaired speech or motor function. Among numerous improvements, a most central feature has received little attention, namely optimizing the stimulus used for eliciting ERPs. Therefore we compared P300-Speller performance with the standard stimulus (flashing characters) against performance with stimuli known for eliciting particularly strong ERPs due to their psychological salience, i.e. flashing familiar faces transparently superimposed on characters. Our results not only indicate remarkably increased ERPs in response to familiar faces but also improved P300-Speller performance due to a significant reduction of stimulus sequences needed for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-Speller.

  19. Self-paced brain-computer interface control of ambulation in a virtual reality environment

    Science.gov (United States)

    Wang, Po T.; King, Christine E.; Chui, Luis A.; Do, An H.; Nenadic, Zoran

    2012-10-01

    Objective. Spinal cord injury (SCI) often leaves affected individuals unable to ambulate. Electroencephalogram (EEG) based brain-computer interface (BCI) controlled lower extremity prostheses may restore intuitive and able-body-like ambulation after SCI. To test its feasibility, the authors developed and tested a novel EEG-based, data-driven BCI system for intuitive and self-paced control of the ambulation of an avatar within a virtual reality environment (VRE). Approach. Eight able-bodied subjects and one with SCI underwent the following 10-min training session: subjects alternated between idling and walking kinaesthetic motor imageries (KMI) while their EEG were recorded and analysed to generate subject-specific decoding models. Subjects then performed a goal-oriented online task, repeated over five sessions, in which they utilized the KMI to control the linear ambulation of an avatar and make ten sequential stops at designated points within the VRE. Main results. The average offline training performance across subjects was 77.2±11.0%, ranging from 64.3% (p = 0.001 76) to 94.5% (p = 6.26×10-23), with chance performance being 50%. The average online performance was 8.5±1.1 (out of 10) successful stops and 303±53 s completion time (perfect = 211 s). All subjects achieved performances significantly different than those of random walk (p prosthesis systems may be feasible.

  20. Electroencephalogram-Based Brain–Computer Interface and Lower-Limb Prosthesis Control: A Case Study

    Directory of Open Access Journals (Sweden)

    Douglas P. Murphy

    2017-12-01

    Full Text Available ObjectiveThe purpose of this study was to establish the feasibility of manipulating a prosthetic knee directly by using a brain–computer interface (BCI system in a transfemoral amputee. Although the other forms of control could be more reliable and quick (e.g., electromyography control, the electroencephalography (EEG-based BCI may provide amputees an alternative way to control a prosthesis directly from brain.MethodsA transfemoral amputee subject was trained to activate a knee-unlocking switch through motor imagery of the movement of his lower extremity. Surface scalp electrodes transmitted brain wave data to a software program that was keyed to activate the switch when the event-related desynchronization in EEG reached a certain threshold. After achieving more than 90% reliability for switch activation by EEG rhythm-feedback training, the subject then progressed to activating the knee-unlocking switch on a prosthesis that turned on a motor and unlocked a prosthetic knee. The project took place in the prosthetic department of a Veterans Administration medical center. The subject walked back and forth in the parallel bars and unlocked the knee for swing phase and for sitting down. The success of knee unlocking through this system was measured. Additionally, the subject filled out a questionnaire on his experiences.ResultsThe success of unlocking the prosthetic knee mechanism ranged from 50 to 100% in eight test segments.ConclusionThe performance of the subject supports the feasibility for BCI control of a lower extremity prosthesis using surface scalp EEG electrodes. Investigating direct brain control in different types of patients is important to promote real-world BCI applications.

  1. Visual-motor integration and fine motor skills at 6½ years of age and associations with neonatal brain volumes in children born extremely preterm in Sweden: a population-based cohort study.

    Science.gov (United States)

    Bolk, Jenny; Padilla, Nelly; Forsman, Lea; Broström, Lina; Hellgren, Kerstin; Åden, Ulrika

    2018-02-17

    This exploratory study aimed to investigate associations between neonatal brain volumes and visual-motor integration (VMI) and fine motor skills in children born extremely preterm (EPT) when they reached 6½ years of age. Prospective population-based cohort study in Stockholm, Sweden, during 3 years. All children born before gestational age, 27 weeks, during 2004-2007 in Stockholm, without major morbidities and impairments, and who underwent MRI at term-equivalent age. Brain volumes were calculated using morphometric analyses in regions known to be involved in VMI and fine motor functions. VMI was assessed with The Beery-Buktenica Developmental Test of Visual-Motor Integration-sixth edition and fine motor skills were assessed with the manual dexterity subtest from the Movement Assessment Battery for Children-second edition, at 6½ years. Associations between the brain volumes and VMI and fine motor skills were evaluated using partial correlation, adjusted for total cerebral parenchyma and sex. Out of 107 children born at gestational age skills (r=0.54, P=0.01). Associations were also seen between fine motor skills and the volume of the cerebellum (r=0.42, P=0.02), brainstem (r=0.47, P=0.008) and grey matter (r=-0.38, P=0.04). Neonatal brain volumes in areas known to be involved in VMI and fine motor skills were associated with scores for these two functions when children born EPT without major brain lesions or cerebral palsy were evaluated at 6½ years of age. Establishing clear associations between early brain volume alterations and later VMI and/or fine motor skills could make early interventions possible. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  2. Neural Correlates of User-initiated Motor Success and Failure - A Brain-Computer Interface Perspective.

    Science.gov (United States)

    Yazmir, Boris; Reiner, Miriam

    2018-05-15

    Any motor action is, by nature, potentially accompanied by human errors. In order to facilitate development of error-tailored Brain-Computer Interface (BCI) correction systems, we focused on internal, human-initiated errors, and investigated EEG correlates of user outcome successes and errors during a continuous 3D virtual tennis game against a computer player. We used a multisensory, 3D, highly immersive environment. Missing and repelling the tennis ball were considered, as 'error' (miss) and 'success' (repel). Unlike most previous studies, where the environment "encouraged" the participant to perform a mistake, here errors happened naturally, resulting from motor-perceptual-cognitive processes of incorrect estimation of the ball kinematics, and can be regarded as user internal, self-initiated errors. Results show distinct and well-defined Event-Related Potentials (ERPs), embedded in the ongoing EEG, that differ across conditions by waveforms, scalp signal distribution maps, source estimation results (sLORETA) and time-frequency patterns, establishing a series of typical features that allow valid discrimination between user internal outcome success and error. The significant delay in latency between positive peaks of error- and success-related ERPs, suggests a cross-talk between top-down and bottom-up processing, represented by an outcome recognition process, in the context of the game world. Success-related ERPs had a central scalp distribution, while error-related ERPs were centro-parietal. The unique characteristics and sharp differences between EEG correlates of error/success provide the crucial components for an improved BCI system. The features of the EEG waveform can be used to detect user action outcome, to be fed into the BCI correction system. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  3. Control of Brain Activity in hMT+/V5 at Three Response Levels Using fMRI-Based Neurofeedback/BCI.

    Science.gov (United States)

    Sousa, Teresa; Direito, Bruno; Lima, João; Ferreira, Carlos; Nunes, Urbano; Castelo-Branco, Miguel

    2016-01-01

    A major challenge in brain-computer interface (BCI) research is to increase the number of command classes and levels of control. BCI studies often use binary control level approaches (level 0 and 1 of brain activation for each class of control). Different classes may often be achieved but not different levels of activation for the same class. The increase in the number of levels of control in BCI applications may allow for larger efficiency in neurofeedback applications. In this work we test the hypothesis whether more than two modulation levels can be achieved in a single brain region, the hMT+/V5 complex. Participants performed three distinct imagery tasks during neurofeedback training: imagery of a stationary dot, imagery of a dot with two opposing motions in the vertical axis and imagery of a dot with four opposing motions in vertical or horizontal axes (imagery of 2 or 4 motion directions). The larger the number of motion alternations, the higher the expected hMT+/V5 response. A substantial number (17 of 20) of participants achieved successful binary level of control and 12 were able to reach even 3 significant levels of control within the same session, confirming the whole group effects at the individual level. With this simple approach we suggest that it is possible to design a parametric system of control based on activity modulation of a specific brain region with at least 3 different levels. Furthermore, we show that particular imagery task instructions, based on different number of motion alternations, provide feasible achievement of different control levels in BCI and/or neurofeedback applications.

  4. Kinesthetic motor imagery training modulates frontal midline theta during imagination of a dart throw.

    Science.gov (United States)

    Weber, E; Doppelmayr, M

    2016-12-01

    Motor imagery (MI) is a frequently used and effective method for motor learning in sports as well as in other domains. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) studies indicated that experts within a certain sport exhibit a more pronounced brain activity during MI as compared to novices. Similar to the execution, during MI the motor sequence has to be planned. Thus, the frontal attentional system, in part represented by the frontal midline theta (4-7Hz), is closely related to these processes and presumably plays a major role in MI as well. In this study, a MI dart training and its impact on frontal midline theta activity (fmt) during MI are examined. 53 healthy subjects with no prior dart experience were randomly allocated to a kinesthetic training group (KinVis) or to a control group (Control). Both groups performed 15 training sessions. While in the KinVis group dart throwing was accompanied by MI, the Control group trained without MI. Dart performance and fmt activity during MI within the first and the 15th session were compared. As expected, the performance increase was more pronounced in the KinVis group. Furthermore, frontal theta amplitude was significantly increased in the KinVis group during MI in the 15th training session as compared to the baseline. These results confirm the effectivity of MI. The enhanced fmt activity in the KinVis group can be interpreted as a better allocation of the requested resources in the frontal attentional network after MI. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training

    DEFF Research Database (Denmark)

    Bender, Thomas; Kjaer, Troels W.; Thomsen, Carsten E.

    2013-01-01

    This paper presents a novel and computationally simple tri-training based semi-supervised steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). It is implemented with autocorrelation-based features and a Naïve-Bayes classifier (NBC). The system uses nine characters...

  6. The neural correlates of visual imagery: A co-ordinate-based meta-analysis.

    Science.gov (United States)

    Winlove, Crawford I P; Milton, Fraser; Ranson, Jake; Fulford, Jon; MacKisack, Matthew; Macpherson, Fiona; Zeman, Adam

    2018-01-02

    Visual imagery is a form of sensory imagination, involving subjective experiences typically described as similar to perception, but which occur in the absence of corresponding external stimuli. We used the Activation Likelihood Estimation algorithm (ALE) to identify regions consistently activated by visual imagery across 40 neuroimaging studies, the first such meta-analysis. We also employed a recently developed multi-modal parcellation of the human brain to attribute stereotactic co-ordinates to one of 180 anatomical regions, the first time this approach has been combined with the ALE algorithm. We identified a total 634 foci, based on measurements from 464 participants. Our overall comparison identified activation in the superior parietal lobule, particularly in the left hemisphere, consistent with the proposed 'top-down' role for this brain region in imagery. Inferior premotor areas and the inferior frontal sulcus were reliably activated, a finding consistent with the prominent semantic demands made by many visual imagery tasks. We observed bilateral activation in several areas associated with the integration of eye movements and visual information, including the supplementary and cingulate eye fields (SCEFs) and the frontal eye fields (FEFs), suggesting that enactive processes are important in visual imagery. V1 was typically activated during visual imagery, even when participants have their eyes closed, consistent with influential depictive theories of visual imagery. Temporal lobe activation was restricted to area PH and regions of the fusiform gyrus, adjacent to the fusiform face complex (FFC). These results provide a secure foundation for future work to characterise in greater detail the functional contributions of specific areas to visual imagery. Copyright © 2017. Published by Elsevier Ltd.

  7. EXPERIMENTAL AND THEORETICAL FOUNDATIONS AND PRACTICAL IMPLEMENTATION OF TECHNOLOGY BRAIN-COMPUTER INTERFACE

    Directory of Open Access Journals (Sweden)

    A. Ya. Kaplan

    2013-01-01

    Full Text Available Technology brain-computer interface (BCI allow saperson to learn how to control external devices via thevoluntary regulation of own EEG directly from the brain without the involvement in the process of nerves and muscles. At the beginning the main goal of BCI was to replace or restore motor function to people disabled by neuromuscular disorders. Currently, the task of designing the BCI increased significantly, more capturing different aspects of life a healthy person. This article discusses the theoretical, experimental and technological base of BCI development and systematized critical fields of real implementation of these technologies.

  8. Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface

    OpenAIRE

    Raza, H; Cecotti, H; Li, Y; Prasad, G

    2015-01-01

    A common assumption in traditional supervised learning is the similar probability distribution of data between the training phase and the testing/operating phase. When transitioning from the training to testing phase, a shift in the probability distribution of input data is known as a covariate shift. Covariate shifts commonly arise in a wide range of real-world systems such as electroencephalogram-based brain–computer interfaces (BCIs). In such systems, there is a necessity for continuous mo...

  9. An associative Brain-Computer-Interface for acute stroke patients

    DEFF Research Database (Denmark)

    Mrachacz-Kersting, Natalie; Stevenson, Andrew James Thomas; Aliakbaryhosseinabadi, Susan

    2016-01-01

    An efficient innovative Brain-Computer-Interface system that empowers chronic stroke patients to control an artificial activation of their lower limb muscle through task specific motor intent has been tested in the past. In the current study it was applied to acute stroke patients. The system...

  10. EEG sensorimotor rhythms' variation and functional connectivity measures during motor imagery: linear relations and classification approaches.

    Science.gov (United States)

    Stefano Filho, Carlos A; Attux, Romis; Castellano, Gabriela

    2017-01-01

    Hands motor imagery (MI) has been reported to alter synchronization patterns amongst neurons, yielding variations in the mu and beta bands' power spectral density (PSD) of the electroencephalography (EEG) signal. These alterations have been used in the field of brain-computer interfaces (BCI), in an attempt to assign distinct MI tasks to commands of such a system. Recent studies have highlighted that information may be missing if knowledge about brain functional connectivity is not considered. In this work, we modeled the brain as a graph in which each EEG electrode represents a node. Our goal was to understand if there exists any linear correlation between variations in the synchronization patterns-that is, variations in the PSD of mu and beta bands-induced by MI and alterations in the corresponding functional networks. Moreover, we (1) explored the feasibility of using functional connectivity parameters as features for a classifier in the context of an MI-BCI; (2) investigated three different types of feature selection (FS) techniques; and (3) compared our approach to a more traditional method using the signal PSD as classifier inputs. Ten healthy subjects participated in this study. We observed significant correlations ( p  < 0.05) with values ranging from 0.4 to 0.9 between PSD variations and functional network alterations for some electrodes, prominently in the beta band. The PSD method performed better for data classification, with mean accuracies of (90 ± 8)% and (87 ± 7)% for the mu and beta band, respectively, versus (83 ± 8)% and (83 ± 7)% for the same bands for the graph method. Moreover, the number of features for the graph method was considerably larger. However, results for both methods were relatively close, and even overlapped when the uncertainties of the accuracy rates were considered. Further investigation regarding a careful exploration of other graph metrics may provide better alternatives.

  11. Are children who play a sport or a musical instrument better at motor imagery than children who do not?

    Science.gov (United States)

    Dey, Abhishikta; Barnsley, Nadia; Mohan, Rahul; McCormick, Marianne; McAuley, James H; Moseley, G Lorimer

    2012-10-01

    Playing a sport or a musical instrument is presumed to improve motor ability. One would therefore predict that children who play a sport or music are better at motor imagery tasks, which rely on an intact cortical proprioceptive representation and precise motor planning, than children who do not. The authors tested this prediction. This study involved an online questionnaire and then a motor imagery task. The task measured the reaction time (RT) and the accuracy for left/right-hand judgements in children aged 5 to 17 years. Forty pictured hands (20 left), held in various positions and rotated zero, 90°, 180° or 270°, were displayed on a screen. Participants indicated whether the displayed hands were left or right by pressing keys on a keyboard. Fifty-seven children (30 boys; mean±SD age=10±3.3 years) participated. The mean±SD RT was 3015.4±1330.0 ms and the accuracy was 73.9±16.6%. There was no difference in RT between children who played sport, music, neither or both (four-level one-way analysis of variance, p=0.85). There was no difference in accuracy between groups either (Kruskal-Wallis, p=0.46). In a secondary analysis, participants whose parents rated them as being 'clumsy' were no slower (n.s.) but were about 25% less accurate than those rated coordinated or very coordinated (pmusic is associated with better cortical proprioceptive representation and motor planning. Secondary analyses suggest that parent-rated clumsiness is negatively related to motor imagery performance.

  12. Tactile acuity is disrupted in osteoarthritis but is unrelated to disruptions in motor imagery performance.

    NARCIS (Netherlands)

    Stanton, T.R.; Lin, C.W.; Bray, H.; Smeets, R.J.P.; Taylor, D.; Law, R.Y.; Moseley, G.L.

