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Sample records for auditory multi-class brain-computer

  1. Probabilistic Methods in Multi-Class Brain-Computer Interface

    Institute of Scientific and Technical Information of China (English)

    Ping Yang; Xu Lei; Tie-Jun Liu; Peng Xu; De-Zhong Yao

    2009-01-01

    Two probabilistic methods are extended to research multi-class motor imagery of brain-computer interface (BCI):support vector machine (SVM) with posteriori probability (PSVM) and Bayesian linear dis-criminant analysis with probabilistic output (PBLDA).A comparative evaluation of these two methods is conducted.The results shows that:1) probabilistic information can improve the performance of BCI for subjects with high kappa coefficient,and 2) PSVM usually results in a stable kappa coefficient whereas PBLDA is more efficient in estimating the model parameters.

  2. Multi-class motor imagery EEG decoding for brain-computer interfaces

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    Deng eWang

    2012-10-01

    Full Text Available 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 (ICA 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-continuous 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 (SVM 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.

  3. An enhanced probabilistic LDA for multi-class brain computer interface.

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    Peng Xu

    Full Text Available BACKGROUND: There is a growing interest in the study of signal processing and machine learning methods, which may make the brain computer interface (BCI a new communication channel. A variety of classification methods have been utilized to convert the brain information into control commands. However, most of the methods only produce uncalibrated values and uncertain results. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we presented a probabilistic method "enhanced BLDA" (EBLDA for multi-class motor imagery BCI, which utilized Bayesian linear discriminant analysis (BLDA with probabilistic output to improve the classification performance. EBLDA builds a new classifier that enlarges training dataset by adding test samples with high probability. EBLDA is based on the hypothesis that unlabeled samples with high probability provide valuable information to enhance learning process and generate a classifier with refined decision boundaries. To investigate the performance of EBLDA, we first used carefully designed simulated datasets to study how EBLDA works. Then, we adopted a real BCI dataset for further evaluation. The current study shows that: 1 Probabilistic information can improve the performance of BCI for subjects with high kappa coefficient; 2 With supplementary training samples from the test samples of high probability, EBLDA is significantly better than BLDA in classification, especially for small training datasets, in which EBLDA can obtain a refined decision boundary by a shift of BLDA decision boundary with the support of the information from test samples. CONCLUSIONS/SIGNIFICANCE: The proposed EBLDA could potentially reduce training effort. Therefore, it is valuable for us to realize an effective online BCI system, especially for multi-class BCI systems.

  4. An Enhanced Probabilistic LDA for Multi-Class Brain Computer Interface

    OpenAIRE

    Peng Xu; Ping Yang; Xu Lei; Dezhong Yao

    2011-01-01

    BACKGROUND: There is a growing interest in the study of signal processing and machine learning methods, which may make the brain computer interface (BCI) a new communication channel. A variety of classification methods have been utilized to convert the brain information into control commands. However, most of the methods only produce uncalibrated values and uncertain results. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we presented a probabilistic method "enhanced BLDA" (EBLDA) for multi-c...

  5. Prediction of auditory and visual p300 brain-computer interface aptitude.

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    Sebastian Halder

    Full Text Available OBJECTIVE: Brain-computer interfaces (BCIs provide a non-muscular communication channel for patients with late-stage motoneuron disease (e.g., amyotrophic lateral sclerosis (ALS or otherwise motor impaired people and are also used for motor rehabilitation in chronic stroke. Differences in the ability to use a BCI vary from person to person and from session to session. A reliable predictor of aptitude would allow for the selection of suitable BCI paradigms. For this reason, we investigated whether P300 BCI aptitude could be predicted from a short experiment with a standard auditory oddball. METHODS: Forty healthy participants performed an electroencephalography (EEG based visual and auditory P300-BCI spelling task in a single session. In addition, prior to each session an auditory oddball was presented. Features extracted from the auditory oddball were analyzed with respect to predictive power for BCI aptitude. RESULTS: Correlation between auditory oddball response and P300 BCI accuracy revealed a strong relationship between accuracy and N2 amplitude and the amplitude of a late ERP component between 400 and 600 ms. Interestingly, the P3 amplitude of the auditory oddball response was not correlated with accuracy. CONCLUSIONS: Event-related potentials recorded during a standard auditory oddball session moderately predict aptitude in an audiory and highly in a visual P300 BCI. The predictor will allow for faster paradigm selection. SIGNIFICANCE: Our method will reduce strain on patients because unsuccessful training may be avoided, provided the results can be generalized to the patient population.

  6. The WIN-Speller: A new Intuitive Auditory Brain-Computer Interface Spelling Application

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    Sonja C Kleih

    2015-10-01

    Full Text Available The objective of this study was to test the usability of a new auditory Brain-Computer Interface (BCI application for communication. We introduce a word based, intuitive auditory spelling paradigm the WIN-speller. In the WIN-speller letters are grouped by words, such as the word KLANG representing the letters A, G, K, L and N. Thereby, the decoding step between perceiving a code and translating it to the stimuli it represents becomes superfluous. We tested 11 healthy volunteers and 4 end-users with motor impairment in the copy spelling mode. Spelling was successful with an average accuracy of 84% in the healthy sample. Three of the end-users communicated with average accuracies of 80% or higher while one user was not able to communicate reliably. Even though further evaluation is required, the WIN-speller represents a potential alternative for BCI based communication in end-users.

  7. A vision-free brain-computer interface (BCI) paradigm based on auditory selective attention.

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    Kim, Do-Won; Cho, Jae-Hyun; Hwang, Han-Jeong; Lim, Jeong-Hwan; Im, Chang-Hwan

    2011-01-01

    Majority of the recently developed brain computer interface (BCI) systems have been using visual stimuli or visual feedbacks. However, the BCI paradigms based on visual perception might not be applicable to severe locked-in patients who have lost their ability to control their eye movement or even their vision. In the present study, we investigated the feasibility of a vision-free BCI paradigm based on auditory selective attention. We used the power difference of auditory steady-state responses (ASSRs) when the participant modulates his/her attention to the target auditory stimulus. The auditory stimuli were constructed as two pure-tone burst trains with different beat frequencies (37 and 43 Hz) which were generated simultaneously from two speakers located at different positions (left and right). Our experimental results showed high classification accuracies (64.67%, 30 commands/min, information transfer rate (ITR) = 1.89 bits/min; 74.00%, 12 commands/min, ITR = 2.08 bits/min; 82.00%, 6 commands/min, ITR = 1.92 bits/min; 84.33%, 3 commands/min, ITR = 1.12 bits/min; without any artifact rejection, inter-trial interval = 6 sec), enough to be used for a binary decision. Based on the suggested paradigm, we implemented a first online ASSR-based BCI system that demonstrated the possibility of materializing a totally vision-free BCI system.

  8. An Auditory-Tactile Visual Saccade-Independent P300 Brain-Computer Interface.

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    Yin, Erwei; Zeyl, Timothy; Saab, Rami; Hu, Dewen; Zhou, Zongtan; Chau, Tom

    2016-02-01

    Most P300 event-related potential (ERP)-based brain-computer interface (BCI) studies focus on gaze shift-dependent BCIs, which cannot be used by people who have lost voluntary eye movement. However, the performance of visual saccade-independent P300 BCIs is generally poor. To improve saccade-independent BCI performance, we propose a bimodal P300 BCI approach that simultaneously employs auditory and tactile stimuli. The proposed P300 BCI is a vision-independent system because no visual interaction is required of the user. Specifically, we designed a direction-congruent bimodal paradigm by randomly and simultaneously presenting auditory and tactile stimuli from the same direction. Furthermore, the channels and number of trials were tailored to each user to improve online performance. With 12 participants, the average online information transfer rate (ITR) of the bimodal approach improved by 45.43% and 51.05% over that attained, respectively, with the auditory and tactile approaches individually. Importantly, the average online ITR of the bimodal approach, including the break time between selections, reached 10.77 bits/min. These findings suggest that the proposed bimodal system holds promise as a practical visual saccade-independent P300 BCI. PMID:26678249

  9. Exploring combinations of auditory and visual stimuli for gaze-independent brain-computer interfaces.

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    Xingwei An

    Full Text Available For Brain-Computer Interface (BCI systems that are designed for users with severe impairments of the oculomotor system, an appropriate mode of presenting stimuli to the user is crucial. To investigate whether multi-sensory integration can be exploited in the gaze-independent event-related potentials (ERP speller and to enhance BCI performance, we designed a visual-auditory speller. We investigate the possibility to enhance stimulus presentation by combining visual and auditory stimuli within gaze-independent spellers. In this study with N = 15 healthy users, two different ways of combining the two sensory modalities are proposed: simultaneous redundant streams (Combined-Speller and interleaved independent streams (Parallel-Speller. Unimodal stimuli were applied as control conditions. The workload, ERP components, classification accuracy and resulting spelling speed were analyzed for each condition. The Combined-speller showed a lower workload than uni-modal paradigms, without the sacrifice of spelling performance. Besides, shorter latencies, lower amplitudes, as well as a shift of the temporal and spatial distribution of discriminative information were observed for Combined-speller. These results are important and are inspirations for future studies to search the reason for these differences. For the more innovative and demanding Parallel-Speller, where the auditory and visual domains are independent from each other, a proof of concept was obtained: fifteen users could spell online with a mean accuracy of 87.7% (chance level <3% showing a competitive average speed of 1.65 symbols per minute. The fact that it requires only one selection period per symbol makes it a good candidate for a fast communication channel. It brings a new insight into the true multisensory stimuli paradigms. Novel approaches for combining two sensory modalities were designed here, which are valuable for the development of ERP-based BCI paradigms.

  10. An online brain-computer interface based on shifting attention to concurrent streams of auditory stimuli

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    Hill, N. J.; Schölkopf, B.