    2013-01-01

    OBJECTIVE: To determine whether tactile acuity is disrupted in people with knee OA and to determine whether tactile acuity, a clinical signature of primary sensory cortex representation, is related to motor imagery performance (MIP; evaluates working body schema) and pain. METHODS: Experiment 1:

  13. A computational role for bistability and traveling waves in motor cortex

    Directory of Open Access Journals (Sweden)

    Stewart eHeitmann

    2012-09-01

    Full Text Available Adaptive changes in behavior require rapid changes in brain states yet the brain must also remain stable. We investigated two neural mechanisms for evoking rapid transitions between spatiotemporal synchronization patterns of beta oscillations (13--30Hz in motor cortex. Cortex was modeled as a sheet of neural oscillators that were spatially coupled using a center-surround connection topology. Manipulating the inhibitory surround was found to evoke reliable transitions between synchronous oscillation patterns and traveling waves. These transitions modulated the simulated local field potential in agreement with physiological observations in humans. Intermediate levels of surround inhibition were also found to produce bistable coupling topologies that supported both waves and synchrony. State-dependent perturbation between bistable states produced very rapid transitions but were less reliable. We surmise that motor cortex may thus employ state-dependent computation to achieve very rapid changes between bistable motor states when the demand for speed exceeds the demand for accuracy.

  14. Visuo-motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of sensorimotor rhythms (SMR

    Directory of Open Access Journals (Sweden)

    Eva Maria Hammer

    2014-08-01

    Full Text Available Modulation of sensorimotor rhythms (SMR was suggested as a control signal for brain-computer interfaces (BCI. Yet, there is a population of users estimated between 10 to 50% not able to achieve reliable control and only about 20% of users achieve high (80-100% performance. Predicting performance prior to BCI use would facilitate selection of the most feasible system for an individual, thus constitute a practical benefit for the user, and increase our knowledge about the correlates of BCI control. In a recent study, we predicted SMR-BCI performance from psychological variables that were assessed prior to the BCI sessions and BCI control was supported with machine-learning techniques. We described two significant psychological predictors, namely the visuo-motor coordination ability and the ability to concentrate on the task. The purpose of the current study was to replicate these results thereby validating these predictors within a neurofeedback based SMR-BCI that involved no machine learning. Thirty-three healthy BCI novices participated in a calibration session and three further neurofeedback training sessions. Two variables were related with mean SMR-BCI performance: (1 A measure for the accuracy of fine motor skills, i.e. a trade for a person’s visuo-motor control ability and (2 subject’s attentional impulsivity. In a linear regression they accounted for almost 20% in variance of SMR-BCI performance, but predictor (1 failed significance. Nevertheless, on the basis of our prior regression model for sensorimotor control ability we could predict current SMR-BCI performance with an average prediction error of M = 12.07%. In more than 50% of the participants, the prediction error was smaller than 10%. Hence, psychological variables played a moderate role in predicting SMR-BCI performance in a neurofeedback approach that involved no machine learning. Future studies are needed to further consolidate (or reject the present predictors.

  15. Eye-gaze independent EEG-based brain-computer interfaces for communication

    Science.gov (United States)

    Riccio, A.; Mattia, D.; Simione, L.; Olivetti, M.; Cincotti, F.

    2012-08-01

    The present review systematically examines the literature reporting gaze independent interaction modalities in non-invasive brain-computer interfaces (BCIs) for communication. BCIs measure signals related to specific brain activity and translate them into device control signals. This technology can be used to provide users with severe motor disability (e.g. late stage amyotrophic lateral sclerosis (ALS); acquired brain injury) with an assistive device that does not rely on muscular contraction. Most of the studies on BCIs explored mental tasks and paradigms using visual modality. Considering that in ALS patients the oculomotor control can deteriorate and also other potential users could have impaired visual function, tactile and auditory modalities have been investigated over the past years to seek alternative BCI systems which are independent from vision. In addition, various attentional mechanisms, such as covert attention and feature-directed attention, have been investigated to develop gaze independent visual-based BCI paradigms. Three areas of research were considered in the present review: (i) auditory BCIs, (ii) tactile BCIs and (iii) independent visual BCIs. Out of a total of 130 search results, 34 articles were selected on the basis of pre-defined exclusion criteria. Thirteen articles dealt with independent visual BCIs, 15 reported on auditory BCIs and the last six on tactile BCIs, respectively. From the review of the available literature, it can be concluded that a crucial point is represented by the trade-off between BCI systems/paradigms with high accuracy and speed, but highly demanding in terms of attention and memory load, and systems requiring lower cognitive effort but with a limited amount of communicable information. These issues should be considered as priorities to be explored in future studies to meet users’ requirements in a real-life scenario.

  16. A Study of Various Feature Extraction Methods on a Motor Imagery Based Brain Computer Interface System

    Directory of Open Access Journals (Sweden)

    Seyed Navid Resalat

    2016-01-01

    Discussion: These features were selected for the designed real-time navigation. The corresponding results revealed the subject-specific nature of the MI-based BCI system however, the Power Spectral Density (PSD based &alpha-BP feature had the highest averaged accuracy.

  17. Brain-Computer Interfaces in Medicine

    Science.gov (United States)

    Shih, Jerry J.; Krusienski, Dean J.; Wolpaw, Jonathan R.

    2012-01-01

    Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography-based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function. PMID:22325364

  18. Neural activation and functional connectivity during motor imagery of bimanual everyday actions.

    Directory of Open Access Journals (Sweden)

    André J Szameitat

    Full Text Available Bimanual actions impose intermanual coordination demands not present during unimanual actions. We investigated the functional neuroanatomical correlates of these coordination demands in motor imagery (MI of everyday actions using functional magnetic resonance imaging (fMRI. For this, 17 participants imagined unimanual actions with the left and right hand as well as bimanual actions while undergoing fMRI. A univariate fMRI analysis showed no reliable cortical activations specific to bimanual MI, indicating that intermanual coordination demands in MI are not associated with increased neural processing. A functional connectivity analysis based on psychophysiological interactions (PPI, however, revealed marked increases in connectivity between parietal and premotor areas within and between hemispheres. We conclude that in MI of everyday actions intermanual coordination demands are primarily met by changes in connectivity between areas and only moderately, if at all, by changes in the amount of neural activity. These results are the first characterization of the neuroanatomical correlates of bimanual coordination demands in MI. Our findings support the assumed equivalence of overt and imagined actions and highlight the differences between uni- and bimanual actions. The findings extent our understanding of the motor system and may aid the development of clinical neurorehabilitation approaches based on mental practice.

  19. Dyslexia singular brain

    International Nuclear Information System (INIS)

    Habis, M.; Robichon, F.; Demonet, J.F.

    1996-01-01

    Of late ten years, neurologists are studying the brain of the dyslectics. The cerebral imagery (NMR imaging, positron computed tomography) has allowed to confirm the anatomical particularities discovered by some of them: asymmetry default of cerebral hemispheres, size abnormally large of the white substance mass which connect the two hemispheres. The functional imagery, when visualizing this singular brain at work, allows to understand why it labors to reading. (O.M.)

  20. Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-computer interface

    Directory of Open Access Journals (Sweden)

    Brittany Mei Young

    2014-07-01

    Full Text Available This study aims to examine the changes in task-related brain activity induced by rehabilitative therapy using brain-computer interface (BCI technologies and whether these changes are relevant to functional gains achieved through the use of these therapies. Stroke patients with persistent upper-extremity motor deficits received interventional rehabilitation therapy using a closed-loop neurofeedback BCI device (n=8 or no therapy (n=6. Behavioral assessments using the Stroke Impact Scale, the Action Research Arm Test, and the Nine-Hole Peg Test as well as task-based fMRI scans were conducted before, during, after, and one month after therapy administration or at analogous intervals in the absence of therapy. Laterality Index (LI during finger tapping of each hand were calculated for each time point and assessed for correlation with behavioral outcomes. Brain activity during finger tapping of each hand shifted over the course of BCI therapy but not in the absence of therapy to greater involvement of the non-lesioned hemisphere (and lesser involvement of the stroke-lesioned hemisphere as measured by LI. Moreover, changes from baseline LI values during finger tapping of the impaired hand were correlated with gains in both objective and subjective behavioral measures. These findings suggest that the administration of interventional BCI therapy can induce differential changes in brain activity patterns between the lesioned and nonlesioned hemisphere and that these brain changes are associated with changes in specific motor functions.

  1. Age-Related Differences in Corticospinal Excitability during Observation and Motor Imagery of Balance Tasks.

    Science.gov (United States)

    Mouthon, Audrey A; Ruffieux, Jan; Keller, Martin; Taube, Wolfgang

    2016-01-01

    Postural control declines across adult lifespan. Non-physical balance training has been suggested as an alternative to improve postural control in frail/immobilized elderly people. Previous studies showed that this kind of training can improve balance control in young and older adults. However, it is unclear whether the brain of young and older adults is activated differently during mental simulations of balance tasks. For this purpose, soleus (SOL) and tibialis motor evoked potentials (MEPs) and SOL H-reflexes were elicited while 15 elderly (mean ± SD = 71 ± 4.6 years) and 15 young participants (mean ± SD = 27 ± 4.6 years) mentally simulated static and dynamic balance tasks using motor imagery (MI), action observation (AO) or the combination of AO and MI (AO + MI). Young subjects displayed significant modulations of MEPs that depended on the kind of mental simulation and the postural task. Elderly adults also revealed differences between tasks, but not between mental simulation conditions. Furthermore, the elderly displayed larger MEP facilitation during mental simulation (AGE-GROUP; F (1,28) = 5.9; p = 0.02) in the SOL muscle compared to the young and a task-dependent modulation of the tibialis background electromyography (bEMG) activity. H-reflex amplitudes and bEMG in the SOL showed neither task- nor age-dependent modulation. As neither mental simulation nor balance tasks modulated H-reflexes and bEMG in the SOL muscle, despite large variations in the MEP-amplitudes, there seems to be an age-related change in the internal cortical representation of balance tasks. Moreover, the modulation of the tibialis bEMG in the elderly suggests that aging partially affects the ability to inhibit motor output.

  2. Motor imagery in Asperger syndrome: testing action simulation by the hand laterality task.

    Directory of Open Access Journals (Sweden)

    Massimiliano Conson

    Full Text Available Asperger syndrome (AS is a neurodevelopmental condition within the Autism Spectrum Disorders (ASD characterized by specific difficulties in social interaction, communication and behavioural control. In recent years, it has been suggested that ASD is related to a dysfunction of action simulation processes, but studies employing imitation or action observation tasks provided mixed results. Here, we addressed action simulation processes in adolescents with AS by means of a motor imagery task, the classical hand laterality task (to decide whether a rotated hand image is left or right; mental rotation of letters was also evaluated. As a specific marker of action simulation in hand rotation, we assessed the so-called biomechanical effect, that is the advantage for judging hand pictures showing physically comfortable versus physically awkward positions. We found the biomechanical effect in typically-developing participants but not in participants with AS. Overall performance on both hand laterality and letter rotation tasks, instead, did not differ in the two groups. These findings demonstrated a specific alteration of motor imagery skills in AS. We suggest that impaired mental simulation and imitation of goal-less movements in ASD could be related to shared cognitive mechanisms.

  3. A synergy-based hand control is encoded in human motor cortical areas

    Science.gov (United States)

    Leo, Andrea; Handjaras, Giacomo; Bianchi, Matteo; Marino, Hamal; Gabiccini, Marco; Guidi, Andrea; Scilingo, Enzo Pasquale; Pietrini, Pietro; Bicchi, Antonio; Santello, Marco; Ricciardi, Emiliano

    2016-01-01

    How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses. DOI: http://dx.doi.org/10.7554/eLife.13420.001 PMID:26880543

  4. Sensorless speed control of switched reluctance motor using brain emotional learning based intelligent controller

    International Nuclear Information System (INIS)

    Dehkordi, Behzad Mirzaeian; Parsapoor, Amir; Moallem, Mehdi; Lucas, Caro

    2011-01-01

    In this paper, a brain emotional learning based intelligent controller (BELBIC) is developed to control the switched reluctance motor (SRM) speed. Like other intelligent controllers, BELBIC is model free and is suitable to control nonlinear systems. Motor parameter changes, operating point changes, measurement noise, open circuit fault in one phase and asymmetric phases in SRM are also simulated to show the robustness and superior performance of BELBIC. To compare the BELBIC performance with other intelligent controllers, Fuzzy Logic Controller (FLC) is developed. System responses with BELBIC and FLC are compared. Furthermore, by eliminating the position sensor, a method is introduced to estimate the rotor position. This method is based on Adaptive Neuro Fuzzy Inference System (ANFIS). The estimator inputs are four phase flux linkages. Suggested rotor position estimator is simulated in different conditions. Simulation results confirm the accurate rotor position estimation in different loads and speeds.

  5. Sensorless speed control of switched reluctance motor using brain emotional learning based intelligent controller

    Energy Technology Data Exchange (ETDEWEB)

    Dehkordi, Behzad Mirzaeian, E-mail: mirzaeian@eng.ui.ac.i [Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Hezar-Jerib St., Postal code 8174673441, Isfahan (Iran, Islamic Republic of); Parsapoor, Amir, E-mail: amirparsapoor@yahoo.co [Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Hezar-Jerib St., Postal code 8174673441, Isfahan (Iran, Islamic Republic of); Moallem, Mehdi, E-mail: moallem@cc.iut.ac.i [Department of Electrical Engineering, Isfahan University of Technology, Isfahan (Iran, Islamic Republic of); Lucas, Caro, E-mail: lucas@ut.ac.i [Centre of Excellence for Control and Intelligent Processing, Electrical and Computer Engineering Faculty, College of Engineering, University of Tehran, Tehran (Iran, Islamic Republic of)

    2011-01-15

    In this paper, a brain emotional learning based intelligent controller (BELBIC) is developed to control the switched reluctance motor (SRM) speed. Like other intelligent controllers, BELBIC is model free and is suitable to control nonlinear systems. Motor parameter changes, operating point changes, measurement noise, open circuit fault in one phase and asymmetric phases in SRM are also simulated to show the robustness and superior performance of BELBIC. To compare the BELBIC performance with other intelligent controllers, Fuzzy Logic Controller (FLC) is developed. System responses with BELBIC and FLC are compared. Furthermore, by eliminating the position sensor, a method is introduced to estimate the rotor position. This method is based on Adaptive Neuro Fuzzy Inference System (ANFIS). The estimator inputs are four phase flux linkages. Suggested rotor position estimator is simulated in different conditions. Simulation results confirm the accurate rotor position estimation in different loads and speeds.

  6. Temporal coding of brain patterns for direct limb control in humans

    Directory of Open Access Journals (Sweden)

    Gernot Mueller-Putz

    2010-06-01

    Full Text Available For individuals with a high spinal cord injury (SCI not only the lower limbs, but also the upper extremities are paralyzed. A neuroprosthesis can be used to restore the lost hand and arm function in those tetraplegics. The main problem for this group of individuals, however, is the reduced ability to voluntarily operate device controllers. A Brain-Computer Interface provides a non-manual alternative to conventional input devices by translating brain activity patterns into control commands. We show that the temporal coding of individual mental imagery pattern can be used to control two independent degrees of freedom – grasp and elbow function - of an artificial robotic arm by utilizing a minimum number of EEG scalp electrodes. We describe the procedure from the initial screening to the final application. From eight naïve subjects participating on-line feedback experiments, four were able to voluntarily control an artificial arm by inducing one motor imagery pattern derived from one EEG derivation only.

  7. Early detection of consciousness in patients with acute severe traumatic brain injury.

    Science.gov (United States)

    Edlow, Brian L; Chatelle, Camille; Spencer, Camille A; Chu, Catherine J; Bodien, Yelena G; O'Connor, Kathryn L; Hirschberg, Ronald E; Hochberg, Leigh R; Giacino, Joseph T; Rosenthal, Eric S; Wu, Ona

    2017-09-01

    See Schiff (doi:10.1093/awx209) for a scientific commentary on this article. Patients with acute severe traumatic brain injury may recover consciousness before self-expression. Without behavioural evidence of consciousness at the bedside, clinicians may render an inaccurate prognosis, increasing the likelihood of withholding life-sustaining therapies or denying rehabilitative services. Task-based functional magnetic resonance imaging and electroencephalography techniques have revealed covert consciousness in the chronic setting, but these techniques have not been tested in the intensive care unit. We prospectively enrolled 16 patients admitted to the intensive care unit for acute severe traumatic brain injury to test two hypotheses: (i) in patients who lack behavioural evidence of language expression and comprehension, functional magnetic resonance imaging and electroencephalography detect command-following during a motor imagery task (i.e. cognitive motor dissociation) and association cortex responses during language and music stimuli (i.e. higher-order cortex motor dissociation); and (ii) early responses to these paradigms are associated with better 6-month outcomes on the Glasgow Outcome Scale-Extended. Patients underwent functional magnetic resonance imaging on post-injury Day 9.2 ± 5.0 and electroencephalography on Day 9.8 ± 4.6. At the time of imaging, behavioural evaluation with the Coma Recovery Scale-Revised indicated coma (n = 2), vegetative state (n = 3), minimally conscious state without language (n = 3), minimally conscious state with language (n = 4) or post-traumatic confusional state (n = 4). Cognitive motor dissociation was identified in four patients, including three whose behavioural diagnosis suggested a vegetative state. Higher-order cortex motor dissociation was identified in two additional patients. Complete absence of responses to language, music and motor imagery was only observed in coma patients. In patients with behavioural evidence

  8. Cortical and subcortical mechanisms of brain-machine interfaces.

    Science.gov (United States)

    Marchesotti, Silvia; Martuzzi, Roberto; Schurger, Aaron; Blefari, Maria Laura; Del Millán, José R; Bleuler, Hannes; Blanke, Olaf

    2017-06-01

    Technical advances in the field of Brain-Machine Interfaces (BMIs) enable users to control a variety of external devices such as robotic arms, wheelchairs, virtual entities and communication systems through the decoding of brain signals in real time. Most BMI systems sample activity from restricted brain regions, typically the motor and premotor cortex, with limited spatial resolution. Despite the growing number of applications, the cortical and subcortical systems involved in BMI control are currently unknown at the whole-brain level. Here, we provide a comprehensive and detailed report of the areas active during on-line BMI control. We recorded functional magnetic resonance imaging (fMRI) data while participants controlled an EEG-based BMI inside the scanner. We identified the regions activated during BMI control and how they overlap with those involved in motor imagery (without any BMI control). In addition, we investigated which regions reflect the subjective sense of controlling a BMI, the sense of agency for BMI-actions. Our data revealed an extended cortical-subcortical network involved in operating a motor-imagery BMI. This includes not only sensorimotor regions but also the posterior parietal cortex, the insula and the lateral occipital cortex. Interestingly, the basal ganglia and the anterior cingulate cortex were involved in the subjective sense of controlling the BMI. These results inform basic neuroscience by showing that the mechanisms of BMI control extend beyond sensorimotor cortices. This knowledge may be useful for the development of BMIs that offer a more natural and embodied feeling of control for the user. Hum Brain Mapp 38:2971-2989, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  9. Hand grips strength effect on motor function in human brain using fMRI: a pilot study

    International Nuclear Information System (INIS)

    Ismail, S S; Mohamad, M; Syazarina, S O; Nafisah, W Y

    2014-01-01

    Several methods of motor tasks for fMRI scanning have been evolving from simple to more complex tasks. Motor tasks on upper extremity were applied in order to excite the increscent of motor activation on contralesional and ipsilateral hemispheres in brain. The main objective of this study is to study the different conditions for motor tasks on upper extremity that affected the brain activation. Ten healthy right handed with normal vision (3 male and 7 female, age range=20-30 years, mean=24.6 years, SD=2.21) participated in this study. Prior to the scanning, participants were trained on hand grip tasks using rubber ball and pressure gauge tool outside the scanner. During fMRI session, a block design with 30-s task blocks and alternating 30-s rest periods was employed while participants viewed a computer screen via a back projection-mirror system and instructed to follow the instruction by gripping their hand with normal and strong grips using a rubber ball. Statistical Parametric mapping (SPM8) software was used to determine the brain activation. Both tasks activated the primary motor (M1), supplementary motor area (SMA), dorsal and ventral of premotor cortex area (PMA) in left hemisphere while in right hemisphere the area of primary motor (M1) somatosensory was activated. However, the comparison between both tasks revealed that the strong hand grip showed the higher activation at M1, PMA and SMA on left hemisphere and also the area of SMA on right hemisphere. Both conditions of motor tasks could provide insights the functional organization on human brain

  10. Hand grips strength effect on motor function in human brain using fMRI: a pilot study

    Science.gov (United States)

    Ismail, S. S.; Mohamad, M.; Syazarina, S. O.; Nafisah, W. Y.

    2014-11-01

    Several methods of motor tasks for fMRI scanning have been evolving from simple to more complex tasks. Motor tasks on upper extremity were applied in order to excite the increscent of motor activation on contralesional and ipsilateral hemispheres in brain. The main objective of this study is to study the different conditions for motor tasks on upper extremity that affected the brain activation. Ten healthy right handed with normal vision (3 male and 7 female, age range=20-30 years, mean=24.6 years, SD=2.21) participated in this study. Prior to the scanning, participants were trained on hand grip tasks using rubber ball and pressure gauge tool outside the scanner. During fMRI session, a block design with 30-s task blocks and alternating 30-s rest periods was employed while participants viewed a computer screen via a back projection-mirror system and instructed to follow the instruction by gripping their hand with normal and strong grips using a rubber ball. Statistical Parametric mapping (SPM8) software was used to determine the brain activation. Both tasks activated the primary motor (M1), supplementary motor area (SMA), dorsal and ventral of premotor cortex area (PMA) in left hemisphere while in right hemisphere the area of primary motor (M1) somatosensory was activated. However, the comparison between both tasks revealed that the strong hand grip showed the higher activation at M1, PMA and SMA on left hemisphere and also the area of SMA on right hemisphere. Both conditions of motor tasks could provide insights the functional organization on human brain.

  11. Differential activation of brain regions involved with error-feedback and imitation based motor simulation when observing self and an expert's actions in pilots and non-pilots on a complex glider landing task.

    Science.gov (United States)

    Callan, Daniel E; Terzibas, Cengiz; Cassel, Daniel B; Callan, Akiko; Kawato, Mitsuo; Sato, Masa-Aki

    2013-05-15

    In this fMRI study we investigate neural processes related to the action observation network using a complex perceptual-motor task in pilots and non-pilots. The task involved landing a glider (using aileron, elevator, rudder, and dive brake) as close to a target as possible, passively observing a replay of one's own previous trial, passively observing a replay of an expert's trial, and a baseline do nothing condition. The objective of this study is to investigate two types of motor simulation processes used during observation of action: imitation based motor simulation and error-feedback based motor simulation. It has been proposed that the computational neurocircuitry of the cortex is well suited for unsupervised imitation based learning, whereas, the cerebellum is well suited for error-feedback based learning. Consistent with predictions, pilots (to a greater extent than non-pilots) showed significant differential activity when observing an expert landing the glider in brain regions involved with imitation based motor simulation (including premotor cortex PMC, inferior frontal gyrus IFG, anterior insula, parietal cortex, superior temporal gyrus, and middle temporal MT area) than when observing one's own previous trial which showed significant differential activity in the cerebellum (only for pilots) thought to be concerned with error-feedback based motor simulation. While there was some differential brain activity for pilots in regions involved with both Execution and Observation of the flying task (potential Mirror System sites including IFG, PMC, superior parietal lobule) the majority was adjacent to these areas (Observation Only Sites) (predominantly in PMC, IFG, and inferior parietal loblule). These regions showing greater activity for observation than for action may be involved with processes related to motor-based representational transforms that are not necessary when actually carrying out the task. Copyright © 2013 Elsevier Inc. All rights reserved.

  12. Functional and Structural Brain Plasticity Enhanced by Motor and Cognitive Rehabilitation in Multiple Sclerosis

    Directory of Open Access Journals (Sweden)

    Luca Prosperini

    2015-01-01

    Full Text Available Rehabilitation is recognized to be important in ameliorating motor and cognitive functions, reducing disease burden, and improving quality of life in patients with multiple sclerosis (MS. In this systematic review, we summarize the existing evidences that motor and cognitive rehabilitation may enhance functional and structural brain plasticity in patients with MS, as assessed by means of the most advanced neuroimaging techniques, including diffusion tensor imaging and task-related and resting-state functional magnetic resonance imaging (MRI. In most cases, the rehabilitation program was based on computer-assisted/video game exercises performed in either an outpatient or home setting. Despite their heterogeneity, all the included studies describe changes in white matter microarchitecture, in task-related activation, and/or in functional connectivity following both task-oriented and selective training. When explored, relevant correlation between improved function and MRI-detected brain changes was often found, supporting the hypothesis that training-induced brain plasticity is specifically linked to the trained domain. Small sample sizes, lack of randomization and/or an active control group, as well as missed relationship between MRI-detected changes and clinical performance, are the major drawbacks of the selected studies. Knowledge gaps in this field of research are also discussed to provide a framework for future investigations.

  13. Exploring the use of tactile feedback in an ERP-based auditory BCI.

    Science.gov (United States)

    Schreuder, Martijn; Thurlings, Marieke E; Brouwer, Anne-Marie; Van Erp, Jan B F; Tangermann, Michael

    2012-01-01

    Giving direct, continuous feedback on a brain state is common practice in motor imagery based brain-computer interfaces (BCI), but has not been reported for BCIs based on event-related potentials (ERP), where feedback is only given once after a sequence of stimuli. Potentially, direct feedback could allow the user to adjust his strategy during a running trial to obtain the required response. In order to test the usefulness of such feedback, directionally congruent vibrotactile feedback was given during an online auditory BCI experiment. Users received either no feedback, short feedback pulses or continuous feedback. The feedback conditions showed reduced performance both on a behavioral task and in terms of classification accuracy. Several explanations are discussed that give interesting starting points for further research on this topic.

  14. Motor imagery training for children with developmental coordination disorder - study protocol for a randomized controlled trial

    NARCIS (Netherlands)

    Adams, I.L.; Steenbergen, B.; Lust, J.M.; Smits-Engelsman, B.C.

    2016-01-01

    BACKGROUND: Previous studies have shown that the predictive control of movements is impaired in children with Developmental Coordination Disorder (DCD), most likely due to a deficit in the internal modeling of movements. Motor imagery paradigms have been used to test this internal modeling deficit.

  15. Motor imagery training for children with developmental coordination disorder: Study protocol for a randomized controlled trial

    NARCIS (Netherlands)

    Adams, I.L.J.; Steenbergen, B.; Lust, J.M.; Smits-Engelsman, B.C.M.

    2016-01-01

    Background: Previous studies have shown that the predictive control of movements is impaired in children with Developmental Coordination Disorder (DCD), most likely due to a deficit in the internal modeling of movements. Motor imagery paradigms have been used to test this internal modeling deficit.

  16. Mental workload during brain-computer interface training.

    Science.gov (United States)

    Felton, Elizabeth A; Williams, Justin C; Vanderheiden, Gregg C; Radwin, Robert G

    2012-01-01

    It is not well understood how people perceive the difficulty of performing brain-computer interface (BCI) tasks, which specific aspects of mental workload contribute the most, and whether there is a difference in perceived workload between participants who are able-bodied and disabled. This study evaluated mental workload using the NASA Task Load Index (TLX), a multi-dimensional rating procedure with six subscales: Mental Demands, Physical Demands, Temporal Demands, Performance, Effort, and Frustration. Able-bodied and motor disabled participants completed the survey after performing EEG-based BCI Fitts' law target acquisition and phrase spelling tasks. The NASA-TLX scores were similar for able-bodied and disabled participants. For example, overall workload scores (range 0-100) for 1D horizontal tasks were 48.5 (SD = 17.7) and 46.6 (SD 10.3), respectively. The TLX can be used to inform the design of BCIs that will have greater usability by evaluating subjective workload between BCI tasks, participant groups, and control modalities. Mental workload of brain-computer interfaces (BCI) can be evaluated with the NASA Task Load Index (TLX). The TLX is an effective tool for comparing subjective workload between BCI tasks, participant groups (able-bodied and disabled), and control modalities. The data can inform the design of BCIs that will have greater usability.

  17. Comparative neuroimaging in children with cerebral palsy using fMRI and a novel EEG-based brain mapping during a motor task--a preliminary investigation.

    Science.gov (United States)

    Lee, Jae Jin; Lee, Dong Ryul; Shin, Yoon Kyum; Lee, Nam Gi; Han, Bong S; You, Sung Joshua Hyun

    2013-01-01

    The purpose of this study was to compare topographical maps using a novel EEG-based brain mapping system with fMRI in normal and children with cerebral palsy (CP) during a grasping motor task. A normal child (mean ± SD = 13 ± 0 yrs) and four children with CP (mean ± SD = 10.25 ± 2.86 yrs) were recruited from a local community school and medical center. A novel EEG-based brain mapping system with 30 scalp sites (an extension of the 10-20 system) and a 3T MR scanner were used to observe cortical activation patterns during a grasping motor task. Descriptive analysis. In the EEG brain mapping data, the sensorimotor cortex (SMC) and inferior parietal cortex (IPC) were activated in all of the children. The children with CP showed additional activation areas in the premotor cortex (PMC), superior parietal cortex (SPC), and prefrontal cortex (PFC). In the fMRI brain mapping data, SMC activation was observed in all of the children, and the children with CP showed additional activation areas in the PMC and primary somatosensory cortex (PSC). The EEG-based topographical maps were equivalent to the maps obtained from fMRI during the grasping motor task. The results indicate that our novel EEG-based brain mapping system is useful for probing cortical activation patterns in normal children and children with CP.

  18. BrainNetVis: analysis and visualization of brain functional networks.

    Science.gov (United States)

    Tsiaras, Vassilis; Andreou, Dimitris; Tollis, Ioannis G

    2009-01-01

    BrainNetVis is an application, written in Java, that displays and analyzes synchronization networks from brain signals. The program implements a number of network indices and visualization techniques. We demonstrate its use through a case study of left hand and foot motor imagery. The data sets were provided by the Berlin BCI group. Using this program we managed to find differences between the average left hand and foot synchronization networks by comparing them with the average idle state synchronization network.

  19. Noninvasive brain stimulation in neurorehabilitation: Local and distant effects for motor recovery

    Directory of Open Access Journals (Sweden)

    Sook-Lei eLiew

    2014-06-01

    Full Text Available Noninvasive brain stimulation (NIBS may enhance motor recovery after neurological injury through the causal induction of plasticity processes. Neurological injury, such as stroke, often results in serious long-term physical disabilities, and despite intensive therapy, a large majority of brain injury survivors fail to regain full motor function. Emerging research suggests that NIBS techniques, such as transcranial magnetic (TMS and direct current (tDCS stimulation, in association with customarily used neurorehabilitative treatments, may enhance motor recovery. This paper provides a general review on TMS and tDCS paradigms, the mechanisms by which they operate and the stimulation techniques used in neurorehabilitation, specifically stroke. TMS and tDCS influence regional neural activity underlying the stimulation location and also distant interconnected network activity throughout the brain. We discuss recent studies that document NIBS effects on global brain activity measured with various neuroimaging techniques, which help to characterize better strategies for more accurate NIBS stimulation. These rapidly growing areas of inquiry may hold potential for improving the effectiveness of NIBS-based interventions for clinical rehabilitation.

  20. Personality Trait and Facial Expression Filter-Based Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Seongah Chin

    2013-02-01

    Full Text Available In this paper, we present technical approaches that bridge the gap in the research related to the use of brain-computer interfaces for entertainment and facial expressions. Such facial expressions that reflect an individual's personal traits can be used to better realize artificial facial expressions in a gaming environment based on a brain-computer interface. First, an emotion extraction filter is introduced in order to classify emotions on the basis of the users' brain signals in real time. Next, a personality trait filter is defined to classify extrovert and introvert types, which manifest as five traits: very extrovert, extrovert, medium, introvert and very introvert. In addition, facial expressions derived from expression rates are obtained by an extrovert-introvert fuzzy model through its defuzzification process. Finally, we confirm this validation via an analysis of the variance of the personality trait filter, a k-fold cross validation of the emotion extraction filter, an accuracy analysis, a user study of facial synthesis and a test case game.

  1. Physiological basis and image processing in functional magnetic resonance imaging: Neuronal and motor activity in brain

    Directory of Open Access Journals (Sweden)

    Sharma Rakesh

    2004-05-01

    Full Text Available Abstract Functional magnetic resonance imaging (fMRI is recently developing as imaging modality used for mapping hemodynamics of neuronal and motor event related tissue blood oxygen level dependence (BOLD in terms of brain activation. Image processing is performed by segmentation and registration methods. Segmentation algorithms provide brain surface-based analysis, automated anatomical labeling of cortical fields in magnetic resonance data sets based on oxygen metabolic state. Registration algorithms provide geometric features using two or more imaging modalities to assure clinically useful neuronal and motor information of brain activation. This review article summarizes the physiological basis of fMRI signal, its origin, contrast enhancement, physical factors, anatomical labeling by segmentation, registration approaches with examples of visual and motor activity in brain. Latest developments are reviewed for clinical applications of fMRI along with other different neurophysiological and imaging modalities.

  2. Imagery helps in the treatment of epilepsy

    International Nuclear Information System (INIS)

    Mauguiere, F.; Merlet, I.; Ryvlin, P.; Le Bars, D.

    1996-01-01

    The cerebral imagery (NMR imaging, single photon emission computed tomography, positron computed tomography) can be useful in the therapeutic treatment of the epilepsy. Indeed, it allows to delimit the brain part which, in becoming hyper excitable after a cerebral injury is the source of epileptic crises. The surgical ablation is a possible solution to suppress the crises when the anti epileptic drugs are useless. (O.M.)

  3. Spectral-Spatial Differentiation of Brain Activity During Mental Imagery of Improvisational Music Performance Using MEG.