    2012-04-01

    We report on the development and online testing of an electroencephalogram-based brain-computer interface (BCI) that aims to be usable by completely paralysed users—for whom visual or motor-system-based BCIs may not be suitable, and among whom reports of successful BCI use have so far been very rare. The current approach exploits covert shifts of attention to auditory stimuli in a dichotic-listening stimulus design. To compare the efficacy of event-related potentials (ERPs) and steady-state auditory evoked potentials (SSAEPs), the stimuli were designed such that they elicited both ERPs and SSAEPs simultaneously. Trial-by-trial feedback was provided online, based on subjects' modulation of N1 and P3 ERP components measured during single 5 s stimulation intervals. All 13 healthy subjects were able to use the BCI, with performance in a binary left/right choice task ranging from 75% to 96% correct across subjects (mean 85%). BCI classification was based on the contrast between stimuli in the attended stream and stimuli in the unattended stream, making use of every stimulus, rather than contrasting frequent standard and rare ‘oddball’ stimuli. SSAEPs were assessed offline: for all subjects, spectral components at the two exactly known modulation frequencies allowed discrimination of pre-stimulus from stimulus intervals, and of left-only stimuli from right-only stimuli when one side of the dichotic stimulus pair was muted. However, attention modulation of SSAEPs was not sufficient for single-trial BCI communication, even when the subject's attention was clearly focused well enough to allow classification of the same trials via ERPs. ERPs clearly provided a superior basis for BCI. The ERP results are a promising step towards the development of a simple-to-use, reliable yes/no communication system for users in the most severely paralysed states, as well as potential attention-monitoring and -training applications outside the context of assistive technology.

  11. Incorporating modern neuroscience findings to improve brain-computer interfaces: tracking auditory attention

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    Wronkiewicz, Mark; Larson, Eric; Lee, Adrian KC

    2016-10-01

    Objective. Brain-computer interface (BCI) technology allows users to generate actions based solely on their brain signals. However, current non-invasive BCIs generally classify brain activity recorded from surface electroencephalography (EEG) electrodes, which can hinder the application of findings from modern neuroscience research. Approach. In this study, we use source imaging—a neuroimaging technique that projects EEG signals onto the surface of the brain—in a BCI classification framework. This allowed us to incorporate prior research from functional neuroimaging to target activity from a cortical region involved in auditory attention. Main results. Classifiers trained to detect attention switches performed better with source imaging projections than with EEG sensor signals. Within source imaging, including subject-specific anatomical MRI information (instead of using a generic head model) further improved classification performance. This source-based strategy also reduced accuracy variability across three dimensionality reduction techniques—a major design choice in most BCIs. Significance. Our work shows that source imaging provides clear quantitative and qualitative advantages to BCIs and highlights the value of incorporating modern neuroscience knowledge and methods into BCI systems.

  12. A brain-computer interface controlled auditory event-related potential (p300) spelling system for locked-in patients.

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    Kübler, Andrea; Furdea, Adrian; Halder, Sebastian; Hammer, Eva Maria; Nijboer, Femke; Kotchoubey, Boris

    2009-03-01

    Using brain-computer interfaces (BCI) humans can select letters or other targets on a computer screen without any muscular involvement. An intensively investigated kind of BCI is based on the recording of visual event-related brain potentials (ERP). However, some severely paralyzed patients who need a BCI for communication have impaired vision or lack control of gaze movement, thus making a BCI depending on visual input no longer feasible. In an effort to render the ERP-BCI usable for this group of patients, the ERP-BCI was adapted to auditory stimulation. Letters of the alphabet were assigned to cells in a 5 x 5 matrix. Rows of the matrix were coded with numbers 1 to 5, and columns with numbers 6 to 10, and the numbers were presented auditorily. To select a letter, users had to first select the row and then the column containing the desired letter. Four severely paralyzed patients in the end-stage of a neurodegenerative disease were examined. All patients performed above chance level. Spelling accuracy was significantly lower with the auditory system as compared with a similar visual system. Patients reported difficulties in concentrating on the task when presented with the auditory system. In future studies, the auditory ERP-BCI should be adjusted by taking into consideration specific features of severely paralyzed patients, such as reduced attention span. This adjustment in combination with more intensive training will show whether an auditory ERP-BCI can become an option for visually impaired patients. PMID:19351359

  13. An Evaluation of Training with an Auditory P300 Brain-Computer Interface for the Japanese Hiragana Syllabary

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    Halder, Sebastian; Takano, Kouji; Ora, Hiroki; Onishi, Akinari; Utsumi, Kota; Kansaku, Kenji

    2016-01-01

    Gaze-independent brain-computer interfaces (BCIs) are a possible communication channel for persons with paralysis. We investigated if it is possible to use auditory stimuli to create a BCI for the Japanese Hiragana syllabary, which has 46 Hiragana characters. Additionally, we investigated if training has an effect on accuracy despite the high amount of different stimuli involved. Able-bodied participants (N = 6) were asked to select 25 syllables (out of fifty possible choices) using a two step procedure: First the consonant (ten choices) and then the vowel (five choices). This was repeated on 3 separate days. Additionally, a person with spinal cord injury (SCI) participated in the experiment. Four out of six healthy participants reached Hiragana syllable accuracies above 70% and the information transfer rate increased from 1.7 bits/min in the first session to 3.2 bits/min in the third session. The accuracy of the participant with SCI increased from 12% (0.2 bits/min) to 56% (2 bits/min) in session three. Reliable selections from a 10 × 5 matrix using auditory stimuli were possible and performance is increased by training. We were able to show that auditory P300 BCIs can be used for communication with up to fifty symbols. This enables the use of the technology of auditory P300 BCIs with a variety of applications.

  14. Communication and control by listening: towards optimal design of a two-class auditory streaming brain-computer interface

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    N. Jeremy Hill

    2012-12-01

    Full Text Available Most brain-computer interface (BCI systems require users to modulate brain signals in response to visual stimuli. Thus, they may not be useful to people with limited vision, such as those with severe paralysis. One important approach for overcoming this issue is auditory streaming, an approach whereby a BCI system is driven by shifts of attention between two dichotically presented auditory stimulus streams. Motivated by the long-term goal of translating such a system into a reliable, simple yes-no interface for clinical usage, we aim to answer two main questions. First, we asked which of two previously-published variants provides superior performance: a fixed-phase (FP design in which the streams have equal period and opposite phase, or a drifting-phase (DP design where the periods are unequal. We found FP to be superior to DP (p = 0.002: average performance levels were 80% and 72% correct, respectively. We were also able to show, in a pilot with one subject, that auditory streaming can support continuous control and neurofeedback applications: by shifting attention between ongoing left and right auditory streams, the subject was able to control the position of a paddle in a computer game. Second, we examined whether the system is dependent on eye movements, since it is known that eye movements and auditory attention may influence each other, and any dependence on the ability to move one’s eyes would be a barrier to translation to paralyzed users. We discovered that, despite instructions, some subjects did make eye movements that were indicative of the direction of attention. However, there was no correlation, across subjects, between the reliability of the eye movement signal and the reliability of the BCI system, indicating that our system was configured to work independently of eye movement. Together, these findings are an encouraging step forward toward BCIs that provide practical communication and control options for the most severely

  15. Estimating the intended sound direction of the user: toward an auditory brain-computer interface using out-of-head sound localization.

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    Isao Nambu

    Full Text Available The auditory Brain-Computer Interface (BCI using electroencephalograms (EEG is a subject of intensive study. As a cue, auditory BCIs can deal with many of the characteristics of stimuli such as tone, pitch, and voices. Spatial information on auditory stimuli also provides useful information for a BCI. However, in a portable system, virtual auditory stimuli have to be presented spatially through earphones or headphones, instead of loudspeakers. We investigated the possibility of an auditory BCI using the out-of-head sound localization technique, which enables us to present virtual auditory stimuli to users from any direction, through earphones. The feasibility of a BCI using this technique was evaluated in an EEG oddball experiment and offline analysis. A virtual auditory stimulus was presented to the subject from one of six directions. Using a support vector machine, we were able to classify whether the subject attended the direction of a presented stimulus from EEG signals. The mean accuracy across subjects was 70.0% in the single-trial classification. When we used trial-averaged EEG signals as inputs to the classifier, the mean accuracy across seven subjects reached 89.5% (for 10-trial averaging. Further analysis showed that the P300 event-related potential responses from 200 to 500 ms in central and posterior regions of the brain contributed to the classification. In comparison with the results obtained from a loudspeaker experiment, we confirmed that stimulus presentation by out-of-head sound localization achieved similar event-related potential responses and classification performances. These results suggest that out-of-head sound localization enables us to provide a high-performance and loudspeaker-less portable BCI system.

  16. An auditory multiclass brain-computer interface with natural stimuli: usability evaluation with healthy participants and a motor impaired end user

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    Nadine eSimon

    2015-01-01

    Full Text Available Brain-computer interfaces (BCIs can serve as muscle independent communication aids. Persons, who are unable to control their eye muscles (e.g. in the completely locked-in state or have severe visual impairments for other reasons, need BCI systems that do not rely on the visual modality. For this reason, BCIs that employ auditory stimuli were suggested. In this study, a multiclass BCI spelling system was implemented that uses animal voices with directional cues to code rows and columns of a letter matrix. To reveal possible training effects with the system, 11 healthy participants performed spelling tasks on two consecutive days. In a second step, the system was tested by a participant with amyotrophic lateral sclerosis (ALS in two sessions. In the first session, healthy participants spelled with an average accuracy of 76% (3.29 bits/min that increased to 90% (4.23 bits/min on the second day. Spelling accuracy by the participant with ALS was 20% in the first and 47% in the second session. The results indicate a strong training effect for both the healthy participants and the participant with ALS. While healthy participants reached high accuracies in the first session and second session, accuracies for the participant with ALS were not sufficient for satisfactory communication in both sessions. More training sessions might be needed to improve spelling accuracies. The study demonstrated the feasibility of the auditory BCI with healthy users and stresses the importance of training with auditory multiclass BCIs, especially for potential end-users of BCI with disease.