    Science.gov (United States)

    Boasen, Jared; Takeshita, Yuya; Kuriki, Shinya; Yokosawa, Koichi

    2018-01-01

    Group musical improvisation is thought to be akin to conversation, and therapeutically has been shown to be effective at improving communicativeness, sociability, creative expression, and overall psychological health. To understand these therapeutic effects, clarifying the nature of brain activity during improvisational cognition is important. Some insight regarding brain activity during improvisational music cognition has been gained via functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). However, we have found no reports based on magnetoencephalography (MEG). With the present study, we aimed to demonstrate the feasibility of improvisational music performance experimentation in MEG. We designed a novel MEG-compatible keyboard, and used it with experienced musicians ( N = 13) in a music performance paradigm to spectral-spatially differentiate spontaneous brain activity during mental imagery of improvisational music performance. Analyses of source activity revealed that mental imagery of improvisational music performance induced greater theta (5-7 Hz) activity in left temporal areas associated with rhythm production and communication, greater alpha (8-12 Hz) activity in left premotor and parietal areas associated with sensorimotor integration, and less beta (15-29 Hz) activity in right frontal areas associated with inhibition control. These findings support the notion that musical improvisation is conversational, and suggest that creation of novel auditory content is facilitated by a more internally-directed, disinhibited cognitive state.

  4. The association of motor imagery and kinesthetic illusion prolongs the effect of transcranial direct current stimulation on corticospinal tract excitability.

    Science.gov (United States)

    Kaneko, Fuminari; Shibata, Eriko; Hayami, Tatsuya; Nagahata, Keita; Aoyama, Toshiyuki

    2016-04-15

    A kinesthetic illusion induced by a visual stimulus (KI) can produce vivid kinesthetic perception. During KI, corticospinal tract excitability increases and results in the activation of cerebral networks. Transcranial direct current stimulation (tDCS) is emerging as an alternative potential therapeutic modality for a variety of neurological and psychiatric conditions, such that identifying factors that enhance the magnitude and duration of tDCS effects is currently a topic of great scientific interest. This study aimed to establish whether the combination of tDCS with KI and sensory-motor imagery (MI) induces larger and longer-lasting effects on the excitability of corticomotor pathways in healthy Japanese subjects. A total of 21 healthy male volunteers participated in this study. Four interventions were investigated in the first experiment: (1) anodal tDCS alone (tDCSa), (2) anodal tDCS with visually evoked kinesthetic illusion (tDCSa + KI), (3) anodal tDCS with motor imagery (tDCSa + MI), and (4) anodal tDCS with kinesthetic illusion and motor imagery (tDCSa + KIMI). In the second experiment, we added a sham tDCS intervention with kinesthetic illusion and motor imagery (sham + KIMI) as a control for the tDCSa + KIMI condition. Direct currents were applied to the right primary motor cortex. Corticospinal excitability was examined using transcranial magnetic stimulation of the area associated with the left first dorsal interosseous. In the first experiment, corticomotor excitability was sustained for at least 30 min following tDCSa + KIMI (p < 0.01). The effect of tDCSa + KIMI on corticomotor excitability was greater and longer-lasting than that achieved in all other conditions. In the second experiment, significant effects were not achieved following sham + KIMI. Our results suggest that tDCSa + KIMI has a greater therapeutic potential than tDCS alone for inducing higher excitability of the corticospinal tract. The observed

  5. Comparing Features for Classification of MEG Responses to Motor Imagery.

    Directory of Open Access Journals (Sweden)

    Hanna-Leena Halme

    Full Text Available Motor imagery (MI with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest.MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD, Morlet wavelets, short-time Fourier transform (STFT, common spatial patterns (CSP, filter-bank common spatial patterns (FBCSP, spatio-spectral decomposition (SSD, and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject.The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7% and MI-vs-rest (mean 81.3% classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%. There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results.We obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction

  6. Computer screens and brain cancer

    International Nuclear Information System (INIS)

    Wood, A.W.

    1995-01-01

    Australia, both in the media and at the federal government level, over possible links between screen-based computer use and cancer, brain tumour in particular. The screen emissions assumed to be the sources of the putative hazard are the magnetic fields responsible for horizontal and vertical scanning of the display. Time-varying fluctuations in these magnetic fields induce electrical current flows in exposed tissues. This paper estimates that the induced current densities in the brain of the computer user are up to 1 mA/m 2 (due to the vertical flyback). Corresponding values for other electrical appliances or installations are in general much less than this. The epidemiological literature shows no obvious signs of a sudden increase in brain tumour incidence, but the widespread use of computers is a relatively recent phenomenon. The occupational use of other equipment based on cathode ray tubes (such as TV repair) has a much longer history and has been statistically linked to brain tumour in some studies. A number of factors make this an unreliable indicator of the risk from computer screens, however. 42 refs., 3 tabs., 2 figs

  7. The neural basis of kinesthetic and visual imagery in sports: an ALE meta - analysis.

    Science.gov (United States)

    Filgueiras, Alberto; Quintas Conde, Erick Francisco; Hall, Craig R

    2017-12-19

    Imagery is a widely spread technique in the sport sciences that entails the mental rehearsal of a given situation to improve an athlete's learning, performance and motivation. Two modalities of imagery are reported to tap into distinct brain structures, but sharing common components: kinesthetic and visual imagery. This study aimed to investigate the neural basis of those types of imagery with Activation Likelihood Estimation algorithm to perform a meta - analysis. A systematic search was used to retrieve only experimental studies with athletes or sportspersons. Altogether, nine studies were selected and an ALE meta - analysis was performed. Results indicated significant activation of the premotor, somatosensory cortex, supplementary motor areas, inferior and superior parietal lobule, caudate, cingulate and cerebellum in both imagery tasks. It was concluded that visual and kinesthetic imagery share similar neural networks which suggests that combined interventions are beneficial to athletes whereas separate use of those two modalities of imagery may seem less efficient from a neuropsychological approach.

  8. Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity

    Directory of Open Access Journals (Sweden)

    Rosaleena Mohanty

    2018-05-01

    Full Text Available Interventional therapy using brain-computer interface (BCI technology has shown promise in facilitating motor recovery in stroke survivors; however, the impact of this form of intervention on functional networks outside of the motor network specifically is not well-understood. Here, we investigated resting-state functional connectivity (rs-FC in stroke participants undergoing BCI therapy across stages, namely pre- and post-intervention, to identify discriminative functional changes using a machine learning classifier with the goal of categorizing participants into one of the two therapy stages. Twenty chronic stroke participants with persistent upper-extremity motor impairment received neuromodulatory training using a closed-loop neurofeedback BCI device, and rs-functional MRI (rs-fMRI scans were collected at four time points: pre-, mid-, post-, and 1 month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy. In total, 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was applied to reduce the number of features followed by a principal component-based data transformation used by a linear binary support vector machine (SVM classifier to classify each participant into a therapy stage. The SVM classifier achieved a cross-validation accuracy of 92.5% using a leave-one-out method. Outside of the motor network, seeds from the fronto-parietal task control, default mode, subcortical, and visual networks emerged as important contributors to the classification. Furthermore, a higher number of functional changes were observed to be strengthening from the pre- to post-therapy stage than the ones weakening, both of which involved motor and non-motor regions of the brain. These findings may provide new evidence to support the potential clinical utility of BCI therapy as a form of stroke

  9. Non-invasive brain-computer interface system: towards its application as assistive technology.

    Science.gov (United States)

    Cincotti, Febo; Mattia, Donatella; Aloise, Fabio; Bufalari, Simona; Schalk, Gerwin; Oriolo, Giuseppe; Cherubini, Andrea; Marciani, Maria Grazia; Babiloni, Fabio

    2008-04-15

    The quality of life of people suffering from severe motor disabilities can benefit from the use of current assistive technology capable of ameliorating communication, house-environment management and mobility, according to the user's residual motor abilities. Brain-computer interfaces (BCIs) are systems that can translate brain activity into signals that control external devices. Thus they can represent the only technology for severely paralyzed patients to increase or maintain their communication and control options. Here we report on a pilot study in which a system was implemented and validated to allow disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Patients (n=14) with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program carried out in a house-like furnished space. All users utilized regular assistive control options (e.g., microswitches or head trackers). In addition, four subjects learned to operate the system by means of a non-invasive EEG-based BCI. This system was controlled by the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp; this skill was learnt even though the subjects have not had control over their limbs for a long time. We conclude that such a prototype system, which integrates several different assistive technologies including a BCI system, can potentially facilitate the translation from pre-clinical demonstrations to a clinical useful BCI.

  10. User’s Emotions and Usability Study of a Brain-Computer Interface Applied to People with Cerebral Palsy

    Directory of Open Access Journals (Sweden)

    Alejandro Rafael García Ramírez

    2018-02-01

    Full Text Available People with motor and communication disorders face serious challenges in interacting with computers. To enhance this functionality, new human-computer interfaces are being studied. In this work, a brain-computer interface based on the Emotiv Epoc is used to analyze human-computer interactions in cases of cerebral palsy. The Phrase-Composer software was developed to interact with the brain-computer interface. A system usability evaluation was carried out with the participation of three specialists from The Fundação Catarinense de Educação especial (FCEE and four cerebral palsy volunteers. Even though the System Usability Scale (SUS score was acceptable, several challenges remain. Raw electroencephalography (EEG data were also analyzed in order to assess the user’s emotions during their interaction with the communication device. This study brings new evidences about human-computer interaction related to individuals with cerebral palsy.

  11. Brain implants for substituting lost motor function: state of the art and potential impact on the lives of motor-impaired seniors.

    Science.gov (United States)

    Ramsey, N F; Aarnoutse, E J; Vansteensel, M J

    2014-01-01

    Recent scientific achievements bring the concept of neural prosthetics for reinstating lost motor function closer to medical application. Current research involves severely paralyzed people under the age of 65, but implications for seniors with stroke or trauma-induced impairments are clearly on the horizon. Demographic changes will lead to a shortage of personnel to care for an increasing population of senior citizens, threatening maintenance of an acceptable level of care and urging ways for people to live longer at their home independent from personal assistance. This is particularly challenging when people suffer from disabilities such as partial paralysis after stroke or trauma, where daily personal assistance is required. For some of these people, neural prosthetics can reinstate some lost motor function and/or lost communication, thereby increasing independence and possibly quality of life. In this viewpoint article, we present the state of the art in decoding brain activity in the service of brain-computer interfacing. Although some noninvasive applications produce good results, we focus on brain implants that benefit from better quality brain signals. Fully implantable neural prostheses for home use are not available yet, but clinical trials are being prepared. More sophisticated systems are expected to follow in the years to come, with capabilities of interest for less severe paralysis. Eventually the combination of smart robotics and brain implants is expected to enable people to interact well enough with their environment to live an independent life in spite of motor disabilities. © 2014 S. Karger AG, Basel.

  12. Effects of hand orientation on motor imagery--event related potentials suggest kinesthetic motor imagery to solve the hand laterality judgment task.

    Science.gov (United States)

    Jongsma, Marijtje L A; Meulenbroek, Ruud G J; Okely, Judith; Baas, C Marjolein; van der Lubbe, Rob H J; Steenbergen, Bert

    2013-01-01

    Motor imagery (MI) refers to the process of imagining the execution of a specific motor action without actually producing an overt movement. Two forms of MI have been distinguished: visual MI and kinesthetic MI. To distinguish between these forms of MI we employed an event related potential (ERP) study to measure interference effects induced by hand orientation manipulations in a hand laterality judgement task. We hypothesized that this manipulation should only affect kinesthetic MI but not visual MI. The ERPs elicited by rotated hand stimuli contained the classic rotation related negativity (RRN) with respect to palm view stimuli. We observed that laterally rotated stimuli led to a more marked RRN than medially rotated stimuli. This RRN effect was observed when participants had their hands positioned in either a straight (control) or an inward rotated posture, but not when their hands were positioned in an outward rotated posture. Posture effects on the ERP-RRN have not previously been studied. Apparently, a congruent hand posture (hands positioned in an outward rotated fashion) facilitates the judgement of the otherwise more demanding laterally rotated hand stimuli. These ERP findings support a kinesthetic interpretation of MI involved in solving the hand laterality judgement task. The RRN may be used as a non-invasive marker for kinesthetic MI and seems useful in revealing the covert behavior of MI in e.g. rehabilitation programs.

  13. Neuroscience, brains, and computers

    Directory of Open Access Journals (Sweden)

    Giorno Maria Innocenti

    2013-07-01

    Full Text Available This paper addresses the role of the neurosciences in establishing what the brain is and how states of the brain relate to states of the mind. The brain is viewed as a computational deviceperforming operations on symbols. However, the brain is a special purpose computational devicedesigned by evolution and development for survival and reproduction, in close interaction with theenvironment. The hardware of the brain (its structure is very different from that of man-made computers.The computational style of the brain is also very different from traditional computers: the computationalalgorithms, instead of being sets of external instructions, are embedded in brain structure. Concerningthe relationships between brain and mind a number of questions lie ahead. One of them is why andhow, only the human brain grasped the notion of God, probably only at the evolutionary stage attainedby Homo sapiens.

  14. A brain-controlled lower-limb exoskeleton for human gait training

    Science.gov (United States)

    Liu, Dong; Chen, Weihai; Pei, Zhongcai; Wang, Jianhua

    2017-10-01

    Brain-computer interfaces have been a novel approach to translate human intentions into movement commands in robotic systems. This paper describes an electroencephalogram-based brain-controlled lower-limb exoskeleton for gait training, as a proof of concept towards rehabilitation with human-in-the-loop. Instead of using conventional single electroencephalography correlates, e.g., evoked P300 or spontaneous motor imagery, we propose a novel framework integrated two asynchronous signal modalities, i.e., sensorimotor rhythms (SMRs) and movement-related cortical potentials (MRCPs). We executed experiments in a biologically inspired and customized lower-limb exoskeleton where subjects (N = 6) actively controlled the robot using their brain signals. Each subject performed three consecutive sessions composed of offline training, online visual feedback testing, and online robot-control recordings. Post hoc evaluations were conducted including mental workload assessment, feature analysis, and statistics test. An average robot-control accuracy of 80.16% ± 5.44% was obtained with the SMR-based method, while estimation using the MRCP-based method yielded an average performance of 68.62% ± 8.55%. The experimental results showed the feasibility of the proposed framework with all subjects successfully controlled the exoskeleton. The current paradigm could be further extended to paraplegic patients in clinical trials.

  15. Effects of kinesthetic versus visual imagery practice on two technical dance movements: a pilot study.

    Science.gov (United States)

    Girón, Elizabeth Coker; McIsaac, Tara; Nilsen, Dawn

    2012-03-01

    Motor imagery is a type of mental practice that involves imagining the body performing a movement in the absence of motor output. Dance training traditionally incorporates mental practice techniques, but quantitative effects of motor imagery on the performance of dance movements are largely unknown. This pilot study compared the effects of two different imagery modalities, external visual imagery and kinesthetic imagery, on pelvis and hip kinematics during two technical dance movements, plié and sauté. Each of three female dance students (mean age = 19.7 years, mean years of training = 10.7) was assigned to use a type of imagery practice: visual imagery, kinesthetic imagery, or no imagery. Effects of motor imagery on peak external hip rotation varied by both modality and task. Kinesthetic imagery increased peak external hip rotation for pliés, while visual imagery increased peak external hip rotation for sautés. Findings suggest that the success of motor imagery in improving performance may be task-specific. Dancers may benefit from matching imagery modality to technical tasks in order to improve alignment and thereby avoid chronic injury.

  16. Non-invasive brain-to-brain interface (BBI: establishing functional links between two brains.

    Directory of Open Access Journals (Sweden)

    Seung-Schik Yoo

    Full Text Available Transcranial focused ultrasound (FUS is capable of modulating the neural activity of specific brain regions, with a potential role as a non-invasive computer-to-brain interface (CBI. In conjunction with the use of brain-to-computer interface (BCI techniques that translate brain function to generate computer commands, we investigated the feasibility of using the FUS-based CBI to non-invasively establish a functional link between the brains of different species (i.e. human and Sprague-Dawley rat, thus creating a brain-to-brain interface (BBI. The implementation was aimed to non-invasively translate the human volunteer's intention to stimulate a rat's brain motor area that is responsible for the tail movement. The volunteer initiated the intention by looking at a strobe light flicker on a computer display, and the degree of synchronization in the electroencephalographic steady-state-visual-evoked-potentials (SSVEP with respect to the strobe frequency was analyzed using a computer. Increased signal amplitude in the SSVEP, indicating the volunteer's intention, triggered the delivery of a burst-mode FUS (350 kHz ultrasound frequency, tone burst duration of 0.5 ms, pulse repetition frequency of 1 kHz, given for 300 msec duration to excite the motor area of an anesthetized rat transcranially. The successful excitation subsequently elicited the tail movement, which was detected by a motion sensor. The interface was achieved at 94.0±3.0% accuracy, with a time delay of 1.59±1.07 sec from the thought-initiation to the creation of the tail movement. Our results demonstrate the feasibility of a computer-mediated BBI that links central neural functions between two biological entities, which may confer unexplored opportunities in the study of neuroscience with potential implications for therapeutic applications.