  17. Comparison of tactile, auditory and visual modality for brain-computer interface use: A case study with a patient in the locked-in state

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    Tobias eKaufmann

    2013-07-01

    Full Text Available This paper describes a case study with a patient in the classic locked-in state, who currently has no means of independent communication. Following a user-centered approach, we investigated event-related potentials elicited in different modalities for use in brain-computer interface systems. Such systems could provide her with an alternative communication channel. To investigate the most viable modality for achieving BCI based communication, classic oddball paradigms (1 rare and 1 frequent stimulus, ratio 1:5 in the visual, auditory and tactile modality were conducted (2 runs per modality. Classifiers were built on one run and tested offline on another run (and vice versa. In these paradigms, the tactile modality was clearly superior to other modalities, displaying high offline accuracy even when classification was performed on single trials only. Consequently, we tested the tactile paradigm online and the patient successfully selected targets without any error. Furthermore, we investigated use of the visual or tactile modality for different BCI systems with more than two selection options. In the visual modality, several BCI paradigms were tested offline. Neither matrix-based nor so-called gaze-independent paradigms constituted a means of control. These results may thus question the gaze-independence of current gaze-independent approaches to BCI. A tactile four-choice BCI resulted in high offline classification accuracies. Yet, online use raised various issues. Although performance was clearly above chance, practical daily life use appeared unlikely when compared to other communication approaches (e.g. partner scanning. Our results emphasize the need for user-centered design in BCI development including identification of the best stimulus modality for a particular user. Finally, the paper discusses feasibility of EEG-based BCI systems for patients in classic locked-in state and compares BCI to other AT solutions that we also tested during the

  18. Non-Stationary Brain Source Separation for Multi-Class Motor Imagery

    OpenAIRE

    Gouy-Pailler, Cedric; Congedo, Marco; Brunner, Clemens; Jutten, Christian; Pfurtscheller, Gert

    2010-01-01

    International audience This article describes a method to recover taskrelated brain sources in the context of multi-class Brain- Computer Interfaces (BCIs) based on non-invasive electroencephalography (EEG). We extend the method Joint Approximate Diagonalization (JAD) for spatial filtering using a maximum likelihood framework. This generic formulation (1) bridges the gap between the Common Spatial Patterns (CSP) and Blind Source Separation (BSS) of non-stationary sources, and (2) leads to ...

  19. Sistema Brain Computer Interface

    OpenAIRE

    Martín Barraza, Juan Ignacio

    2015-01-01

    En este trabajo de final de grado se realizará una aplicación de un sistema Brain Computer Interface en el cual, a partir del dipositivo Mind Wave de la compañía Neurosky, se pretenderá controlar el prototipo de una mano humana. Esta será controlada a partir de las ondas cerebrales medidas por el sensor que el dispositivo dispone. A continuación, la información captada por nuestro medidor de señales de electroencefalográficas será enviada por radiofrecuencia a un stick USB que viene incorpora...

  20. A multi-class large margin classifier

    Institute of Scientific and Technical Information of China (English)

    Liang TANG; Qi XUAN; Rong XIONG; Tie-jun WU; Jian CHU

    2009-01-01

    Currently there are two approaches for a multi-class support vector classifier (SVC). One is to construct and combine several binary classifiers while the other is to directly consider all classes of data in one optimization formulation. For a K-class problem (K>2), the first approach has to construct at least K classifiers, and the second approach has to solve a much larger op-timization problem proportional to K by the algorithms developed so far. In this paper, following the second approach, we present a novel multi-class large margin classifier (MLMC). This new machine can solve K-class problems in one optimization formula-tion without increasing the size of the quadratic programming (QP) problem proportional to K. This property allows us to construct just one classifier with as few variables in the QP problem as possible to classify multi-class data, and we can gain the advantage of speed from it especially when K is large. Our experiments indicate that MLMC almost works as well as (sometimes better than) many other multi-class SVCs for some benchmark data classification problems, and obtains a reasonable performance in face recognition application on the AR face database.

  1. Natural stimuli improve auditory BCIs with respect to ergonomics and performance

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    Höhne, Johannes; Krenzlin, Konrad; Dähne, Sven; Tangermann, Michael

    2012-08-01

    Moving from well-controlled, brisk artificial stimuli to natural and less-controlled stimuli seems counter-intuitive for event-related potential (ERP) studies. As natural stimuli typically contain a richer internal structure, they might introduce higher levels of variance and jitter in the ERP responses. Both characteristics are unfavorable for a good single-trial classification of ERPs in the context of a multi-class brain-computer interface (BCI) system, where the class-discriminant information between target stimuli and non-target stimuli must be maximized. For the application in an auditory BCI system, however, the transition from simple artificial tones to natural syllables can be useful despite the variance introduced. In the presented study, healthy users (N = 9) participated in an offline auditory nine-class BCI experiment with artificial and natural stimuli. It is shown that the use of syllables as natural stimuli does not only improve the users’ ergonomic ratings; also the classification performance is increased. Moreover, natural stimuli obtain a better balance in multi-class decisions, such that the number of systematic confusions between the nine classes is reduced. Hopefully, our findings may contribute to make auditory BCI paradigms more user friendly and applicable for patients.

  2. Emotional brain-computer interfaces

    NARCIS (Netherlands)

    Garcia Molina, G.; Tsoneva, T.; Nijholt, A.; Nijholt, A.; Heylen, D.K.J.

    2013-01-01

    Research in brain-computer interface (BCI) has significantly increased during the last few years. Additionally to their initial role as assisting devices for the physically challenged, BCIs are now proposed for a wider range of applications. As any human-machine interaction system, BCIs can benefit

  3. Metabolic Brain-Computer Interfaces

    OpenAIRE

    Sitaram, Ranganatha

    2010-01-01

    Brain-Computer Interfaces (BCI) utilise neurophysiological signals originating in the brain to activate or deactivate external devices or computers (Donoghue 2002; Wolpaw, Birbaumer et al. 2002; Nicolelis 2003; Birbaumer and Cohen 2007). The neuronal signals can be recorded from inside the brain (invasive BCIs) or outside (non-invasive BCIs) of the brain. Most BCIs developed so far have used operant training of direct neuroelectric responses, Electroencephalography (EEG) waves, event-related ...

  4. Brain Computer Interfaces, a Review

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    Nicolas-Alonso, Luis Fernando; Gomez-Gil, Jaime

    2012-01-01

    A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices. PMID:22438708

  5. Brain computer interfaces, a review.

    Science.gov (United States)

    Nicolas-Alonso, Luis Fernando; Gomez-Gil, Jaime

    2012-01-01

    A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

  6. Brain Computer Interfaces, a Review

    Directory of Open Access Journals (Sweden)

    Luis Fernando Nicolas-Alonso

    2012-01-01

    Full Text Available A brain-computer interface (BCI is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

  7. Brain-Computer Interfaces : Beyond Medical Applications

    NARCIS (Netherlands)

    Erp, J.B.F. van; Lotte, F.; Tangermann, M.

    2012-01-01

    Brain-computer interaction has already moved from assistive care to applications such as gaming. Improvements in usability, hardware, signal processing, and system integration should yield applications in other nonmedical areas.

  8. 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.

  9. Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials

    OpenAIRE

    Kaufmann, Tobias; Herweg, Andreas; Kübler, Andrea

    2014-01-01

    Background People with severe disabilities, e.g. due to neurodegenerative disease, depend on technology that allows for accurate wheelchair control. For those who cannot operate a wheelchair with a joystick, brain-computer interfaces (BCI) may offer a valuable option. Technology depending on visual or auditory input may not be feasible as these modalities are dedicated to processing of environmental stimuli (e.g. recognition of obstacles, ambient noise). Herein we thus validated the feasi...

  10. Probability output of multi-class support vector machines

    Institute of Scientific and Technical Information of China (English)

    忻栋; 吴朝晖; 潘云鹤

    2002-01-01

    A novel approach to interpret the outputs of multi-class support vector machines is proposed in this paper. Using the geometrical interpretation of the classifying heperplane and the distance of the pattern from the hyperplane, one can calculate the posterior probability in binary classification case. This paper focuses on the probability output in multi-class phase where both the one-against-one and one-against-rest strategies are considered. Experiment on the speaker verification showed that this method has high performance.

  11. The brain-computer interface cycle

    NARCIS (Netherlands)

    Gerven, M. van; Farquhar, J.D.R.; Schaefer, R.S.; Vlek, R.J.; Geuze, J.; Nijholt, A.; Ramsey, N.F.; Haselager, W.F.G.; Vuurpijl, L.G.; Gielen, S.C.A.M.; Desain, P.W.M.

    2009-01-01

    Brain-computer interfaces (BCIs) have attracted much attention recently, triggered by new scientific progress in understanding brain function and by impressive applications. The aim of this review is to give ail overview of the various steps in the BCI cycle, i.e., the loop from the measurement of b

  12. On Markovian multi-class, multi-server queueing

    NARCIS (Netherlands)

    Harten, van A.; Sleptchenko, A.

    2003-01-01

    Multi-class multi-server queueing problems are a generalisation of the well-known M/M/k queue to arrival processes with clients of N types that require exponentially distributed service with different average service times. In this paper, we give a procedure to construct exact solutions of the stati

  13. Brain-Computer Interfaces and Quantum Robots

    OpenAIRE

    Pessa, Eliano; Zizzi, Paola

    2009-01-01

    The actual (classical) Brain-Computer Interface attempts to use brain signals to drive suitable actuators performing the actions corresponding to subject's intention. However this goal is not fully reached, and when BCI works, it does only in particular situations. The reason of this unsatisfactory result is that intention cannot be conceived simply as a set of classical input-output relationships. It is therefore necessary to resort to quantum theory, allowing the occurrence of stable cohere...

  14. Human Behavior Classification Using Multi-Class Relevance Vector Machine

    Directory of Open Access Journals (Sweden)

    Yogameena, B.

    2010-01-01

    Full Text Available Problem statement: In computer vision and robotics, one of the typical tasks is to identify specific objects in an image and to determine each object’s position and orientation relative to coordinate system. This study presented a Multi-class Relevance Vector machine (RVM classification algorithm which classifies different human poses from a single stationary camera for video surveillance applications. Approach: First the foreground blobs and their edges are obtained. Then the relevance vector machine classification scheme classified the normal and abnormal behavior. Results: The performance proposed by our method was compared with Support Vector Machine (SVM and multi-class support vector machine. Experimental results showed the effectiveness of the method. Conclusion: It is evident that RVM has good accuracy and lesser computational than SVM.

  15. Multi-class texture analysis in colorectal cancer histology

    OpenAIRE

    Jakob Nikolas Kather; Cleo-Aron Weis; Francesco Bianconi; Melchers, Susanne M; Schad, Lothar R; Timo Gaiser; Alexander Marx; Frank Gerrit Zöllner

    2016-01-01

    Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no pu...

  16. Adaptive multiclass classification for brain computer interfaces.

    Science.gov (United States)

    Llera, A; Gómez, V; Kappen, H J

    2014-06-01

    We consider the problem of multiclass adaptive classification for brain-computer interfaces and propose the use of multiclass pooled mean linear discriminant analysis (MPMLDA), a multiclass generalization of the adaptation rule introduced by Vidaurre, Kawanabe, von Bünau, Blankertz, and Müller (2010) for the binary class setting. Using publicly available EEG data sets and tangent space mapping (Barachant, Bonnet, Congedo, & Jutten, 2012) as a feature extractor, we demonstrate that MPMLDA can significantly outperform state-of-the-art multiclass static and adaptive methods. Furthermore, efficient learning rates can be achieved using data from different subjects.

  17. The brain-computer interface cycle

    OpenAIRE

    Van Gerven, M.; Farquhar, J.D.R.; Schaefer, R.S.; Vlek, R.J.; Geuze, J.; Nijholt, A.; Ramsey, N.F.; Haselager, W.F.G.; Vuurpijl, L.G.; Gielen, S.C.A.M.; Desain, P.W.M.

    2009-01-01

    Brain-computer interfaces (BCIs) have attracted much attention recently, triggered by new scientific progress in understanding brain function and by impressive applications. The aim of this review is to give ail overview of the various steps in the BCI cycle, i.e., the loop from the measurement of brain activity, classification of data, feedback to the subject and the effect of feedback on brain activity. In this article we will review the critical steps of the BCI cycle, the present issues a...

  18. Brain emotional learning based Brain Computer Interface

    Directory of Open Access Journals (Sweden)

    Abdolreza Asadi Ghanbari

    2012-09-01

    Full Text Available A brain computer interface (BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction and classification operations. Classification is crucial as it has a substantial effect on the BCI speed and bit rate. Recent developments of brain-computer interfaces (BCIs bring forward some challenging problems to the machine learning community, of which classification of time-varying electrophysiological signals is a crucial one. Constructing adaptive classifiers is a promising approach to deal with this problem. In this paper, we introduce adaptive classifiers for classify electroencephalogram (EEG signals. The adaptive classifier is brain emotional learning based adaptive classifier (BELBAC, which is based on emotional learning process. The main purpose of this research is to use a structural model based on the limbic system of mammalian brain, for decision making and control engineering applications. We have adopted a network model developed by Moren and Balkenius, as a computational model that mimics amygdala, orbitofrontal cortex, thalamus, sensory input cortex and generally, those parts of the brain thought responsible for processing emotions. The developed method was compared with other methods used for EEG signals classification (support vector machine (SVM and two different neural network types (MLP, PNN. The result analysis demonstrated an efficiency of the proposed approach.

  19. Python executable script for estimating two effective parameters to individualize Brain-Computer Interfaces: Individual alpha frequency & neurophysiological predictor

    OpenAIRE

    Luz Maria Alonso-Valerdi

    2016-01-01

    A brain-computer interface (BCI) aims to establish communication between the human brain and a computing system so as to enable the interaction between an individual and his environment without using the brain output pathways. Individuals control a BCI system by modulating their brain signals through mental tasks (e.g., motor imagery or mental calculation) or sensory stimulation (e.g., auditory, visual or tactile). As users modulate their brain signals at different frequencies and at differen...

  20. Hardware enhance of brain computer interfaces

    Science.gov (United States)

    Wu, Jerry; Szu, Harold; Chen, Yuechen; Guo, Ran; Gu, Xixi

    2015-05-01

    The history of brain-computer interfaces (BCIs) starts with Hans Berger's discovery of the electrical activity of the human brain and the development of electroencephalography (EEG). Recent years, BCI researches are focused on Invasive, Partially invasive, and Non-invasive BCI. Furthermore, EEG can be also applied to telepathic communication which could provide the basis for brain-based communication using imagined speech. It is possible to use EEG signals to discriminate the vowels and consonants embedded in spoken and in imagined words and apply to military product. In this report, we begin with an example of using high density EEG with high electrode density and analysis the results by using BCIs. The BCIs in this work is enhanced by A field-programmable gate array (FPGA) board with optimized two dimension (2D) image Fast Fourier Transform (FFT) analysis.

  1. Brain-Computer Interfaces and Quantum Robots

    CERN Document Server

    Pessa, Eliano

    2009-01-01

    The actual (classical) Brain-Computer Interface attempts to use brain signals to drive suitable actuators performing the actions corresponding to subject's intention. However this goal is not fully reached, and when BCI works, it does only in particular situations. The reason of this unsatisfactory result is that intention cannot be conceived simply as a set of classical input-output relationships. It is therefore necessary to resort to quantum theory, allowing the occurrence of stable coherence phenomena, in turn underlying high-level mental processes such as intentions and strategies. More precisely, within the context of a dissipative Quantum Field Theory of brain operation it is possible to introduce generalized coherent states associated, within the framework of logic, to the assertions of a quantum metalanguage. The latter controls the quantum-mechanical computing corresponding to standard mental operation. It thus become possible to conceive a Quantum Cyborg in which a human mind controls, through a qu...

  2. An Educative Brain-Computer Interface

    CERN Document Server

    Sorudeykin, Kirill A

    2010-01-01

    In this paper we will describe all necessary parts of Brain-Computer Interface (BCI), such as source of signals, hardware, software, analysis, architectures of complete system. We also will go along various applications of BCI, view some subject fields and their specifics. After preface we will consider the main point of this work-concepts of using BCI in education. Represented direction of BCI development has not been reported prior. In this work a computer system, currently being elaborated in author's laboratory, will be specified. A purpose of it is to determine a degree of clearness of studied information for certain user according to their indications of brain electrical signals. On the basis of this information the system is able to find an optimal approach to interact with each single user. Feedback individualization leads to learning effectiveness increasing. Stated investigations will be supplemented by author's analytical reasoning on the nature of thinking process.

  3. Biased feedback in brain-computer interfaces

    Directory of Open Access Journals (Sweden)

    Barbero Álvaro

    2010-07-01

    Full Text Available Abstract Even though feedback is considered to play an important role in learning how to operate a brain-computer interface (BCI, to date no significant influence of feedback design on BCI-performance has been reported in literature. In this work, we adapt a standard motor-imagery BCI-paradigm to study how BCI-performance is affected by biasing the belief subjects have on their level of control over the BCI system. Our findings indicate that subjects already capable of operating a BCI are impeded by inaccurate feedback, while subjects normally performing on or close to chance level may actually benefit from an incorrect belief on their performance level. Our results imply that optimal feedback design in BCIs should take into account a subject's current skill level.

  4. Brain computer interface for operating a robot

    Science.gov (United States)

    Nisar, Humaira; Balasubramaniam, Hari Chand; Malik, Aamir Saeed

    2013-10-01

    A Brain-Computer Interface (BCI) is a hardware/software based system that translates the Electroencephalogram (EEG) signals produced by the brain activity to control computers and other external devices. In this paper, we will present a non-invasive BCI system that reads the EEG signals from a trained brain activity using a neuro-signal acquisition headset and translates it into computer readable form; to control the motion of a robot. The robot performs the actions that are instructed to it in real time. We have used the cognitive states like Push, Pull to control the motion of the robot. The sensitivity and specificity of the system is above 90 percent. Subjective results show a mixed trend of the difficulty level of the training activities. The quantitative EEG data analysis complements the subjective results. This technology may become very useful for the rehabilitation of disabled and elderly people.

  5. Brain-computer interfaces: a unique window into the hearing soul

    DEFF Research Database (Denmark)

    Treder, Matthias S.; Miklody, Daniel; Blankertz, Benjamin;

    2016-01-01

    quality measure'. We were able to show that for stimuli close to the perceptual threshold, there was sometimes a discrepancy between overt responses and brain responses, shedding light on subjects using different response criteria (e.g., more liberal or more conservative). To conclude, brain-computer...... of perceptual and cognitive biases. Furthermore, subjects can only report on stimuli if they have a clear percept of them. On the other hand, the electroencephalogram (EEG), the electrical brain activity measured with electrodes on the scalp, is a more direct measure. It allows us to tap into the ongoing neural...... auditory processing stream. In particular, it can tap brain processes that are pre-conscious or even unconscious, such as the earliest brain responses to sounds stimuli in primary auditory cortex. In a series of studies, we used a machine learning approach to show that the EEG can accurately reflect...

  6. Brain Computer Interface Boulevard of Smarter Thoughts

    Directory of Open Access Journals (Sweden)

    Sumit Ghulyani

    2012-10-01

    Full Text Available The Brain Computer Interface is a major breakthrough for the technical industry, medical world, military and the society on a whole. It is concerned with the control of devices around us such as computing gears & even automobiles in the near future without really the physical intervention of the user. It helps bridge the communication gap between the society and the disabled. This mainly lays its focus on people suffering from brainstem stroke, going through a spinal cord injury or even blindness. BCI helps such patients to retain or restore communication with the outside world through intelligent signals from the brain due to the high risk of paralysis under such circumstances. This is achieved by a signal acquisition technique and converting these signals available from the sensors placed on the scalp into real-time computer commands that can be visually operated and understood. It has nothing to do with the natural neural transmission of brain signals but extracts them with the help of sensors to be processed and direct the outputs to an external device. This may also prove to be a major military gadget where troops may communicate their thoughts in highly stressed situations without breaking the hush. But, as every technology have some merits and demerits, so does BCI.

  7. Beamforming in noninvasive brain-computer interfaces.

    Science.gov (United States)

    Grosse-Wentrup, Moritz; Liefhold, Christian; Gramann, Klaus; Buss, Martin

    2009-04-01

    Spatial filtering (SF) constitutes an integral part of building EEG-based brain-computer interfaces (BCIs). Algorithms frequently used for SF, such as common spatial patterns (CSPs) and independent component analysis, require labeled training data for identifying filters that provide information on a subject's intention, which renders these algorithms susceptible to overfitting on artifactual EEG components. In this study, beamforming is employed to construct spatial filters that extract EEG sources originating within predefined regions of interest within the brain. In this way, neurophysiological knowledge on which brain regions are relevant for a certain experimental paradigm can be utilized to construct unsupervised spatial filters that are robust against artifactual EEG components. Beamforming is experimentally compared with CSP and Laplacian spatial filtering (LP) in a two-class motor-imagery paradigm. It is demonstrated that beamforming outperforms CSP and LP on noisy datasets, while CSP and beamforming perform almost equally well on datasets with few artifactual trials. It is concluded that beamforming constitutes an alternative method for SF that might be particularly useful for BCIs used in clinical settings, i.e., in an environment where artifact-free datasets are difficult to obtain.

  8. Affective brain-computer music interfacing

    Science.gov (United States)

    Daly, Ian; Williams, Duncan; Kirke, Alexis; Weaver, James; Malik, Asad; Hwang, Faustina; Miranda, Eduardo; Nasuto, Slawomir J.