  17. Motor sequence learning-induced neural efficiency in functional brain connectivity.

    Science.gov (United States)

    Karim, Helmet T; Huppert, Theodore J; Erickson, Kirk I; Wollam, Mariegold E; Sparto, Patrick J; Sejdić, Ervin; VanSwearingen, Jessie M

    2017-02-15

    Previous studies have shown the functional neural circuitry differences before and after an explicitly learned motor sequence task, but have not assessed these changes during the process of motor skill learning. Functional magnetic resonance imaging activity was measured while participants (n=13) were asked to tap their fingers to visually presented sequences in blocks that were either the same sequence repeated (learning block) or random sequences (control block). Motor learning was associated with a decrease in brain activity during learning compared to control. Lower brain activation was noted in the posterior parietal association area and bilateral thalamus during the later periods of learning (not during the control). Compared to the control condition, we found the task-related motor learning was associated with decreased connectivity between the putamen and left inferior frontal gyrus and left middle cingulate brain regions. Motor learning was associated with changes in network activity, spatial extent, and connectivity. Copyright © 2016 Elsevier B.V. All rights reserved.

  18. Tetrahydrobiopterin in antenatal brain hypoxia-ischemia-induced motor impairments and cerebral palsy.

    Science.gov (United States)

    Vasquez-Vivar, Jeannette; Shi, Zhongjie; Luo, Kehuan; Thirugnanam, Karthikeyan; Tan, Sidhartha

    2017-10-01

    Antenatal brain hypoxia-ischemia, which occurs in cerebral palsy, is considered a significant cause of motor impairments in children. The mechanisms by which antenatal hypoxia-ischemia causes brain injury and motor deficits still need to be elucidated. Tetrahydrobiopterin is an important enzyme cofactor that is necessary to produce neurotransmitters and to maintain the redox status of the brain. A genetic deficiency of this cofactor from mutations of biosynthetic or recycling enzymes is a well-recognized factor in the development of childhood neurological disorders characterized by motor impairments, developmental delay, and encephalopathy. Experimental hypoxia-ischemia causes a decline in the availability of tetrahydrobiopterin in the immature brain. This decline coincides with the loss of brain function, suggesting this occurrence contributes to neuronal dysfunction and motor impairments. One possible mechanism linking tetrahydrobiopterin deficiency, hypoxia-ischemia, and neuronal injury is oxidative injury. Evidence of the central role of the developmental biology of tetrahydrobiopterin in response to hypoxic ischemic brain injury, especially the development of motor deficits, is discussed. Copyright © 2017. Published by Elsevier B.V.

  19. Detecting number processing and mental calculation in patients with disorders of consciousness using a hybrid brain-computer interface system.

    Science.gov (United States)

    Li, Yuanqing; Pan, Jiahui; He, Yanbin; Wang, Fei; Laureys, Steven; Xie, Qiuyou; Yu, Ronghao

    2015-12-15

    For patients with disorders of consciousness such as coma, a vegetative state or a minimally conscious state, one challenge is to detect and assess the residual cognitive functions in their brains. Number processing and mental calculation are important brain functions but are difficult to detect in patients with disorders of consciousness using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised due to the patients' motor impairments and inability to provide sufficient motor responses for number- and calculation-based communication. In this study, we presented a hybrid brain-computer interface that combines P300 and steady state visual evoked potentials to detect number processing and mental calculation in Han Chinese patients with disorders of consciousness. Eleven patients with disorders of consciousness who were in a vegetative state (n = 6) or in a minimally conscious state (n = 3) or who emerged from a minimally conscious state (n = 2) participated in the brain-computer interface-based experiment. During the experiment, the patients with disorders of consciousness were instructed to perform three tasks, i.e., number recognition, number comparison, and mental calculation, including addition and subtraction. In each experimental trial, an arithmetic problem was first presented. Next, two number buttons, only one of which was the correct answer to the problem, flickered at different frequencies to evoke steady state visual evoked potentials, while the frames of the two buttons flashed in a random order to evoke P300 potentials. The patients needed to focus on the target number button (the correct answer). Finally, the brain-computer interface system detected P300 and steady state visual evoked potentials to determine the button to which the patients attended, further presenting the results as feedback. Two of the six patients who were in a vegetative state, one of the three patients who were in a minimally conscious state, and

  20. When music tempo affects the temporal congruence between physical practice and motor imagery.

    Science.gov (United States)

    Debarnot, Ursula; Guillot, Aymeric

    2014-06-01

    When people listen to music, they hear beat and a metrical structure in the rhythm; these perceived patterns enable coordination with the music. A clear correspondence between the tempo of actual movement (e.g., walking) and that of music has been demonstrated, but whether similar coordination occurs during motor imagery is unknown. Twenty participants walked naturally for 8m, either physically or mentally, while listening to slow and fast music, or not listening to anything at all (control condition). Executed and imagined walking times were recorded to assess the temporal congruence between physical practice (PP) and motor imagery (MI). Results showed a difference when comparing slow and fast time conditions, but each of these durations did not differ from soundless condition times, hence showing that body movement may not necessarily change in order to synchronize with music. However, the main finding revealed that the ability to achieve temporal congruence between PP and MI times was altered when listening to either slow or fast music. These data suggest that when physical movement is modulated with respect to the musical tempo, the MI efficacy of the corresponding movement may be affected by the rhythm of the music. Practical applications in sport are discussed as athletes frequently listen to music before competing while they mentally practice their movements to be performed. Copyright © 2014 Elsevier B.V. All rights reserved.

  1. Visual memory and visual mental imagery recruit common control and sensory regions of the brain.

    Science.gov (United States)

    Slotnick, Scott D; Thompson, William L; Kosslyn, Stephen M

    2012-01-01

    Separate lines of research have shown that visual memory and visual mental imagery are mediated by frontal-parietal control regions and can rely on occipital-temporal sensory regions of the brain. We used fMRI to assess the degree to which visual memory and visual mental imagery rely on the same neural substrates. During the familiarization/study phase, participants studied drawings of objects. During the test phase, words corresponding to old and new objects were presented. In the memory test, participants responded "remember," "know," or "new." In the imagery test, participants responded "high vividness," "moderate vividness," or "low vividness." Visual memory (old-remember) and visual imagery (old-high vividness) were commonly associated with activity in frontal-parietal control regions and occipital-temporal sensory regions. In addition, visual memory produced greater activity than visual imagery in parietal and occipital-temporal regions. The present results suggest that visual memory and visual imagery rely on highly similar--but not identical--cognitive processes.

  2. Spectral-Spatial Differentiation of Brain Activity During Mental Imagery of Improvisational Music Performance Using MEG

    Science.gov (United States)

    Boasen, Jared; Takeshita, Yuya; Kuriki, Shinya; Yokosawa, Koichi

    2018-01-01

    Group musical improvisation is thought to be akin to conversation, and therapeutically has been shown to be effective at improving communicativeness, sociability, creative expression, and overall psychological health. To understand these therapeutic effects, clarifying the nature of brain activity during improvisational cognition is important. Some insight regarding brain activity during improvisational music cognition has been gained via functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). However, we have found no reports based on magnetoencephalography (MEG). With the present study, we aimed to demonstrate the feasibility of improvisational music performance experimentation in MEG. We designed a novel MEG-compatible keyboard, and used it with experienced musicians (N = 13) in a music performance paradigm to spectral-spatially differentiate spontaneous brain activity during mental imagery of improvisational music performance. Analyses of source activity revealed that mental imagery of improvisational music performance induced greater theta (5–7 Hz) activity in left temporal areas associated with rhythm production and communication, greater alpha (8–12 Hz) activity in left premotor and parietal areas associated with sensorimotor integration, and less beta (15–29 Hz) activity in right frontal areas associated with inhibition control. These findings support the notion that musical improvisation is conversational, and suggest that creation of novel auditory content is facilitated by a more internally-directed, disinhibited cognitive state. PMID:29740300

  3. Spectral-Spatial Differentiation of Brain Activity During Mental Imagery of Improvisational Music Performance Using MEG

    Directory of Open Access Journals (Sweden)

    Jared Boasen

    2018-04-01

    Full Text Available Group musical improvisation is thought to be akin to conversation, and therapeutically has been shown to be effective at improving communicativeness, sociability, creative expression, and overall psychological health. To understand these therapeutic effects, clarifying the nature of brain activity during improvisational cognition is important. Some insight regarding brain activity during improvisational music cognition has been gained via functional magnetic resonance imaging (fMRI and electroencephalography (EEG. However, we have found no reports based on magnetoencephalography (MEG. With the present study, we aimed to demonstrate the feasibility of improvisational music performance experimentation in MEG. We designed a novel MEG-compatible keyboard, and used it with experienced musicians (N = 13 in a music performance paradigm to spectral-spatially differentiate spontaneous brain activity during mental imagery of improvisational music performance. Analyses of source activity revealed that mental imagery of improvisational music performance induced greater theta (5–7 Hz activity in left temporal areas associated with rhythm production and communication, greater alpha (8–12 Hz activity in left premotor and parietal areas associated with sensorimotor integration, and less beta (15–29 Hz activity in right frontal areas associated with inhibition control. These findings support the notion that musical improvisation is conversational, and suggest that creation of novel auditory content is facilitated by a more internally-directed, disinhibited cognitive state.

  4. Motor network plasticity and low-frequency oscillations abnormalities in patients with brain gliomas: a functional MRI study.

    Directory of Open Access Journals (Sweden)

    Chen Niu

    Full Text Available Brain plasticity is often associated with the process of slow-growing tumor formation, which remodels neural organization and optimizes brain network function. In this study, we aimed to investigate whether motor function plasticity would display deficits in patients with slow-growing brain tumors located in or near motor areas, but who were without motor neurological deficits. We used resting-state functional magnetic resonance imaging to probe motor networks in 15 patients with histopathologically confirmed brain gliomas and 15 age-matched healthy controls. All subjects performed a motor task to help identify individual motor activity in the bilateral primary motor cortex (PMC and supplementary motor area (SMA. Frequency-based analysis at three different frequencies was then used to investigate possible alterations in the power spectral density (PSD of low-frequency oscillations. For each group, the average PSD was determined for each brain region and a nonparametric test was performed to determine the difference in power between the two groups. Significantly reduced inter-hemispheric functional connectivity between the left and right PMC was observed in patients compared with controls (P<0.05. We also found significantly decreased PSD in patients compared to that in controls, in all three frequency bands (low: 0.01-0.02 Hz; middle: 0.02-0.06 Hz; and high: 0.06-0.1 Hz, at three key motor regions. These findings suggest that in asymptomatic patients with brain tumors located in eloquent regions, inter-hemispheric connection may be more vulnerable. A comparison of the two approaches indicated that power spectral analysis is more sensitive than functional connectivity analysis for identifying the neurological abnormalities underlying motor function plasticity induced by slow-growing tumors.

  5. Brain-machine and brain-computer interfaces.

    Science.gov (United States)

    Friehs, Gerhard M; Zerris, Vasilios A; Ojakangas, Catherine L; Fellows, Mathew R; Donoghue, John P

    2004-11-01

    The idea of connecting the human brain to a computer or machine directly is not novel and its potential has been explored in science fiction. With the rapid advances in the areas of information technology, miniaturization and neurosciences there has been a surge of interest in turning fiction into reality. In this paper the authors review the current state-of-the-art of brain-computer and brain-machine interfaces including neuroprostheses. The general principles and requirements to produce a successful connection between human and artificial intelligence are outlined and the authors' preliminary experience with a prototype brain-computer interface is reported.

  6. Equal prefrontal cortex activation between males and females in a motor tasks and different visual imagery perspectives: a functional near-infrared spectroscopy (fNIRS study

    Directory of Open Access Journals (Sweden)

    Thiago F. Dias Kanthack

    2013-09-01

    Full Text Available The purpose of this study was to compare the prefrontal cortex (PFC blood flow variation and time on in males and females while performing a motor task and imagery perspectives. Eighteen right handed subjects (11 males and 7 females were volunteers to this study. All subjects went through three randomly conditions, a motor task condition (MT in which they had to do a simple finger tap. The other conditions included practicing imagery in first and third views. During all the conditions, the fNIRS device was attached to the subject forehead to obtain the blood flow; the total time in each task which was measured with a chronometer. No difference had been found in any condition for both sexes in the PFC and time, nor for all subjects integrated in the PFC. Therefore, we conclu-de that both imageries can be used to mentally train a motor task, and probably both sexes can be benefited.

  7. Fukunaga-Koontz transform based dimensionality reduction for hyperspectral imagery

    Science.gov (United States)

    Ochilov, S.; Alam, M. S.; Bal, A.

    2006-05-01

    Fukunaga-Koontz Transform based technique offers some attractive properties for desired class oriented dimensionality reduction in hyperspectral imagery. In FKT, feature selection is performed by transforming into a new space where feature classes have complimentary eigenvectors. Dimensionality reduction technique based on these complimentary eigenvector analysis can be described under two classes, desired class and background clutter, such that each basis function best represent one class while carrying the least amount of information from the second class. By selecting a few eigenvectors which are most relevant to desired class, one can reduce the dimension of hyperspectral cube. Since the FKT based technique reduces data size, it provides significant advantages for near real time detection applications in hyperspectral imagery. Furthermore, the eigenvector selection approach significantly reduces computation burden via the dimensionality reduction processes. The performance of the proposed dimensionality reduction algorithm has been tested using real-world hyperspectral dataset.

  8. Action Observation and Motor Imagery: Innovative Cognitive Tools in the Rehabilitation of Parkinson’s Disease

    Directory of Open Access Journals (Sweden)

    Giovanni Abbruzzese

    2015-01-01

    Full Text Available Parkinson’s disease (PD is characterized by a progressive impairment of motor skills with deterioration of autonomy in daily living activities. Physiotherapy is regarded as an adjuvant to pharmacological and neurosurgical treatment and may provide small and short-lasting clinical benefits in PD patients. However, the development of innovative rehabilitation approaches with greater long-term efficacy is a major unmet need. Motor imagery (MI and action observation (AO have been recently proposed as a promising rehabilitation tool. MI is the ability to imagine a movement without actual performance (or muscle activation. The same cortical-subcortical network active during motor execution is engaged in MI. The physiological basis of AO is represented by the activation of the “mirror neuron system.” Both MI and AO are involved in motor learning and can induce improvements of motor performance, possibly mediated by the development of plastic changes in the motor cortex. The review of available evidences indicated that MI ability and AO feasibility are substantially preserved in PD subjects. A few preliminary studies suggested the possibility of using MI and AO as parts of rehabilitation protocols for PD patients.

  9. Using EEG/MEG Data of Cognitive Processes in Brain-Computer Interfaces

    International Nuclear Information System (INIS)

    Gutierrez, David

    2008-01-01

    Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using electroencephalographic (EEG) and, more recently, magnetoencephalographic (MEG) measurements of the brain function. Most of the current implementations of BCIs rely on EEG/MEG data of motor activities as such neural processes are well characterized, while the use of data related to cognitive activities has been neglected due to its intrinsic complexity. However, cognitive data usually has larger amplitude, lasts longer and, in some cases, cognitive brain signals are easier to control at will than motor signals. This paper briefy reviews the use of EEG/MEG data of cognitive processes in the implementation of BCIs. Specifically, this paper reviews some of the neuromechanisms, signal features, and processing methods involved. This paper also refers to some of the author's work in the area of detection and classifcation of cognitive signals for BCIs using variability enhancement, parametric modeling, and spatial fltering, as well as recent developments in BCI performance evaluation

  10. Non invasive Brain-Computer Interface system: towards its application as assistive technology

    Science.gov (United States)

    Cincotti, Febo; Mattia, Donatella; Aloise, Fabio; Bufalari, Simona; Schalk, Gerwin; Oriolo, Giuseppe; Cherubini, Andrea; Marciani, Maria Grazia; Babiloni, Fabio

    2010-01-01

    The quality of life of people suffering from severe motor disabilities can benefit from the use of current assistive technology capable of ameliorating communication, house-environment management and mobility, according to the user's residual motor abilities. Brain Computer Interfaces (BCIs) are systems that can translate brain activity into signals that control external devices. Thus they can represent the only technology for severely paralyzed patients to increase or maintain their communication and control options. Here we report on a pilot study in which a system was implemented and validated to allow disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Patients (n=14) with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program carried out in a house-like furnished space. All users utilized regular assistive control options (e.g., microswitches or head trackers). In addition, four subjects learned to operate the system by means of a non-invasive EEG-based BCI. This system was controlled by the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp; this skill was learnt even though the subjects have not had control over their limbs for a long time. We conclude that such a prototype system, which integrates several different assistive technologies including a BCI system, can potentially facilitate the translation from pre-clinical demonstrations to a clinical useful BCI. PMID:18394526

  11. A Symbiotic Brain-Machine Interface through Value-Based Decision Making

    Science.gov (United States)

    Mahmoudi, Babak; Sanchez, Justin C.