    2016-08-01

    Objective. We aim to develop and evaluate an affective brain-computer music interface (aBCMI) for modulating the affective states of its users. Approach. An aBCMI is constructed to detect a user's current affective state and attempt to modulate it in order to achieve specific objectives (for example, making the user calmer or happier) by playing music which is generated according to a specific affective target by an algorithmic music composition system and a case-based reasoning system. The system is trained and tested in a longitudinal study on a population of eight healthy participants, with each participant returning for multiple sessions. Main results. The final online aBCMI is able to detect its users current affective states with classification accuracies of up to 65% (3 class, p\\lt 0.01) and modulate its user's affective states significantly above chance level (p\\lt 0.05). Significance. Our system represents one of the first demonstrations of an online aBCMI that is able to accurately detect and respond to user's affective states. Possible applications include use in music therapy and entertainment.

  9. 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. PMID:26849869

  10. Multi-class texture analysis in colorectal cancer histology

    Science.gov (United States)

    Kather, Jakob Nikolas; Weis, Cleo-Aron; Bianconi, Francesco; Melchers, Susanne M.; Schad, Lothar R.; Gaiser, Timo; Marx, Alexander; Zöllner, Frank Gerrit

    2016-06-01

    Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no published results on multiclass texture separation. In this paper we present a new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue. We used this set to assess the classification performance of a wide range of texture descriptors and classifiers. As a result, we found an optimal classification strategy that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation from 96.9% to 98.6% accuracy and setting a new standard for multiclass tissue separation (87.4% accuracy for eight classes). We make our dataset of histological images publicly available under a Creative Commons license and encourage other researchers to use it as a benchmark for their studies.

  11. A tactile P300 brain-computer interface

    NARCIS (Netherlands)

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

    2010-01-01

    De werking van de eerste Brain-Computer-Interface gebaseerd op tactiele EEG response wordt gedemonstreerd en het effect van het aantal gebruikte vibro-tactiele tactoren en stimulus-timing parameters wordt onderzocht

  12. Inferring brain-computational mechanisms with models of activity measurements

    OpenAIRE

    Kriegeskorte, Nikolaus; Diedrichsen, Jörn

    2016-01-01

    High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer, which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each...

  13. Multi-class open set recognition for SAR imagery

    Science.gov (United States)

    Scherreik, Matthew; Rigling, Brian

    2016-05-01

    Supervised multi-class target recognition algorithms label an input pattern according to the most similar training class. Typically, the number of training classes is fixed and known a priori. In practice, however, a classifier may encounter novel targets that were not seen in training and label them incorrectly. Recent work in open set recognition (OSR) develops classifiers that can identify training targets as well as previously unknown targets. This results in a reduced number of forced misclassifications by "ejecting" targets that were not present in training. Several OSR algorithms are based on support vector machines (SVMs), namely, the 1-vs-set machine, W-SVM, and POS-SVM. The 1-vs-set machine, a linear classifier, forms a "lab" around each training class to discriminate it from the remaining training classes and limit the risk of labeling open space as target space. The W-SVM uses a novel dual-calibration technique to map the SVM outputs to posterior probabilities, which are then subjected to a pair of user-specified thresholds. The POS-SVM relies on a single calibration step, but features data-driven posterior probability thresholds that are chosen automatically. Both the W-SVM and POS-SVM have the capability to use nonlinear SVM kernel functions and perform particularly well with the popular Gaussian RBF kernel. Past works have shown that these algorithms can be effective for classifying ladar and IR images with a rejection option. In this paper, we apply these algorithms to the MSTAR SAR dataset and analyze their performance for classifying known targets and rejecting unknown targets in the presence of clutter.

  14. Auditory Display

    DEFF Research Database (Denmark)

    volume. The conference's topics include auditory exploration of data via sonification and audification; real time monitoring of multivariate date; sound in immersive interfaces and teleoperation; perceptual issues in auditory display; sound in generalized computer interfaces; technologies supporting...... auditory display creation; data handling for auditory display systems; applications of auditory display....

  15. Multi-class determination and confirmation of antibiotic residues in honey using LC-MS/MS

    Science.gov (United States)

    A multi-class method was developed for the determination and confirmation in honey of tetracyclines (chlortetracycline, doxycycline, oxytetracycline, and tetracycline), fluoroquinolones (ciprofloxacin, danofloxacin, difloxacin, enrofloxacin, and sarafloxacin), macrolides (tylosin), lincosamides (lin...

  16. A Simple Unifying Theory of Multi-Class Support Vector Machines

    OpenAIRE

    Guermeur, Yann

    2002-01-01

    Vapnik's statistical learning theory has mainly been developed for two types of problems: pattern recognition (computation of dichotomies) and regression (estimation of real-valued functions). Only in recent years has multi-class discriminant analysis been studied independently. Extending several standard results, among which a famous theorem by Bartlett, we have derived distribution-free uniform strong laws of large numbers devoted to multi-class large margin discriminant models. This techni...

  17. Flow-level convergence and insensitivity for multi-class queueing networks

    OpenAIRE

    Neil S. Walton

    2012-01-01

    We consider a multi-class queueing network as a model of packet transfer in a communication network. We define a second stochastic model as a model of document transfer in a communication network where the documents transferred have a general distribution. We prove the weak convergence of the multi-class queueing process to the document transfer process. Our convergence result allows the comparison of general document size distributions, and consequently, we prove general insensitivity result...

  18. Using multi-class queuing network to solve performance models of e-business sites.

    Science.gov (United States)

    Zheng, Xiao-ying; Chen, De-ren

    2004-01-01

    Due to e-business's variety of customers with different navigational patterns and demands, multi-class queuing network is a natural performance model for it. The open multi-class queuing network(QN) models are based on the assumption that no service center is saturated as a result of the combined loads of all the classes. Several formulas are used to calculate performance measures, including throughput, residence time, queue length, response time and the average number of requests. The solution technique of closed multi-class QN models is an approximate mean value analysis algorithm (MVA) based on three key equations, because the exact algorithm needs huge time and space requirement. As mixed multi-class QN models, include some open and some closed classes, the open classes should be eliminated to create a closed multi-class QN so that the closed model algorithm can be applied. Some corresponding examples are given to show how to apply the algorithms mentioned in this article. These examples indicate that multi-class QN is a reasonably accurate model of e-business and can be solved efficiently.

  19. Using multi-class queuing network to solve performance models of e-business sites

    Institute of Scientific and Technical Information of China (English)

    ZHENG Xiao-ying (郑小盈); CHEN De-ren (陈德人)

    2004-01-01

    Due to e-business's variety of customers with different navigational patterns and demands, multi-class queuing network is a natural performance model for it. The open multi-class queuing network(QN) models are based on the assumption that no service center is saturated as a result of the combined loads of all the classes. Several formulas are used to calculate performance measures, including throughput, residence time, queue length, response time and the average number of requests. The solution technique of closed multi-class QN models is an approximate mean value analysis algorithm (MVA) based on three key equations, because the exact algorithm needs huge time and space requirement. As mixed multi-class QN models, include some open and some closed classes, the open classes should be eliminated to create a closed multi-class QN so that the closed model algorithm can be applied. Some corresponding examples are given to show how to apply the algorithms mentioned in this article. These examples indicate that multi-class QN is a reasonably accurate model of e-business and can be solved efficiently.

  20. Auditory Processing Disorders

    Science.gov (United States)

    Auditory Processing Disorders Auditory processing disorders (APDs) are referred to by many names: central auditory processing disorders , auditory perceptual disorders , and central auditory disorders . APDs ...

  1. An associative Brain-Computer-Interface for acute stroke patients

    DEFF Research Database (Denmark)

    Mrachacz-Kersting, Natalie; Stevenson, Andrew James Thomas; Aliakbaryhosseinabadi, Susan;

    2017-01-01

    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...

  2. 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 stim

  3. Guest Editorial Brain-Computer Interface: Today and Tomorrow

    Institute of Scientific and Technical Information of China (English)

    De-Zhong Yao

    2009-01-01

    @@ As the invited editor of this special issue on brain- computer nterface (BCI), I am pleased to give a comment on the state-of-the-art with the introduction of recent advances made at the Chengdu BCI Group, University of Electronic Science and Technology of China (UESTC).

  4. Affective Brain-Computer Interfaces: Special Issue Editorial

    NARCIS (Netherlands)

    Mühl, Christian; Allison, Brandan; Nijholt, Anton; Chanel, Guillaume

    2014-01-01

    Over the last several years, brain-computer interface (BCI) research has grown well beyond initial efforts to provide basic communication for people with severe disabilities that prevent them from communicating otherwise. Since BCIs rely on direct measures of brain activity, users do not have to mov

  5. Tutorial: Signal Processing in Brain-Computer Interfaces

    NARCIS (Netherlands)

    Garcia Molina, G.

    2010-01-01

    Research in Electroencephalogram (EEG) based Brain-Computer Interfaces (BCIs) has been considerably expanding during the last few years. Such an expansion owes to a large extent to the multidisciplinary and challenging nature of BCI research. Signal processing undoubtedly constitutes an essential co

  6. Control-display mapping in brain-computer interfaces

    NARCIS (Netherlands)

    Thurlings, M.E.; Erp, J.B.F. van; Brouwer, A.-M.; Blankertz, B.; Werkhoven, P.J.

    2012-01-01

    Event-related potential (ERP) based brain-computer interfaces (BCIs) employ differences in brain responses to attended and ignored stimuli. When using a tactile ERP-BCI for navigation, mapping is required between navigation directions on a visual display and unambiguously corresponding tactile stimu

  7. Brain-Computer Interface Games: Towards a Framework

    NARCIS (Netherlands)

    Gürkök, Hayrettin; Nijholt, Anton; Poel, Mannes; Nakatsu, Ryohei; Rauterberg, Matthias; Ciancarini, Paolo

    2015-01-01

    The brain-computer interface (BCI) community has started to consider games as potential applications, while the game community has started to consider BCI as a game controller. However, there is a discrepancy between the BCI games developed by the two communities. This not only adds to the workload

  8. Real-time brain computer interface using imaginary movements

    DEFF Research Database (Denmark)

    El-Madani, Ahmad; Sørensen, Helge Bjarup Dissing; Kjær, Troels W.;

    2015-01-01

    Background: Brain Computer Interface (BCI) is the method of transforming mental thoughts and imagination into actions. A real-time BCI system can improve the quality of life of patients with severe neuromuscular disorders by enabling them to communicate with the outside world. In this paper...

  9. Brain-Computer Interfaces, Virtual Reality, and Videogames

    OpenAIRE

    Reilly, Richard

    2008-01-01

    PUBLISHED Major challenges must be tackled for brain-computer interfaces to mature into an established communications medium for VR applications, which will range from basic neuroscience studies to developing optimal peripherals and mental gamepads and more efficient brain-signal processing techniques.

  10. Competing and collaborating brains: multi-brain computer interfacing

    NARCIS (Netherlands)

    Nijholt, Anton; Hassanieu, Aboul Ella; Azar, Ahmad Taher

    2015-01-01

    In this chapter we survey the possibilities of brain-computer interface applications that assume two or more users, where at least one of the users’ brain activity is used as input to the application. Such ‘applications’ were already explored by artists who introduced artistic EEG applications in th

  11. Perspectives on User Experience Evaluation of Brain-Computer Interfaces

    NARCIS (Netherlands)

    Laar, van de Bram; Gürkök, Hayrettin; Plass-Oude Bos, Danny; Nijboer, Femke; Nijholt, Anton; Stephanidis, Constantine

    2011-01-01

    The research on brain-computer interfaces (BCIs) is pushing hard to bring technologies out of the lab and into society and onto the market. The nascent merge between the field of BCI and human-computer interaction (HCI) is paving the way for new applications such as BCI-controlled gaming. The evalua

  12. Data fusion for fault diagnosis using multi-class Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine information from several data sources. In the centralized scheme, all information from several data sources is centralized to construct an input space.Then a multi-class Support Vector Machine classifier is trained. In the distributed schemes, the individual data sources are processed separately and modelled by using the multi-class Support Vector Machine. Then new data fusion strategies are proposed to combine the information from the individual multi-class Support Vector Machine models. Our proposed fusion strategies take into account that an Support Vector Machine (SVM) classifier achieves classification by finding the optimal classification hyperplane with maximal margin. The proposed methods are applied for fault diagnosis of a diesel engine. The experimental results showed that almost all the proposed approaches can largely improve the diagnostic accuracy. The robustness of diagnosis is also improved because of the implementation of data fusion strategies. The proposed methods can also be applied in other fields.

  13. Using multi-class queuing network to solve performance models of e-business sites

    Institute of Scientific and Technical Information of China (English)

    郑小盈; 陈德人

    2004-01-01

    Due to e-business' s variety of customers with different navigational patterns and demands, multiclass queuing network is a natural performance model for it. The open multi-class queuing network(QN) models are based on the assumption that no service center is saturated as a result of the combined loads of all the classes. Several formulas are used to calculate performance measures, including throughput, residence time, queue length, response time and the average number of requests. The solution technique of closed multi-class QN models is an approximate mean value analysis algorithm (MVA) based on three key equations, because the exact algorithm needs huge time and space requirement. As mixed multi-class QN models, include some open and some closed classes, the open classes should be eliminated to create a closed multi-class QN so that the closed model algorithm can be applied. Some corresponding examples are given to show how to apply the algorithms mentioned in this article. These examples indicate that multi-class QN is a reasonably accurate model of e-business and can be solved efficiently.

  14. 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.

  15. Brain-computer interfaces for patients with disorders of consciousness.

    Science.gov (United States)

    Gibson, R M; Owen, A M; Cruse, D

    2016-01-01

    The disorders of consciousness refer to clinical conditions that follow a severe head injury. Patients diagnosed as in a vegetative state lack awareness, while patients diagnosed as in a minimally conscious state retain fluctuating awareness. However, it is a challenge to accurately diagnose these disorders with clinical assessments of behavior. To improve diagnostic accuracy, neuroimaging-based approaches have been developed to detect the presence or absence of awareness in patients who lack overt responsiveness. For the small subset of patients who retain awareness, brain-computer interfaces could serve as tools for communication and environmental control. Here we review the existing literature concerning the sensory and cognitive abilities of patients with disorders of consciousness with respect to existing brain-computer interface designs. We highlight the challenges of device development for this special population and address some of the most promising approaches for future investigations. PMID:27590972

  16. Flow-level convergence and insensitivity for multi-class queueing networks

    Directory of Open Access Journals (Sweden)

    Neil S. Walton

    2012-01-01

    Full Text Available We consider a multi-class queueing network as a model of packet transfer in a communication network. We define a second stochastic model as a model of document transfer in a communication network where the documents transferred have a general distribution. We prove the weak convergence of the multi-class queueing process to the document transfer process. Our convergence result allows the comparison of general document size distributions, and consequently, we prove general insensitivity results for the limit queueing process. We discuss how this separation of time-scales method of proving insensitivity may be applied to other insensitive queueing systems.

  17. Combination of Multi-class Probability Support Vector Machines for Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    To deal with multi-source multi-class classification problems, the method of combining multiple multi-class probability support vector machines (MPSVMs) using Bayesian theory is proposed in this paper. The MPSVMs are designed by mapping the output of standard support vector machines into a calibrated posterior probability by using a learned sigmoid function and then combining these learned binary-class probability SVMs. Two Bayes based methods for combining multiple MPSVMs are applied to improve the performance of classification. Our proposed methods are applied to fault diagnosis of a diesel engine. The experimental results show that the new methods can improve the accuracy and robustness of fault diagnosis.

  18. Robot Animals Based on Brain-Computer Interface

    Institute of Scientific and Technical Information of China (English)

    Yang Xia; Lei Lei; Tie-Jun Liu; De-Zhong Yao

    2009-01-01

    The study of robot animals based on brain-computer interface (BCI) technology is an important field in robots and neuroscience at present.In this paper,the development status at home and abroad of the motion control of robot based on BCI and principle of robot animals are introduced,then a new animals' behavior control method by photostimulation is presented.At last,the application prospect is provided.

  19. Towards brain-computer music interfaces: progress and challenges

    OpenAIRE

    Miranda, E. R.; Durrant, Simon; Anders, T.

    2008-01-01

    Brain-Computer Music Interface (BCMI) is a new research area that is emerging at the cross roads of neurobiology,engineering sciences and music. This research involves three major challenging problems: the extraction of meaningful control information from signals emanating directly from the brain, the design of generative music techniques that respond to such information, and the training of subjects to use the system. We have implemented a proof-of-concept BCMI system that is able to use ...

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

    OpenAIRE

    Noman eNaseer; Keum-Shik eHong

    2015-01-01

    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 mos...

  1. Effect of mindfulness meditation on brain-computer interface performance

    OpenAIRE

    Tan, Lee-Fan; Dienes, Zoltan; Jansari, Ashok S.; Goh, Sing-Yau

    2014-01-01

    Electroencephalogram based Brain-Computer Interfaces (BCIs) enable stroke and motor neuron disease patients to communicate and control devices. Mindfulness meditation has been claimed to enhance metacognitive regulation. The current study explores whether mindfulness meditation training can thus improve the performance of BCI users. To eliminate the possibility of expectation of improvement influencing the results, we introduced a music training condition. A norming study found that both...

  2. A Multi-purpose Brain-Computer Interface Output Device

    OpenAIRE

    Thompson, David E.; Huggins, Jane E

    2011-01-01

    While brain-computer interfaces (BCIs) are a promising alternative access pathway for individuals with severe motor impairments, many BCI systems are designed as standalone communication and control systems, rather than as interfaces to existing systems built for these purposes. While an individual communication and control system may be powerful or flexible, no single system can compete with the variety of options available in the commercial assistive technology (AT) market. BCIs could inste...

  3. On multi-class multi-server queueing and spare parts management

    NARCIS (Netherlands)

    Harten, van Aart; Sleptchenko, Andrei

    2000-01-01

    Multi-class multi-server queuing problems are a generalization of the wellknown M/M/k situation to arrival processes with clients of N types that require exponentially distributed service with different averaged service time. Problems of this sort arise naturally in various applications, such as spa

  4. A Randomized Heuristic for Kernel Parameter Selection with Large-scale Multi-class Data

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Abrahamsen, Trine Julie; Hansen, Lars Kai

    2011-01-01

    . In this contribution we investigate a novel randomized approach for kernel parameter selection in large-scale multi-class data. We fit a minimum enclosing ball to the class means in Reproducing Kernel Hilbert Spaces (RKHS), and use the radius as a quality measure of the space, defined by the kernel parameter. We apply...

  5. Multi-class remote sensing object recognition based on discriminative sparse representation.

    Science.gov (United States)

    Wang, Xin; Shen, Siqiu; Ning, Chen; Huang, Fengchen; Gao, Hongmin

    2016-02-20

    The automatic recognition of multi-class objects with various backgrounds is a big challenge in the field of remote sensing (RS) image analysis. In this paper, we propose a novel recognition framework for multi-class RS objects based on the discriminative sparse representation. In this framework, the recognition problem is implemented in two stages. In the first, or discriminative dictionary learning stage, considering the characterization of remote sensing objects, the scale-invariant feature transform descriptor is first combined with an improved bag-of-words model for multi-class objects feature extraction and representation. Then, information about each class of training samples is fused into the dictionary learning process; by using the K-singular value decomposition algorithm, a discriminative dictionary can be learned for sparse coding. In the second, or recognition, stage, to improve the computational efficiency, the phase spectrum of a quaternion Fourier transform model is applied to the test image to predict a small set of object candidate locations. Then, a multi-scale sliding window mechanism is utilized to scan the image over those candidate locations to obtain the object candidates (or objects of interest). Subsequently, the sparse coding coefficients of these candidates under the discriminative dictionary are mapped to the discriminative vectors that have a good ability to distinguish different classes of objects. Finally, multi-class object recognition can be accomplished by analyzing these vectors. The experimental results show that the proposed work outperforms a number of state-of-the-art methods for multi-class remote sensing object recognition. PMID:26906591

  6. 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

  7. Brain Computer Interface. Comparison of Neural Networks Classifiers.

    OpenAIRE

    Martínez Pérez, Jose Luis; Barrientos Cruz, Antonio

    2008-01-01

    Brain Computer Interface is an emerging technology that allows new output paths to communicate the user’s intentions without use of normal output ways, such as muscles or nerves (Wolpaw, J. R.; et al., 2002).In order to obtain its objective BCI devices shall make use of classifier which translate the inputs provided by user’s brain signal to commands for external devices. The primary uses of this technology will benefit persons with some kind blocking disease as for example: ALS, brainstem st...

  8. Robot Control Through Brain Computer Interface For Patterns Generation

    Science.gov (United States)

    Belluomo, P.; Bucolo, M.; Fortuna, L.; Frasca, M.

    2011-09-01

    A Brain Computer Interface (BCI) system processes and translates neuronal signals, that mainly comes from EEG instruments, into commands for controlling electronic devices. This system can allow people with motor disabilities to control external devices through the real-time modulation of their brain waves. In this context an EEG-based BCI system that allows creative luminous artistic representations is here presented. The system that has been designed and realized in our laboratory interfaces the BCI2000 platform performing real-time analysis of EEG signals with a couple of moving luminescent twin robots. Experiments are also presented.