    2011-01-01

    Background In the development of Brain Machine Interfaces (BMIs), there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC). Methodology The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc) contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1) and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc. Conclusions Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and reward

  12. A symbiotic brain-machine interface through value-based decision making.

    Directory of Open Access Journals (Sweden)

    Babak Mahmoudi

    Full Text Available BACKGROUND: In the development of Brain Machine Interfaces (BMIs, there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC. METHODOLOGY: The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1 and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc. CONCLUSIONS: Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and

  13. Using non-invasive brain stimulation to augment motor training-induced plasticity

    Directory of Open Access Journals (Sweden)

    Pascual-Leone Alvaro

    2009-03-01

    Full Text Available Abstract Therapies for motor recovery after stroke or traumatic brain injury are still not satisfactory. To date the best approach seems to be the intensive physical therapy. However the results are limited and functional gains are often minimal. The goal of motor training is to minimize functional disability and optimize functional motor recovery. This is thought to be achieved by modulation of plastic changes in the brain. Therefore, adjunct interventions that can augment the response of the motor system to the behavioural training might be useful to enhance the therapy-induced recovery in neurological populations. In this context, noninvasive brain stimulation appears to be an interesting option as an add-on intervention to standard physical therapies. Two non-invasive methods of inducing electrical currents into the brain have proved to be promising for inducing long-lasting plastic changes in motor systems: transcranial magnetic stimulation (TMS and transcranial direct current stimulation (tDCS. These techniques represent powerful methods for priming cortical excitability for a subsequent motor task, demand, or stimulation. Thus, their mutual use can optimize the plastic changes induced by motor practice, leading to more remarkable and outlasting clinical gains in rehabilitation. In this review we discuss how these techniques can enhance the effects of a behavioural intervention and the clinical evidence to date.

  14. Electroencephalography(EEG)-based instinctive brain-control of a quadruped locomotion robot.

    Science.gov (United States)

    Jia, Wenchuan; Huang, Dandan; Luo, Xin; Pu, Huayan; Chen, Xuedong; Bai, Ou

    2012-01-01

    Artificial intelligence and bionic control have been applied in electroencephalography (EEG)-based robot system, to execute complex brain-control task. Nevertheless, due to technical limitations of the EEG decoding, the brain-computer interface (BCI) protocol is often complex, and the mapping between the EEG signal and the practical instructions lack of logic associated, which restrict the user's actual use. This paper presents a strategy that can be used to control a quadruped locomotion robot by user's instinctive action, based on five kinds of movement related neurophysiological signal. In actual use, the user drives or imagines the limbs/wrists action to generate EEG signal to adjust the real movement of the robot according to his/her own motor reflex of the robot locomotion. This method is easy for real use, as the user generates the brain-control signal through the instinctive reaction. By adopting the behavioral control of learning and evolution based on the proposed strategy, complex movement task may be realized by instinctive brain-control.

  15. A review of brain-computer interface games and an opinion survey from researchers, developers and users.

    Science.gov (United States)

    Ahn, Minkyu; Lee, Mijin; Choi, Jinyoung; Jun, Sung Chan

    2014-08-11

    In recent years, research on Brain-Computer Interface (BCI) technology for healthy users has attracted considerable interest, and BCI games are especially popular. This study reviews the current status of, and describes future directions, in the field of BCI games. To this end, we conducted a literature search and found that BCI control paradigms using electroencephalographic signals (motor imagery, P300, steady state visual evoked potential and passive approach reading mental state) have been the primary focus of research. We also conducted a survey of nearly three hundred participants that included researchers, game developers and users around the world. From this survey, we found that all three groups (researchers, developers and users) agreed on the significant influence and applicability of BCI and BCI games, and they all selected prostheses, rehabilitation and games as the most promising BCI applications. User and developer groups tended to give low priority to passive BCI and the whole head sensor array. Developers gave higher priorities to "the easiness of playing" and the "development platform" as important elements for BCI games and the market. Based on our assessment, we discuss the critical point at which BCI games will be able to progress from their current stage to widespread marketing to consumers. In conclusion, we propose three critical elements important for expansion of the BCI game market: standards, gameplay and appropriate integration.

  16. A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users

    Directory of Open Access Journals (Sweden)

    Minkyu Ahn

    2014-08-01

    Full Text Available In recent years, research on Brain-Computer Interface (BCI technology for healthy users has attracted considerable interest, and BCI games are especially popular. This study reviews the current status of, and describes future directions, in the field of BCI games. To this end, we conducted a literature search and found that BCI control paradigms using electroencephalographic signals (motor imagery, P300, steady state visual evoked potential and passive approach reading mental state have been the primary focus of research. We also conducted a survey of nearly three hundred participants that included researchers, game developers and users around the world. From this survey, we found that all three groups (researchers, developers and users agreed on the significant influence and applicability of BCI and BCI games, and they all selected prostheses, rehabilitation and games as the most promising BCI applications. User and developer groups tended to give low priority to passive BCI and the whole head sensor array. Developers gave higher priorities to “the easiness of playing” and the “development platform” as important elements for BCI games and the market. Based on our assessment, we discuss the critical point at which BCI games will be able to progress from their current stage to widespread marketing to consumers. In conclusion, we propose three critical elements important for expansion of the BCI game market: standards, gameplay and appropriate integration.

  17. Classification of brain signals associated with imagination of hand grasping, opening and reaching by means of wavelet-based common spatial pattern and mutual information.

    Science.gov (United States)

    Amanpour, Behzad; Erfanian, Abbas

    2013-01-01

    An important issue in designing a practical brain-computer interface (BCI) is the selection of mental tasks to be imagined. Different types of mental tasks have been used in BCI including left, right, foot, and tongue motor imageries. However, the mental tasks are different from the actions to be controlled by the BCI. It is desirable to select a mental task to be consistent with the desired action to be performed by BCI. In this paper, we investigated the detecting the imagination of the hand grasping, hand opening, and hand reaching in one hand using electroencephalographic (EEG) signals. The results show that the ERD/ERS patterns, associated with the imagination of hand grasping, opening, and reaching are different. For classification of brain signals associated with these mental tasks and feature extraction, a method based on wavelet packet, regularized common spatial pattern (CSP), and mutual information is proposed. The results of an offline analysis on five subjects show that the two-class mental tasks can be classified with an average accuracy of 77.6% using proposed method. In addition, we examine the proposed method on datasets IVa from BCI Competition III and IIa from BCI Competition IV.

  18. GATE: computation code for medical imagery, radiotherapy and dosimetry

    International Nuclear Information System (INIS)

    Jan, S.

    2010-01-01

    The author presents the GATE code, a simulation software based on the Geant4 development environment developed by the CERN (the European organization for nuclear research) which enables Monte-Carlo type simulation to be developed for tomography imagery using ionizing radiation, and radiotherapy examinations (conventional and hadron therapy) to be simulated. The authors concentrate on the use of medical imagery in carcinology. They comment some results obtained in nuclear imagery and in radiotherapy

  19. Region based Brain Computer Interface for a home control application.

    Science.gov (United States)

    Akman Aydin, Eda; Bay, Omer Faruk; Guler, Inan

    2015-08-01

    Environment control is one of the important challenges for disabled people who suffer from neuromuscular diseases. Brain Computer Interface (BCI) provides a communication channel between the human brain and the environment without requiring any muscular activation. The most important expectation for a home control application is high accuracy and reliable control. Region-based paradigm is a stimulus paradigm based on oddball principle and requires selection of a target at two levels. This paper presents an application of region based paradigm for a smart home control application for people with neuromuscular diseases. In this study, a region based stimulus interface containing 49 commands was designed. Five non-disabled subjects were attended to the experiments. Offline analysis results of the experiments yielded 95% accuracy for five flashes. This result showed that region based paradigm can be used to select commands of a smart home control application with high accuracy in the low number of repetitions successfully. Furthermore, a statistically significant difference was not observed between the level accuracies.

  20. RADIOMETRIC CALIBRATION OF MARS HiRISE HIGH RESOLUTION IMAGERY BASED ON FPGA

    Directory of Open Access Journals (Sweden)

    Y. Hou

    2016-06-01

    Full Text Available Due to the large data amount of HiRISE imagery, traditional radiometric calibration method is not able to meet the fast processing requirements. To solve this problem, a radiometric calibration system of HiRISE imagery based on field program gate array (FPGA is designed. The montage gap between two channels caused by gray inconsistency is removed through histogram matching. The calibration system is composed of FPGA and DSP, which makes full use of the parallel processing ability of FPGA and fast computation as well as flexible control characteristic of DSP. Experimental results show that the designed system consumes less hardware resources and the real-time processing ability of radiometric calibration of HiRISE imagery is improved.

  1. Control of a visual keyboard using an electrocorticographic brain-computer interface.

    Science.gov (United States)

    Krusienski, Dean J; Shih, Jerry J

    2011-05-01

    Brain-computer interfaces (BCIs) are devices that enable severely disabled people to communicate and interact with their environments using their brain waves. Most studies investigating BCI in humans have used scalp EEG as the source of electrical signals and focused on motor control of prostheses or computer cursors on a screen. The authors hypothesize that the use of brain signals obtained directly from the cortical surface will more effectively control a communication/spelling task compared to scalp EEG. A total of 6 patients with medically intractable epilepsy were tested for the ability to control a visual keyboard using electrocorticographic (ECOG) signals. ECOG data collected during a P300 visual task paradigm were preprocessed and used to train a linear classifier to subsequently predict the intended target letters. The classifier was able to predict the intended target character at or near 100% accuracy using fewer than 15 stimulation sequences in 5 of the 6 people tested. ECOG data from electrodes outside the language cortex contributed to the classifier and enabled participants to write words on a visual keyboard. This is a novel finding because previous invasive BCI research in humans used signals exclusively from the motor cortex to control a computer cursor or prosthetic device. These results demonstrate that ECOG signals from electrodes both overlying and outside the language cortex can reliably control a visual keyboard to generate language output without voice or limb movements.

  2. Supplementary motor area and primary auditory cortex activation in an expert break-dancer during the kinesthetic motor imagery of dance to music.

    Science.gov (United States)

    Olshansky, Michael P; Bar, Rachel J; Fogarty, Mary; DeSouza, Joseph F X

    2015-01-01

    The current study used functional magnetic resonance imaging to examine the neural activity of an expert dancer with 35 years of break-dancing experience during the kinesthetic motor imagery (KMI) of dance accompanied by highly familiar and unfamiliar music. The goal of this study was to examine the effect of musical familiarity on neural activity underlying KMI within a highly experienced dancer. In order to investigate this in both primary sensory and motor planning cortical areas, we examined the effects of music familiarity on the primary auditory cortex [Heschl's gyrus (HG)] and the supplementary motor area (SMA). Our findings reveal reduced HG activity and greater SMA activity during imagined dance to familiar music compared to unfamiliar music. We propose that one's internal representations of dance moves are influenced by auditory stimuli and may be specific to a dance style and the music accompanying it.

  3. Motor network efficiency and disability in multiple sclerosis

    Science.gov (United States)

    Yaldizli, Özgür; Sethi, Varun; Muhlert, Nils; Liu, Zheng; Samson, Rebecca S.; Altmann, Daniel R.; Ron, Maria A.; Wheeler-Kingshott, Claudia A.M.; Miller, David H.; Chard, Declan T.

    2015-01-01

    Objective: To develop a composite MRI-based measure of motor network integrity, and determine if it explains disability better than conventional MRI measures in patients with multiple sclerosis (MS). Methods: Tract density imaging and constrained spherical deconvolution tractography were used to identify motor network connections in 22 controls. Fractional anisotropy (FA), magnetization transfer ratio (MTR), and normalized volume were computed in each tract in 71 people with relapse onset MS. Principal component analysis was used to distill the FA, MTR, and tract volume data into a single metric for each tract, which in turn was used to compute a composite measure of motor network efficiency (composite NE) using graph theory. Associations were investigated between the Expanded Disability Status Scale (EDSS) and the following MRI measures: composite motor NE, NE calculated using FA alone, FA averaged in the combined motor network tracts, brain T2 lesion volume, brain parenchymal fraction, normal-appearing white matter MTR, and cervical cord cross-sectional area. Results: In univariable analysis, composite motor NE explained 58% of the variation in EDSS in the whole MS group, more than twice that of the other MRI measures investigated. In a multivariable regression model, only composite NE and disease duration were independently associated with EDSS. Conclusions: A composite MRI measure of motor NE was able to predict disability substantially better than conventional non-network-based MRI measures. PMID:26320199

  4. Online LDA BASED brain-computer interface system to aid disabled people

    OpenAIRE

    Apdullah Yayık; Yakup Kutlu

    2017-01-01

    This paper aims to develop brain-computer interface system based on electroencephalography that can aid disabled people in daily life. The system relies on one of the most effective event-related potential wave, P300, which can be elicited by oddball paradigm. Developed application has a basic interaction tool that enables disabled people to convey their needs to other people selecting related objects. These objects pseudo-randomly flash in a visual interface on computer screen. The user must...

  5. Design and evaluation of a motor imagery electroencephalogram-controlled robot system

    Directory of Open Access Journals (Sweden)

    Baoguo Xu

    2015-03-01

    Full Text Available Brain–computer interface provides a new communication channel to control external device by directly translating the brain activity into commands. In this article, as the foundation of electroencephalogram-based robot-assisted upper limb rehabilitation therapy, we report on designing a brain–computer interface–based online robot control system which is made up of electroencephalogram amplifier, acquisition and experimental platform, feature extraction algorithm based on discrete wavelet transform and autoregressive model, linear discriminant analysis classifier, robot control board, and Rhino XR-1 robot. The performance of the system has been tested by 30 participants, and satisfactory results are achieved with an average error rate of 8.5%. Moreover, the advantage of the feature extraction method was further validated by the Graz data set for brain–computer interface competition 2003, and an error rate of 10.0% was obtained. This method provides a useful way for the research of brain–computer interface system and lays a foundation for brain–computer interface–based robotic upper extremity rehabilitation therapy.

  6. Endogenous Sensory Discrimination and Selection by a Fast Brain Switch for a High Transfer Rate Brain-Computer Interface.

    Science.gov (United States)

    Xu, Ren; Jiang, Ning; Dosen, Strahinja; Lin, Chuang; Mrachacz-Kersting, Natalie; Dremstrup, Kim; Farina, Dario

    2016-08-01

    In this study, we present a novel multi-class brain-computer interface (BCI) for communication and control. In this system, the information processing is shared by the algorithm (computer) and the user (human). Specifically, an electro-tactile cycle was presented to the user, providing the choice (class) by delivering timely sensory input. The user discriminated these choices by his/her endogenous sensory ability and selected the desired choice with an intuitive motor task. This selection was detected by a fast brain switch based on real-time detection of movement-related cortical potentials from scalp EEG. We demonstrated the feasibility of such a system with a four-class BCI, yielding a true positive rate of  ∼ 80% and  ∼ 70%, and an information transfer rate of  ∼ 7 bits/min and  ∼ 5 bits/min, for the movement and imagination selection command, respectively. Furthermore, when the system was extended to eight classes, the throughput of the system was improved, demonstrating the capability of accommodating a large number of classes. Combining the endogenous sensory discrimination with the fast brain switch, the proposed system could be an effective, multi-class, gaze-independent BCI system for communication and control applications.

  7. Classifying motor imagery in presence of speech

    NARCIS (Netherlands)

    Gürkök, Hayrettin; Poel, Mannes; Zwiers, Jakob

    2010-01-01

    In the near future, brain-computer interface (BCI) applications for non-disabled users will require multimodal interaction and tolerance to dynamic environment. However, this conflicts with the highly sensitive recording techniques used for BCIs, such as electroencephalography (EEG). Advanced

  8. Regressive Imagery in Creative Problem-Solving: Comparing Verbal Protocols of Expert and Novice Visual Artists and Computer Programmers

    Science.gov (United States)

    Kozbelt, Aaron; Dexter, Scott; Dolese, Melissa; Meredith, Daniel; Ostrofsky, Justin

    2015-01-01

    We applied computer-based text analyses of regressive imagery to verbal protocols of individuals engaged in creative problem-solving in two domains: visual art (23 experts, 23 novices) and computer programming (14 experts, 14 novices). Percentages of words involving primary process and secondary process thought, plus emotion-related words, were…

  9. Population-based, inception cohort study of the incidence, course, and prognosis of mild traumatic brain injury after motor vehicle collisions

    DEFF Research Database (Denmark)

    Cassidy, John David; Boyle, Eleanor; Carroll, Linda J

    2014-01-01

    . PARTICIPANTS: All adults (N=1716) incurring an MTBI in a motor vehicle collision between November 1997 and December 1999 in Saskatchewan. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Age- and sex-stratified incidence rates, time to self-reported recovery, and prognostic factors over a 1-year follow......OBJECTIVE: To determine the incidence, course, and prognosis of adult mild traumatic brain injury (MTBI) caused by motor vehicle collisions. DESIGN: Prospective, population-based, inception cohort study. SETTING: The province of Saskatchewan, Canada, with a population of about 1,000,000 inhabitants...

  10. Enhancing transcranial direct current stimulation via motor imagery and kinesthetic illusion: crossing internal and external tools.

    Science.gov (United States)

    Bodranghien, Florian; Manto, Mario; Lebon, Florent

    2016-06-01

    Transcranial direct current stimulation is a safe technique which is now part of the therapeutic armamentarium for the neuromodulation of motor functions and cognitive operations. It is currently considered that tDCS is an intervention that might promote functional recovery after a lesion in the central nervous system, thus reducing long-term disability and associated socio-economic burden. A recent study shows that kinesthetic illusion and motor imagery prolong the effects of tDCS on corticospinal excitability, overcoming one of the limitations of this intervention. Because changes in excitability anticipate changes in structural plasticity in the CNS, this interesting multi-modal approach might very soon find applications in neurorehabilitation.