  9. Implants and Decoding for Intracortical Brain Computer Interfaces

    OpenAIRE

    Homer, Mark L.; Nurmikko, Arto V.; Donoghue, John P.; Hochberg, Leigh R.

    2013-01-01

    Intracortical brain computer interfaces (iBCIs) are being developed to enable a person to drive an output device, such as a computer cursor, directly from their neural activity. One goal of the technology is to help people with severe paralysis or limb loss. Key elements of an iBCI are the implanted sensor that records the neural signals and the software which decodes the user’s intended movement from those signals. Here, we focus on recent advances in these two areas, with special attention ...

  10. A Novel Audiovisual Brain-Computer Interface and Its Application in Awareness Detection.

    Science.gov (United States)

    Wang, Fei; He, Yanbin; Pan, Jiahui; Xie, Qiuyou; Yu, Ronghao; Zhang, Rui; Li, Yuanqing

    2015-06-30

    Currently, detecting awareness in patients with disorders of consciousness (DOC) is a challenging task, which is commonly addressed through behavioral observation scales such as the JFK Coma Recovery Scale-Revised. Brain-computer interfaces (BCIs) provide an alternative approach to detect awareness in patients with DOC. However, these patients have a much lower capability of using BCIs compared to healthy individuals. This study proposed a novel BCI using temporally, spatially, and semantically congruent audiovisual stimuli involving numbers (i.e., visual and spoken numbers). Subjects were instructed to selectively attend to the target stimuli cued by instruction. Ten healthy subjects first participated in the experiment to evaluate the system. The results indicated that the audiovisual BCI system outperformed auditory-only and visual-only systems. Through event-related potential analysis, we observed audiovisual integration effects for target stimuli, which enhanced the discriminability between brain responses for target and nontarget stimuli and thus improved the performance of the audiovisual BCI. This system was then applied to detect the awareness of seven DOC patients, five of whom exhibited command following as well as number recognition. Thus, this audiovisual BCI system may be used as a supportive bedside tool for awareness detection in patients with DOC.

  11. A Novel Audiovisual Brain-Computer Interface and Its Application in Awareness Detection.

    Science.gov (United States)

    Wang, Fei; He, Yanbin; Pan, Jiahui; Xie, Qiuyou; Yu, Ronghao; Zhang, Rui; Li, Yuanqing

    2015-01-01

    Currently, detecting awareness in patients with disorders of consciousness (DOC) is a challenging task, which is commonly addressed through behavioral observation scales such as the JFK Coma Recovery Scale-Revised. Brain-computer interfaces (BCIs) provide an alternative approach to detect awareness in patients with DOC. However, these patients have a much lower capability of using BCIs compared to healthy individuals. This study proposed a novel BCI using temporally, spatially, and semantically congruent audiovisual stimuli involving numbers (i.e., visual and spoken numbers). Subjects were instructed to selectively attend to the target stimuli cued by instruction. Ten healthy subjects first participated in the experiment to evaluate the system. The results indicated that the audiovisual BCI system outperformed auditory-only and visual-only systems. Through event-related potential analysis, we observed audiovisual integration effects for target stimuli, which enhanced the discriminability between brain responses for target and nontarget stimuli and thus improved the performance of the audiovisual BCI. This system was then applied to detect the awareness of seven DOC patients, five of whom exhibited command following as well as number recognition. Thus, this audiovisual BCI system may be used as a supportive bedside tool for awareness detection in patients with DOC. PMID:26123281

  12. Encoder-decoder optimization for brain-computer interfaces.

    Directory of Open Access Journals (Sweden)

    Josh Merel

    2015-06-01

    Full Text Available Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model" and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.

  13. Collaborative brain-computer interface for aiding decision-making.

    Directory of Open Access Journals (Sweden)

    Riccardo Poli

    Full Text Available We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a simple matching task involving the rapid sequential presentation of pairs of visual patterns and the subsequent decision as whether the two patterns in a pair were the same or different. We recorded the response times of observers as well as a neural feature which predicts incorrect decisions and, thus, indirectly indicates the confidence of the decisions made by the observers. We then built a composite neuro-behavioural feature which optimally combines the two measures. For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features. Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule. An analysis of event-related potentials indicates that substantial differences are present in the proximity of the response for correct and incorrect trials, further corroborating the idea of using hybrids of brain-computer interfaces and traditional strategies for improving decision making.

  14. Fast Multi-class Dictionaries Learning with Geometrical Directions in MRI Reconstruction

    CERN Document Server

    Zhan, Zhifang; Guo, Di; Liu, Yunsong; Chen, Zhong; Qu, Xiaobo

    2016-01-01

    Objective: Improve the reconstructed image with fast and multi-class dictionaries learning when magnetic resonance imaging is accelerated by undersampling the k-space data. Methods: A fast orthogonal dictionary learning method is introduced into magnetic resonance image reconstruction to providing adaptive sparse representation of images. To enhance the sparsity, image is divided into classified patches according to the same geometrical direction and dictionary is trained within each class. A new sparse reconstruction model with the multi-class dictionaries is proposed and solved using a fast alternating direction method of multipliers. Results: Experiments on phantom and brain imaging data with acceleration factor up to 10 and various undersampling patterns are conducted. The proposed method is compared with state-of-the-art magnetic resonance image reconstruction methods. Conclusion: Artifacts are better suppressed and image edges are better preserved than the compared methods. Besides, the computation of t...

  15. Design of an EEG Preamplifier for Brain-Computer Interface

    Institute of Scientific and Technical Information of China (English)

    Xian-Jie Pu; Tie-Jun Liu; De-Zhong Yao

    2009-01-01

    As a non-invasive neurophysiological index for brain-computer interface (BCI),electro-encephalogram (EEG) attracts much attention at present.In order to have a portable BCI,a simple and efficient pre-amplifier is crucial in practice.In this work,a preamplifier based on the characteristics of EEG signals is designed,which consists of a highly symmetrical input stage,low-pass filter,50 Hz notch filter and a post amplifier.A prototype of this EEG module is fabricated and EEG data are obtained through an actual experiment.The results demonstrate that the EEG preamplifier will be a promising unit for BCI in the future.

  16. A brain-computer interface to support functional recovery

    DEFF Research Database (Denmark)

    Kjaer, Troels W; Sørensen, Helge Bjarup Dissing

    2013-01-01

    Brain-computer interfaces (BCI) register changes in brain activity and utilize this to control computers. The most widely used method is based on registration of electrical signals from the cerebral cortex using extracranially placed electrodes also called electroencephalography (EEG). The features...... extracted from the EEG may, besides controlling the computer, also be fed back to the patient for instance as visual input. This facilitates a learning process. BCI allow us to utilize brain activity in the rehabilitation of patients after stroke. The activity of the cerebral cortex varies with the type...... of movement we imagine, and by letting the patient know the type of brain activity best associated with the intended movement the rehabilitation process may be faster and more efficient. The focus of BCI utilization in medicine has changed in recent years. While we previously focused on devices facilitating...

  17. Towards perception awareness: Perceptual event detection for Brain computer interfaces.

    Science.gov (United States)

    Nejati, Hossein; Tsourides, Kleovoulos; Pomponiu, Victor; Ehrenberg, Evan C; Ngai-Man Cheung; Sinha, Pawan

    2015-08-01

    Brain computer interface (BCI) technology is becoming increasingly popular in many domains such as entertainment, mental state analysis, and rehabilitation. For robust performance in these domains, detecting perceptual events would be a vital ability, enabling adaptation to and act on the basis of user's perception of the environment. Here we present a framework to automatically mine spatiotemporal characteristics of a given perceptual event. As this "signature" is derived directly from subject's neural behavior, it can serve as a representation of the subject's perception of the targeted scenario, which in turn allows a BCI system to gain a new level of context awareness: perception awareness. As a proof of concept, we show the application of the proposed framework on MEG signal recordings from a face perception study, and the resulting temporal and spatial characteristics of the derived neural signature, as well as it's compatibility with the neuroscientific literature on face perception.

  18. Implants and Decoding for Intracortical Brain Computer Interfaces

    Science.gov (United States)

    Homer, Mark L.; Nurmikko, Arto V.; Donoghue, John P.; Hochberg, Leigh R.

    2014-01-01

    Intracortical brain computer interfaces (iBCIs) are being developed to enable a person to drive an output device, such as a computer cursor, directly from their neural activity. One goal of the technology is to help people with severe paralysis or limb loss. Key elements of an iBCI are the implanted sensor that records the neural signals and the software which decodes the user’s intended movement from those signals. Here, we focus on recent advances in these two areas, with special attention being placed on contributions that are or may soon be adopted by the iBCI research community. We discuss how these innovations increase the technology’s capability, accuracy, and longevity, all important steps that are expanding the range of possible future clinical applications. PMID:23862678

  19. Sensors and decoding for intracortical brain computer interfaces.

    Science.gov (United States)

    Homer, Mark L; Nurmikko, Arto V; Donoghue, John P; Hochberg, Leigh R

    2013-01-01

    Intracortical brain computer interfaces (iBCIs) are being developed to enable people to drive an output device, such as a computer cursor, directly from their neural activity. One goal of the technology is to help people with severe paralysis or limb loss. Key elements of an iBCI are the implanted sensor that records the neural signals and the software that decodes the user's intended movement from those signals. Here, we focus on recent advances in these two areas, placing special attention on contributions that are or may soon be adopted by the iBCI research community. We discuss how these innovations increase the technology's capability, accuracy, and longevity, all important steps that are expanding the range of possible future clinical applications.

  20. Brain-Computer Interfaces Revolutionizing Human-Computer Interaction

    CERN Document Server

    Graimann, Bernhard; Allison, Brendan

    2010-01-01

    A brain-computer interface (BCI) establishes a direct output channel between the human brain and external devices. BCIs infer user intent via direct measures of brain activity and thus enable communication and control without movement. This book, authored by experts in the field, provides an accessible introduction to the neurophysiological and signal-processing background required for BCI, presents state-of-the-art non-invasive and invasive approaches, gives an overview of current hardware and software solutions, and reviews the most interesting as well as new, emerging BCI applications. The book is intended not only for students and young researchers, but also for newcomers and other readers from diverse backgrounds keen to learn about this vital scientific endeavour.