  11. Asynchronous P300-based brain-computer interface to control a virtual environment: initial tests on end users.

    Science.gov (United States)

    Aloise, Fabio; Schettini, Francesca; Aricò, Pietro; Salinari, Serenella; Guger, Christoph; Rinsma, Johanna; Aiello, Marco; Mattia, Donatella; Cincotti, Febo

    2011-10-01

    Motor disability and/or ageing can prevent individuals from fully enjoying home facilities, thus worsening their quality of life. Advances in the field of accessible user interfaces for domotic appliances can represent a valuable way to improve the independence of these persons. An asynchronous P300-based Brain-Computer Interface (BCI) system was recently validated with the participation of healthy young volunteers for environmental control. In this study, the asynchronous P300-based BCI for the interaction with a virtual home environment was tested with the participation of potential end-users (clients of a Frisian home care organization) with limited autonomy due to ageing and/or motor disabilities. System testing revealed that the minimum number of stimulation sequences needed to achieve correct classification had a higher intra-subject variability in potential end-users with respect to what was previously observed in young controls. Here we show that the asynchronous modality performed significantly better as compared to the synchronous mode in continuously adapting its speed to the users' state. Furthermore, the asynchronous system modality confirmed its reliability in avoiding misclassifications and false positives, as previously shown in young healthy subjects. The asynchronous modality may contribute to filling the usability gap between BCI systems and traditional input devices, representing an important step towards their use in the activities of daily living.

  12. Using the Hand Laterality Judgement Task to assess motor imagery : a study of practice effects in repeated measurements

    NARCIS (Netherlands)

    Boonstra, Anne M.; de Vries, Sjoerd J.; Veenstra, Evelien; Tepper, Marga; Feenstra, Wya; Otten, Egbert

    The aim of this study was to determine whether there is a practice effect on the Hand Laterality Judgement Task (HLJT). The HLJT task is a mental rotation task that can be used to assess motor imagery ability in stroke patients. Thirty-three healthy individuals performed the HLJT and two control

  13. Visual motion imagery neurofeedback based on the hMT+/V5 complex: evidence for a feedback-specific neural circuit involving neocortical and cerebellar regions

    Science.gov (United States)

    Banca, Paula; Sousa, Teresa; Catarina Duarte, Isabel; Castelo-Branco, Miguel

    2015-12-01

    Objective. Current approaches in neurofeedback/brain-computer interface research often focus on identifying, on a subject-by-subject basis, the neural regions that are best suited for self-driven modulation. It is known that the hMT+/V5 complex, an early visual cortical region, is recruited during explicit and implicit motion imagery, in addition to real motion perception. This study tests the feasibility of training healthy volunteers to regulate the level of activation in their hMT+/V5 complex using real-time fMRI neurofeedback and visual motion imagery strategies. Approach. We functionally localized the hMT+/V5 complex to further use as a target region for neurofeedback. An uniform strategy based on motion imagery was used to guide subjects to neuromodulate hMT+/V5. Main results. We found that 15/20 participants achieved successful neurofeedback. This modulation led to the recruitment of a specific network as further assessed by psychophysiological interaction analysis. This specific circuit, including hMT+/V5, putative V6 and medial cerebellum was activated for successful neurofeedback runs. The putamen and anterior insula were recruited for both successful and non-successful runs. Significance. Our findings indicate that hMT+/V5 is a region that can be modulated by focused imagery and that a specific cortico-cerebellar circuit is recruited during visual motion imagery leading to successful neurofeedback. These findings contribute to the debate on the relative potential of extrinsic (sensory) versus intrinsic (default-mode) brain regions in the clinical application of neurofeedback paradigms. This novel circuit might be a good target for future neurofeedback approaches that aim, for example, the training of focused attention in disorders such as ADHD.

  14. Effect of Error Augmentation on Brain Activation and Motor Learning of a Complex Locomotor Task

    Directory of Open Access Journals (Sweden)

    Laura Marchal-Crespo

    2017-09-01

    indicate that gait-like motor learning depends on interplay between subcortical, cerebellar, and fronto-parietal brain regions. An interesting observation was the low activation observed in the brain's reward system after training with error amplification compared to training without perturbations. Our results suggest that to enhance learning of a locomotor task, errors should be augmented based on subjects' skill level. The impacts of these strategies on motor learning, brain activation, and motivation in neurological patients need further investigation.

  15. Brain-Computer Interface Controlled Functional Electrical Stimulation System for Ankle Movement

    Directory of Open Access Journals (Sweden)

    King Christine E

    2011-08-01

    Full Text Available Abstract Background Many neurological conditions, such as stroke, spinal cord injury, and traumatic brain injury, can cause chronic gait function impairment due to foot-drop. Current physiotherapy techniques provide only a limited degree of motor function recovery in these individuals, and therefore novel therapies are needed. Brain-computer interface (BCI is a relatively novel technology with a potential to restore, substitute, or augment lost motor behaviors in patients with neurological injuries. Here, we describe the first successful integration of a noninvasive electroencephalogram (EEG-based BCI with a noninvasive functional electrical stimulation (FES system that enables the direct brain control of foot dorsiflexion in able-bodied individuals. Methods A noninvasive EEG-based BCI system was integrated with a noninvasive FES system for foot dorsiflexion. Subjects underwent computer-cued epochs of repetitive foot dorsiflexion and idling while their EEG signals were recorded and stored for offline analysis. The analysis generated a prediction model that allowed EEG data to be analyzed and classified in real time during online BCI operation. The real-time online performance of the integrated BCI-FES system was tested in a group of five able-bodied subjects who used repetitive foot dorsiflexion to elicit BCI-FES mediated dorsiflexion of the contralateral foot. Results Five able-bodied subjects performed 10 alternations of idling and repetitive foot dorsifiexion to trigger BCI-FES mediated dorsifiexion of the contralateral foot. The epochs of BCI-FES mediated foot dorsifiexion were highly correlated with the epochs of voluntary foot dorsifiexion (correlation coefficient ranged between 0.59 and 0.77 with latencies ranging from 1.4 sec to 3.1 sec. In addition, all subjects achieved a 100% BCI-FES response (no omissions, and one subject had a single false alarm. Conclusions This study suggests that the integration of a noninvasive BCI with a lower

  16. Brain-computer interface controlled functional electrical stimulation system for ankle movement.

    Science.gov (United States)

    Do, An H; Wang, Po T; King, Christine E; Abiri, Ahmad; Nenadic, Zoran

    2011-08-26

    Many neurological conditions, such as stroke, spinal cord injury, and traumatic brain injury, can cause chronic gait function impairment due to foot-drop. Current physiotherapy techniques provide only a limited degree of motor function recovery in these individuals, and therefore novel therapies are needed. Brain-computer interface (BCI) is a relatively novel technology with a potential to restore, substitute, or augment lost motor behaviors in patients with neurological injuries. Here, we describe the first successful integration of a noninvasive electroencephalogram (EEG)-based BCI with a noninvasive functional electrical stimulation (FES) system that enables the direct brain control of foot dorsiflexion in able-bodied individuals. A noninvasive EEG-based BCI system was integrated with a noninvasive FES system for foot dorsiflexion. Subjects underwent computer-cued epochs of repetitive foot dorsiflexion and idling while their EEG signals were recorded and stored for offline analysis. The analysis generated a prediction model that allowed EEG data to be analyzed and classified in real time during online BCI operation. The real-time online performance of the integrated BCI-FES system was tested in a group of five able-bodied subjects who used repetitive foot dorsiflexion to elicit BCI-FES mediated dorsiflexion of the contralateral foot. Five able-bodied subjects performed 10 alternations of idling and repetitive foot dorsifiexion to trigger BCI-FES mediated dorsifiexion of the contralateral foot. The epochs of BCI-FES mediated foot dorsifiexion were highly correlated with the epochs of voluntary foot dorsifiexion (correlation coefficient ranged between 0.59 and 0.77) with latencies ranging from 1.4 sec to 3.1 sec. In addition, all subjects achieved a 100% BCI-FES response (no omissions), and one subject had a single false alarm. This study suggests that the integration of a noninvasive BCI with a lower-extremity FES system is feasible. With additional modifications

  17. Australian Cerebral Palsy Child Study: protocol of a prospective population based study of motor and brain development of preschool aged children with cerebral palsy.

    Science.gov (United States)

    Boyd, Roslyn N; Jordan, Rachel; Pareezer, Laura; Moodie, Anne; Finn, Christine; Luther, Belinda; Arnfield, Evyn; Pym, Aaron; Craven, Alex; Beall, Paula; Weir, Kelly; Kentish, Megan; Wynter, Meredith; Ware, Robert; Fahey, Michael; Rawicki, Barry; McKinlay, Lynne; Guzzetta, Andrea

    2013-06-11

    describes a large population-based study of early motor development and brain structure in a representative sample of preschool aged children with CP, using direct clinical assessment. The results of this study will be published in peer reviewed journals and presented at relevant international conferences. Australia and New Zealand Clinical Trials Register (ACTRN1261200169820).

  18. Computational and mathematical methods in brain atlasing.

    Science.gov (United States)

    Nowinski, Wieslaw L

    2017-12-01

    Brain atlases have a wide range of use from education to research to clinical applications. Mathematical methods as well as computational methods and tools play a major role in the process of brain atlas building and developing atlas-based applications. Computational methods and tools cover three areas: dedicated editors for brain model creation, brain navigators supporting multiple platforms, and atlas-assisted specific applications. Mathematical methods in atlas building and developing atlas-aided applications deal with problems in image segmentation, geometric body modelling, physical modelling, atlas-to-scan registration, visualisation, interaction and virtual reality. Here I overview computational and mathematical methods in atlas building and developing atlas-assisted applications, and share my contribution to and experience in this field.

  19. Association Between Motor Symptoms and Brain Metabolism in Early Huntington Disease.

    Science.gov (United States)

    Gaura, Véronique; Lavisse, Sonia; Payoux, Pierre; Goldman, Serge; Verny, Christophe; Krystkowiak, Pierre; Damier, Philippe; Supiot, Frédéric; Bachoud-Levi, Anne-Catherine; Remy, Philippe

    2017-09-01

    Brain hypometabolism is associated with the clinical consequences of the degenerative process, but little is known about regional hypermetabolism, sometimes observed in the brain of patients with clinically manifest Huntington disease (HD). Studying the role of regional hypermetabolism is needed to better understand its interaction with the motor symptoms of the disease. To investigate the association between brain hypometabolism and hypermetabolism with motor scores of patients with early HD. This study started in 2001, and analysis was completed in 2016. Sixty symptomatic patients with HD and 15 healthy age-matched control individuals underwent positron emission tomography to measure cerebral metabolism in this cross-sectional study. They also underwent the Unified Huntington's Disease Rating Scale motor test, and 2 subscores were extracted: (1) a hyperkinetic score, combining dystonia and chorea, and (2) a hypokinetic score, combining bradykinesia and rigidity. Statistical parametric mapping software (SPM5) was used to identify all hypo- and hypermetabolic regions in patients with HD relative to control individuals. Correlation analyses (P motor subscores and brain metabolic values were performed for regions with significant hypometabolism and hypermetabolism. Among 60 patients with HD, 22 were women (36.7%), and the mean (SD) age was 44.6 (7.6) years. Of the 15 control individuals, 7 were women (46.7%), and the mean (SD) age was 42.2 (7.3) years. In statistical parametric mapping, striatal hypometabolism was significantly correlated with the severity of all motor scores. Hypermetabolism was negatively correlated only with hypokinetic scores in the cuneus (z score = 3.95, P motor scores were associated with higher metabolic values in the inferior parietal lobule, anterior cingulate, inferior temporal lobule, the dentate nucleus, and the cerebellar lobules IV/V, VI, and VIII bilaterally corresponding to the motor regions of the cerebellum (z score = 3

  20. Age-specific activation of cerebral areas in motor imagery - a fMRI study

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Li [Chongqing University, Key Laboratory of Biorheological Science and Technology of Ministry of Education, Bioengineering College, Chongqing (China); Third Military Medical University, Department of Medical Image, College of Biomedical Engineering, Chongqing (China); Qiu, Mingguo; Zhang, Jingna; Zhang, Ye; Sang, Linqiong [Third Military Medical University, Department of Medical Image, College of Biomedical Engineering, Chongqing (China); Liu, Chen; Yang, Jun [Third Military Medical University, Department of Radiology, Southwest Hospital, Chongqing (China); Yan, Rubing [Third Military Medical University, Department of Rehabilitation, Southwest Hospital, Chongqing (China); Zheng, Xiaolin [Chongqing University, Key Laboratory of Biorheological Science and Technology of Ministry of Education, Bioengineering College, Chongqing (China)

    2014-04-15

    The objectives of this study were to study the age-specific activation patterns of cerebral areas during motor execution (ME) and motor imaging (MI) of the upper extremities and to discuss the age-related neural mechanisms associated with ME or MI. The functional magnetic resonance imaging technique was used to monitor the pattern and intensity of brain activation during the ME and MI of the upper extremities in 20 elderly (>50 years) and 19 young healthy subjects (<25 years). No major differences were identified regarding the activated brain areas during ME or MI between the two groups; however, a minor difference was noted. The intensity of the activated brain area during ME was stronger in the older group than in the younger group, while the results with MI were the opposite. The posterior central gyrus and supplementary motor area during MI were more active in the younger group than in the older group. The putamen, lingual, and so on demonstrated stronger activation during dominant hand MI in the older group. The results of this study revealed that the brain structure was altered and that neuronal activity was attenuated with age, and the cerebral cortex and subcortical tissues were found to be over-activated to achieve the same level of ME and MI, indicating that the activating effects of the left hemisphere enhanced with age, whereas the inhibitory effects declined during ME, and activation of the right hemisphere became more difficult during MI. (orig.)

  1. Age-specific activation of cerebral areas in motor imagery - a fMRI study

    International Nuclear Information System (INIS)

    Wang, Li; Qiu, Mingguo; Zhang, Jingna; Zhang, Ye; Sang, Linqiong; Liu, Chen; Yang, Jun; Yan, Rubing; Zheng, Xiaolin

    2014-01-01

    The objectives of this study were to study the age-specific activation patterns of cerebral areas during motor execution (ME) and motor imaging (MI) of the upper extremities and to discuss the age-related neural mechanisms associated with ME or MI. The functional magnetic resonance imaging technique was used to monitor the pattern and intensity of brain activation during the ME and MI of the upper extremities in 20 elderly (>50 years) and 19 young healthy subjects (<25 years). No major differences were identified regarding the activated brain areas during ME or MI between the two groups; however, a minor difference was noted. The intensity of the activated brain area during ME was stronger in the older group than in the younger group, while the results with MI were the opposite. The posterior central gyrus and supplementary motor area during MI were more active in the younger group than in the older group. The putamen, lingual, and so on demonstrated stronger activation during dominant hand MI in the older group. The results of this study revealed that the brain structure was altered and that neuronal activity was attenuated with age, and the cerebral cortex and subcortical tissues were found to be over-activated to achieve the same level of ME and MI, indicating that the activating effects of the left hemisphere enhanced with age, whereas the inhibitory effects declined during ME, and activation of the right hemisphere became more difficult during MI. (orig.)

  2. AN EVOLUTIONARY ALGORITHM FOR FAST INTENSITY BASED IMAGE MATCHING BETWEEN OPTICAL AND SAR SATELLITE IMAGERY

    Directory of Open Access Journals (Sweden)

    P. Fischer

    2018-04-01

    Full Text Available This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.

  3. Brain architecture: a design for natural computation.

    Science.gov (United States)

    Kaiser, Marcus

    2007-12-15

    Fifty years ago, John von Neumann compared the architecture of the brain with that of the computers he invented and which are still in use today. In those days, the organization of computers was based on concepts of brain organization. Here, we give an update on current results on the global organization of neural systems. For neural systems, we outline how the spatial and topological architecture of neuronal and cortical networks facilitates robustness against failures, fast processing and balanced network activation. Finally, we discuss mechanisms of self-organization for such architectures. After all, the organization of the brain might again inspire computer architecture.

  4. Fully Implanted Brain-Computer Interface in a Locked-In Patient with ALS.

    Science.gov (United States)

    Vansteensel, Mariska J; Pels, Elmar G M; Bleichner, Martin G; Branco, Mariana P; Denison, Timothy; Freudenburg, Zachary V; Gosselaar, Peter; Leinders, Sacha; Ottens, Thomas H; Van Den Boom, Max A; Van Rijen, Peter C; Aarnoutse, Erik J; Ramsey, Nick F

    2016-11-24

    Options for people with severe paralysis who have lost the ability to communicate orally are limited. We describe a method for communication in a patient with late-stage amyotrophic lateral sclerosis (ALS), involving a fully implanted brain-computer interface that consists of subdural electrodes placed over the motor cortex and a transmitter placed subcutaneously in the left side of the thorax. By attempting to move the hand on the side opposite the implanted electrodes, the patient accurately and independently controlled a computer typing program 28 weeks after electrode placement, at the equivalent of two letters per minute. The brain-computer interface offered autonomous communication that supplemented and at times supplanted the patient's eye-tracking device. (Funded by the Government of the Netherlands and the European Union; ClinicalTrials.gov number, NCT02224469 .).

  5. Brain systems for probabilistic and dynamic prediction: computational specificity and integration.

    Directory of Open Access Journals (Sweden)

    Jill X O'Reilly

    2013-09-01

    Full Text Available A computational approach to functional specialization suggests that brain systems can be characterized in terms of the types of computations they perform, rather than their sensory or behavioral domains. We contrasted the neural systems associated with two computationally distinct forms of predictive model: a reinforcement-learning model of the environment obtained through experience with discrete events, and continuous dynamic forward modeling. By manipulating the precision with which each type of prediction could be used, we caused participants to shift computational strategies within a single spatial prediction task. Hence (using fMRI we showed that activity in two brain systems (typically associated with reward learning and motor control could be dissociated in terms of the forms of computations that were performed there, even when both systems were used to make parallel predictions of the same event. A region in parietal cortex, which was sensitive to the divergence between the predictions of the models and anatomically connected to both computational networks, is proposed to mediate integration of the two predictive modes to produce a single behavioral output.

  6. Computer simulation of a 3-phase induction motor

    International Nuclear Information System (INIS)

    Memon, N.A.; Unsworth, P.J.

    2004-01-01

    Computer Simulation of a 3-phase squirrel-cage induction motor is presented in Microsoft QBASIC for understanding trends and various operational modes of an induction motor. Thyristor fed, phase controlled induction motor (three-wire) model has been simulated. In which voltage is applied to the motor stator winding through back-to-back connected thyristors as controlled switches in series with the stator. The simulated induction motor system opens up towards a wide range of investigation/analysis options for research and development work in the field. Key features of the simulation performed are highlighted for development of better understanding of the work done. Complete study of an Induction Motor, starting modes in terms the voltage/current, torque/speed characteristics and their graphical representation produced is presented. Ideal agreement of the simulation results with the notional outcome encourages users to go ahead for various hardware development projects based on the study through the simulation. (author)

  7. Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review.

    Science.gov (United States)

    Hong, Keum-Shik; Khan, Muhammad Jawad

    2017-01-01

    In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain-computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.

  8. Brain-computer interface controlled gaming: evaluation of usability by severely motor restricted end-users.

    Science.gov (United States)

    Holz, Elisa Mira; Höhne, Johannes; Staiger-Sälzer, Pit; Tangermann, Michael; Kübler, Andrea

    2013-10-01

    Connect-Four, a new sensorimotor rhythm (SMR) based brain-computer interface (BCI) gaming application, was evaluated by four severely motor restricted end-users; two were in the locked-in state and had unreliable eye-movement. Following the user-centred approach, usability of the BCI prototype was evaluated in terms of effectiveness (accuracy), efficiency (information transfer rate (ITR) and subjective workload) and users' satisfaction. Online performance varied strongly across users and sessions (median accuracy (%) of end-users: A=.65; B=.60; C=.47; D=.77). Our results thus yielded low to medium effectiveness in three end-users and high effectiveness in one end-user. Consequently, ITR was low (0.05-1.44bits/min). Only two end-users were able to play the game in free-mode. Total workload was moderate but varied strongly across sessions. Main sources of workload were mental and temporal demand. Furthermore, frustration contributed to the subjective workload of two end-users. Nevertheless, most end-users accepted the BCI application well and rated satisfaction medium to high. Sources for dissatisfaction were (1) electrode gel and cap, (2) low effectiveness, (3) time-consuming adjustment and (4) not easy-to-use BCI equipment. All four end-users indicated ease of use as being one of the most important aspect of BCI. Effectiveness and efficiency are lower as compared to applications using the event-related potential as input channel. Nevertheless, the SMR-BCI application was satisfactorily accepted by the end-users and two of four could imagine using the BCI application in their daily life. Thus, despite moderate effectiveness and efficiency BCIs might be an option when controlling an application for entertainment. Copyright © 2013 Elsevier B.V. All rights reserved.

  9. Long-Lasting Cortical Reorganization as the Result of Motor Imagery of Throwing a Ball in a Virtual Tennis Court

    Science.gov (United States)

    Cebolla, Ana M.; Petieau, Mathieu; Cevallos, Carlos; Leroy, Axelle; Dan, Bernard; Cheron, Guy

    2015-01-01

    In order to characterize the neural signature of a motor imagery (MI) task, the present study investigates for the first time the oscillation characteristics including both of the time-frequency measurements, event related spectral perturbation and intertrial coherence (ITC) underlying the variations in the temporal measurements (event related potentials, ERP) directly related to a MI task. We hypothesize that significant variations in both of the time-frequency measurements underlie the specific changes in the ERP directly related to MI. For the MI task, we chose a simple everyday task (throwing a tennis ball), that does not require any particular motor expertise, set within the controlled virtual reality scenario of a tennis court. When compared to the rest condition a consistent, long-lasting negative fronto-central ERP wave was accompanied by significant changes in both time frequency measurements suggesting long-lasting cortical activity reorganization. The ERP wave was characterized by two peaks at about 300 ms (N300) and 1000 ms (N1000). The N300 component was centrally localized on the scalp and was accompanied by significant phase consistency in the delta brain rhythms in the contralateral central scalp areas. The N1000 component spread wider centrally and was accompanied by a significant power decrease (or event related desynchronization) in low beta brain rhythms localized in fronto-precentral and parieto-occipital scalp areas and also by a significant power increase (or event related synchronization) in theta brain rhythms spreading fronto-centrally. During the transition from N300 to N1000, a contralateral alpha (mu) as well as post-central and parieto-theta rhythms occurred. The visual representation of movement formed in the minds of participants might underlie a top-down process from the fronto-central areas which is reflected by the amplitude changes observed in the fronto-central ERPs and by the significant phase synchrony in contralateral fronto

  10. Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface

    Science.gov (United States)

    Blankertz, Benjamin; Tangermann, Michael; Vidaurre, Carmen; Dickhaus, Thorsten; Sannelli, Claudia; Popescu, Florin; Fazli, Siamac; Danóczy, Márton; Curio, Gabriel; Müller, Klaus-Robert

    The Berlin Brain-Computer Interface Brain-Computer Interface (BBCI) uses a machine learning approach to extract user-specific patterns from high-dimensional EEG-features optimized for revealing the user's mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2-5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user's intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [6] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Sections 4.3 and 4.4.

  11. Motion/imagery secure cloud enterprise architecture analysis

    Science.gov (United States)

    DeLay, John L.

    2012-06-01

    Cloud computing with storage virtualization and new service-oriented architectures brings a new perspective to the aspect of a distributed motion imagery and persistent surveillance enterprise. Our existing research is focused mainly on content management, distributed analytics, WAN distributed cloud networking performance issues of cloud based technologies. The potential of leveraging cloud based technologies for hosting motion imagery, imagery and analytics workflows for DOD and security applications is relatively unexplored. This paper will examine technologies for managing, storing, processing and disseminating motion imagery and imagery within a distributed network environment. Finally, we propose areas for future research in the area of distributed cloud content management enterprises.

  12. Mapping the involvement of BA 4a and 4p during Motor Imagery.

    Science.gov (United States)

    Sharma, Nikhil; Jones, P S; Carpenter, T A; Baron, Jean-Claude

    2008-05-15

    Motor Imagery (MI) is an attractive but intriguing means to access the motor network. There are marked inconsistencies in the functional imaging literature regarding the degree, extent and distribution of the primary motor cortex (BA 4) involvement during MI as compared to Executed Movement (EM), which may in part be related to the diverse role of BA 4 and its two subdivisions (i.e., 4a and 4p) in motor processes as well as to methodological issues. Here we used fMRI with monitoring of compliance to show that in healthy volunteers optimally screened for their ability to perform MI the contralateral BA 4 is involved during MI of a finger opposition sequence (2, 3, 4, 5; paced at 1 Hz), albeit less than during EM of the same sequence, and in a location sparing the hand area. Furthermore, both 4a and 4p subdivisions were found to be involved in MI, but the relative involvement of BA 4p appeared more robust and closer to that seen with EM. We suggest that during MI the role of BA 4 and its subdivisions may be non-executive, perhaps related to spatial encoding, though clearly further studies are needed. Finally, we report a similar hemispheric activation balance within BA 4 with both tasks, which extends the commonalities between EM and MI.

  13. Short-term kinesthetic training for sensorimotor rhythms: effects in experts and amateurs.

    Science.gov (United States)

    Zapała, Dariusz; Zabielska-Mendyk, Emilia; Cudo, Andrzej; Krzysztofiak, Agnieszka; Augustynowicz, Paweł; Francuz, Piotr

    2015-01-01

    The authors' aim was to examine whether short-term kinesthetic training affects the level of sensorimotor rhythm (SMR) in different frequency band: alpha (8-12 Hz), lower beta (12.5-16 Hz) and beta (16.5-20 Hz) during the execution of a motor imagery task of closing and opening the right and the left hand by experts (jugglers, practicing similar exercises on an everyday basis) and amateurs (individuals not practicing any sports). It was found that the performance of short kinesthetic training increases the power of alpha rhythm when executing imagery tasks only in the group of amateurs. Therefore, kinesthetic training may be successfully used as a method increasing the vividness of motor imagery, for example, in tasks involving the control of brain-computer interfaces based on SMR.

  14. Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report.

    Science.gov (United States)

    Broetz, Doris; Braun, Christoph; Weber, Cornelia; Soekadar, Surjo R; Caria, Andrea; Birbaumer, Niels

    2010-09-01

    There is no accepted and efficient rehabilitation strategy to reduce focal impairments for patients with chronic stroke who lack residual movements. A 67-year-old hemiplegic patient with no active finger extension was trained with a brain-computer interface (BCI) combined with a specific daily life-oriented physiotherapy. The BCI used electrical brain activity (EEG) and magnetic brain activity (MEG) to drive an orthosis and a robot affixed to the patient's affected upper extremity, which enabled him to move the paralyzed arm and hand driven by voluntary modulation of micro-rhythm activity. In addition, the patient practiced goal-directed physiotherapy training. Over 1 year, he completed 3 training blocks. Arm motor function, gait capacities (using Fugl-Meyer Assessment, Wolf Motor Function Test, Modified Ashworth Scale, 10-m walk speed, and goal attainment score), and brain reorganization (functional MRI, MEG) were repeatedly assessed. The ability of hand and arm movements as well as speed and safety of gait improved significantly (mean 46.6%). Improvement of motor function was associated with increased micro-oscillations in the ipsilesional motor cortex. This proof-of-principle study suggests that the combination of BCI training with goal-directed, active physical therapy may improve the motor abilities of chronic stroke patients despite apparent initial paralysis.

  15. THE USE OF A COMPLEX “BRAIN-COMPUTER INTERFACE AND EXO-SKELETON” AND MOVEMENT IMAGINATION TECHNIQUE FOR POST-STROKE REHABILITATION

    Directory of Open Access Journals (Sweden)

    S. V. Kotov

    2015-01-01

    Full Text Available Background: Efficacy of physical exercise and movement imagination for restoration of motor dysfunction after a stroke is seen as proven. However, the use of movement imagination is complicated by impossibility of objective and subjective control over  the exercise, as well as by the absence of their motor support. The brain-computer interface based on electroencephalography is a technique that enables a feedback during movement imagination.Materials and methods: We assessed 10 patients (6 men and 4 women aged from 30 to 66 years (mean age, 47 ± 7.7 years with an ischemic (n = 9 and hemorrhagic (n = 1 stroke during the last 2 months to 4 years. Online recognition of movement imagination was done by a classifier with a brain computer interface. An exo-skeleton supported passive movements in a paretic hand managed by the brain-computer interface. During 2 weeks the patients had 10 sessions of 45–90 minute duration each. For control, we used data from 5 stroke patients who, in addition to their standard treatment, underwent an imitation of rehabilitation procedures without movement imagination and feedback. To assess efficacy of treatment, we used a modified Ashworth scale, Fugl-Meyer scale, test for evaluation of hand functions ARAT, British scale for assessment of muscle force MRC-SS. Level of everyday activity and working ability was measured with a modified Rankin scale and Bartel index. Cognitive functions were assessed with Schulte tables.Results: Online recognition of movement imagination according to desynchronization of μ rhythm was registered in 50–75% of patients. All patients reported a subjective improvement of motor functions and working ability. Positive results for at least one parameter were observed in all patients; however, there were no significant difference between the parameters before and after rehabilitation procedures, excluding cognitive functions (degree of warming-up, p < 0.02.Conclusion: In post stroke patients

  16. Dyslexia singular brain; Le singulier cerveau des dyslexiques

    Energy Technology Data Exchange (ETDEWEB)

    Habis, M.; Robichon, F. [Centre Hospitalier Universitaire de la Timone, 13 - Marseille (France); Demonet, J.F. [Centre Hospitalier Universitaire la Grave, 31 - Toulouse (France)

    1996-07-01

    Of late ten years, neurologists are studying the brain of the dyslectics. The cerebral imagery (NMR imaging, positron computed tomography) has allowed to confirm the anatomical particularities discovered by some of them: asymmetry default of cerebral hemispheres, size abnormally large of the white substance mass which connect the two hemispheres. The functional imagery, when visualizing this singular brain at work, allows to understand why it labors to reading. (O.M.). 4 refs.

  17. Training visual imagery: Improvements of metacognition, but not imagery strength

    Directory of Open Access Journals (Sweden)

    Rosanne Lynn Rademaker

    2012-07-01

    Full Text Available Visual imagery has been closely linked to brain mechanisms involved in perception. Can visual imagery, like visual perception, improve by means of training? Previous research has demonstrated that people can reliably evaluate the vividness of single episodes of sensory imagination – might the metacognition of imagery also improve over the course of training? We had participants imagine colored Gabor patterns for an hour a day, over the course of five consecutive days, and again two weeks after training. Participants rated the subjective vividness and effort of their mental imagery on each trial. The influence of imagery on subsequent binocular rivalry dominance was taken as our measure of imagery strength. We found no overall effect of training on imagery strength. Training did, however, improve participant’s metacognition of imagery. Trial-by-trial ratings of vividness gained predictive power on subsequent rivalry dominance as a function of training. These data suggest that, while imagery strength might be immune to training in the current context, people’s metacognitive understanding of mental imagery can improve with practice.

  18. Local-learning-based neuron selection for grasping gesture prediction in motor brain machine interfaces

    Science.gov (United States)

    Xu, Kai; Wang, Yiwen; Wang, Yueming; Wang, Fang; Hao, Yaoyao; Zhang, Shaomin; Zhang, Qiaosheng; Chen, Weidong; Zheng, Xiaoxiang

    2013-04-01

    Objective. The high-dimensional neural recordings bring computational challenges to movement decoding in motor brain machine interfaces (mBMI), especially for portable applications. However, not all recorded neural activities relate to the execution of a certain movement task. This paper proposes to use a local-learning-based method to perform neuron selection for the gesture prediction in a reaching and grasping task. Approach. Nonlinear neural activities are decomposed into a set of linear ones in a weighted feature space. A margin is defined to measure the distance between inter-class and intra-class neural patterns. The weights, reflecting the importance of neurons, are obtained by minimizing a margin-based exponential error function. To find the most dominant neurons in the task, 1-norm regularization is introduced to the objective function for sparse weights, where near-zero weights indicate irrelevant neurons. Main results. The signals of only 10 neurons out of 70 selected by the proposed method could achieve over 95% of the full recording's decoding accuracy of gesture predictions, no matter which different decoding methods are used (support vector machine and K-nearest neighbor). The temporal activities of the selected neurons show visually distinguishable patterns associated with various hand states. Compared with other algorithms, the proposed method can better eliminate the irrelevant neurons with near-zero weights and provides the important neuron subset with the best decoding performance in statistics. The weights of important neurons converge usually within 10-20 iterations. In addition, we study the temporal and spatial variation of neuron importance along a period of one and a half months in the same task. A high decoding performance can be maintained by updating the neuron subset. Significance. The proposed algorithm effectively ascertains the neuronal importance without assuming any coding model and provides a high performance with different

  19. Functional connectivity between somatosensory and motor brain areas predicts individual differences in motor learning by observing.

    Science.gov (United States)

    McGregor, Heather R; Gribble, Paul L

    2017-08-01

    Action observation can facilitate the acquisition of novel motor skills; however, there is considerable individual variability in the extent to which observation promotes motor learning. Here we tested the hypothesis that individual differences in brain function or structure can predict subsequent observation-related gains in motor learning. Subjects underwent an anatomical MRI scan and resting-state fMRI scans to assess preobservation gray matter volume and preobservation resting-state functional connectivity (FC), respectively. On the following day, subjects observed a video of a tutor adapting her reaches to a novel force field. After observation, subjects performed reaches in a force field as a behavioral assessment of gains in motor learning resulting from observation. We found that individual differences in resting-state FC, but not gray matter volume, predicted postobservation gains in motor learning. Preobservation resting-state FC between left primary somatosensory cortex and bilateral dorsal premotor cortex, primary motor cortex, and primary somatosensory cortex and left superior parietal lobule was positively correlated with behavioral measures of postobservation motor learning. Sensory-motor resting-state FC can thus predict the extent to which observation will promote subsequent motor learning. NEW & NOTEWORTHY We show that individual differences in preobservation brain function can predict subsequent observation-related gains in motor learn