  1. Effect of mindfulness meditation on brain-computer interface performance.

    Science.gov (United States)

    Tan, Lee-Fan; Dienes, Zoltan; Jansari, Ashok; Goh, Sing-Yau

    2014-01-01

    Electroencephalogram based brain-computer interfaces (BCIs) enable stroke and motor neuron disease patients to communicate and control devices. Mindfulness meditation has been claimed to enhance metacognitive regulation. The current study explores whether mindfulness meditation training can thus improve the performance of BCI users. To eliminate the possibility of expectation of improvement influencing the results, we introduced a music training condition. A norming study found that both meditation and music interventions elicited clear expectations for improvement on the BCI task, with the strength of expectation being closely matched. In the main 12 week intervention study, seventy-six healthy volunteers were randomly assigned to three groups: a meditation training group; a music training group; and a no treatment control group. The mindfulness meditation training group obtained a significantly higher BCI accuracy compared to both the music training and no-treatment control groups after the intervention, indicating effects of meditation above and beyond expectancy effects.

  2. Brain-computer interfaces current trends and applications

    CERN Document Server

    Azar, Ahmad

    2015-01-01

    The success of a BCI system depends as much on the system itself as on the user’s ability to produce distinctive EEG activity. BCI systems can be divided into two groups according to the placement of the electrodes used to detect and measure neurons firing in the brain. These groups are: invasive systems, electrodes are inserted directly into the cortex are used for single cell or multi unit recording, and electrocorticography (EcoG), electrodes are placed on the surface of the cortex (or dura); noninvasive systems, they are placed on the scalp and use electroencephalography (EEG) or magnetoencephalography (MEG) to detect neuron activity. The book is basically divided into three parts. The first part of the book covers the basic concepts and overviews of Brain Computer Interface. The second part describes new theoretical developments of BCI systems. The third part covers views on real applications of BCI systems.

  3. Improved Classification Methods for Brain Computer Interface System

    Directory of Open Access Journals (Sweden)

    YI Fang

    2012-03-01

    Full Text Available Brain computer interface (BCI aims at providing a new communication way without brain’s normal output through nerve and muscle. The electroencephalography (EEG has been widely used for BCI system because it is a non-invasive approach. For the EEG signals of left and right hand motor imagery, the event-related desynchronization (ERD and event-related synchronization(ERS are used as classification features in this paper. The raw data are transformed by nonlinear methods and classified by Fisher classifier. Compared with the linear methods, the classification accuracy can get an obvious increase to 86.25%. Two different nonlinear transform were arised and one of them is under the consideration of the relativity of two channels of EEG signals. With these nonlinear transform, the performance are also stable with the balance of two misclassifications.

  4. Quality Criteria Implementation for Brain Computed Tomography Examinations

    Energy Technology Data Exchange (ETDEWEB)

    Calzado, A.; Rodriguez, R.; Munoz, A

    1998-07-01

    The main aim of this study was to implement the quality criteria proposed by the European Commission for brain computed tomography (CT) examinations. The proposed criteria were evaluated for 102 brain CT examinations, with a special emphasis on the diagnostic and radiation dose requirements. The examinations were selected at random from brain examinations performed over a period of a month at eight scanners of the CT Pace range. Achievement of image criteria was evaluated by two independent observers. The most important preliminary findings concerning image criteria fulfilment or lack of it and disagreements between observers are presented and discussed. The mean values of the proposed dosimetric quantities are compared to the reference values. The examinations performed with and without injection of a contrast agent are also analysed in relation to dosimetric quantities and criteria fulfilment. (author)

  5. Evaluation of LDA Ensembles Classifiers for Brain Computer Interface

    Science.gov (United States)

    Arjona, Cristian; Pentácolo, José; Gareis, Iván; Atum, Yanina; Gentiletti, Gerardo; Acevedo, Rubén; Rufiner, Leonardo

    2011-12-01

    The Brain Computer Interface (BCI) translates brain activity into computer commands. To increase the performance of the BCI, to decode the user intentions it is necessary to get better the feature extraction and classification techniques. In this article the performance of a three linear discriminant analysis (LDA) classifiers ensemble is studied. The system based on ensemble can theoretically achieved better classification results than the individual counterpart, regarding individual classifier generation algorithm and the procedures for combine their outputs. Classic algorithms based on ensembles such as bagging and boosting are discussed here. For the application on BCI, it was concluded that the generated results using ER and AUC as performance index do not give enough information to establish which configuration is better.

  6. Perspectives and Potential of the Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    MUSSATTO, G. G.

    2014-06-01

    Full Text Available A Brain-Computer Interface (BCI, also known as Brain-Machine Interface, is a system that allows for the interaction between the user and its surroundings using control signals generated by his brain activity. The improvement of the research on BCI correlates mainly with the advances of Neurophisiology and Computer Science. Initial research was dedicated to the development of devices for the communication of individuals who lost voluntary muscle control but had no cognitive impairment. Nowadays, we find applications in the fields of mobility, communication and the treatment of diseases of user who may or may not have movement impairment. Considering the expansion scenario of the BCI applications, this paper presents a pedagogical description of the recent publication on this field of study. Hence, we descrive the basic concepts related to this research area, as well as some of its applications and limitations.

  7. Inferring brain-computational mechanisms with models of activity measurements.

    Science.gov (United States)

    Kriegeskorte, Nikolaus; Diedrichsen, Jörn

    2016-10-01

    High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.

  8. Probabilistic co-adaptive brain-computer interfacing

    Science.gov (United States)

    Bryan, Matthew J.; Martin, Stefan A.; Cheung, Willy; Rao, Rajesh P. N.

    2013-12-01

    Objective. Brain-computer interfaces (BCIs) are confronted with two fundamental challenges: (a) the uncertainty associated with decoding noisy brain signals, and (b) the need for co-adaptation between the brain and the interface so as to cooperatively achieve a common goal in a task. We seek to mitigate these challenges. Approach. We introduce a new approach to brain-computer interfacing based on partially observable Markov decision processes (POMDPs). POMDPs provide a principled approach to handling uncertainty and achieving co-adaptation in the following manner: (1) Bayesian inference is used to compute posterior probability distributions (‘beliefs’) over brain and environment state, and (2) actions are selected based on entire belief distributions in order to maximize total expected reward; by employing methods from reinforcement learning, the POMDP’s reward function can be updated over time to allow for co-adaptive behaviour. Main results. We illustrate our approach using a simple non-invasive BCI which optimizes the speed-accuracy trade-off for individual subjects based on the signal-to-noise characteristics of their brain signals. We additionally demonstrate that the POMDP BCI can automatically detect changes in the user’s control strategy and can co-adaptively switch control strategies on-the-fly to maximize expected reward. Significance. Our results suggest that the framework of POMDPs offers a promising approach for designing BCIs that can handle uncertainty in neural signals and co-adapt with the user on an ongoing basis. The fact that the POMDP BCI maintains a probability distribution over the user’s brain state allows a much more powerful form of decision making than traditional BCI approaches, which have typically been based on the output of classifiers or regression techniques. Furthermore, the co-adaptation of the system allows the BCI to make online improvements to its behaviour, adjusting itself automatically to the user’s changing

  9. Inferring brain-computational mechanisms with models of activity measurements.

    Science.gov (United States)

    Kriegeskorte, Nikolaus; Diedrichsen, Jörn

    2016-10-01

    High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'. PMID:27574316

  10. Papers from the Fifth International Brain-Computer Interface Meeting

    Science.gov (United States)

    Huggins, Jane E.; Wolpaw, Jonathan R.

    2014-06-01

    Brain-computer interfaces (BCIs), also known as brain-machine interfaces (BMIs), translate brain activity into new outputs that replace, restore, enhance, supplement or improve natural brain outputs. BCI research and development has grown rapidly for the past two decades. It is beginning to provide useful communication and control capacities to people with severe neuromuscular disabilities; and it is expanding into new areas such as neurorehabilitation that may greatly increase its clinical impact. At the same time, significant challenges remain, particularly in regard to translating laboratory advances into clinical use. The papers in this special section report some of the work presented at the Fifth International BCI Meeting held on 3-7 June 2013 at the Asilomar Conference Center in Pacific Grove, California, USA. Like its predecessors over the past 15 years, this meeting was supported by the National Institutes of Health, the National Science Foundation, and a variety of other governmental and private sponsors [1]. This fifth meeting was organized and managed by a program committee of BCI researchers from throughout the world [2]. It retained the distinctive retreat-style format developed by the Wadsworth Center researchers who organized and managed the first four meetings. The 301 attendees came from 165 research groups in 29 countries; 37% were students or postdoctoral fellows. Of more than 200 extended abstracts submitted for peer review, 25 were selected for oral presentation [3], and 181 were presented as posters [4] and published in the open-access conference proceedings [5]. The meeting featured 19 highly interactive workshops [6] covering the broad spectrum of BCI research and development, as well as many demonstrations of BCI systems and associated technology. Like the first four meetings, this one included attendees and embraced topics from across the broad spectrum of disciplines essential to effective BCI research and development, including

  11. Brain-computer interface based on generation of visual images.

    Directory of Open Access Journals (Sweden)

    Pavel Bobrov

    Full Text Available This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP classifier.

  12. Auditory Neuropathy

    Science.gov (United States)

    ... field differ in their opinions about the potential benefits of hearing aids, cochlear implants, and other technologies for people with auditory neuropathy. Some professionals report that hearing aids and personal listening devices such as frequency modulation (FM) systems are ...

  13. Neurological rehabilitation of stroke patients via motor imaginary-based brain-computer interface technology

    Institute of Scientific and Technical Information of China (English)

    Hongyu Sun; Yang Xiang; Mingdao Yang

    2011-01-01

    The present study utilized motor imaginary-based brain-computer interface technology combined with rehabilitation training in 20 stroke patients. Results from the Berg Balance Scale and the Holden Walking Classification were significantly greater at 4 weeks after treatment (P < 0.01), which suggested that motor imaginary-based brain-computer interface technology improved balance and walking in stroke patients.

  14. Multi-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification

    KAUST Repository

    Zhu, Xiaofeng

    2015-05-28

    This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classification rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms.

  15. Classification Of Multi-Classed Stochastic Images Buried In Additive Noise

    Science.gov (United States)

    Gu, Zu-Han; Lee, Sing H.

    1987-01-01

    The Optimal Correlation Filter for the discrimination or classification of multi-class stochastic images buried in additive noise is designed. We consider noise in images as the (K+1)th class of stochastic image so that the K-class with noise problem becomes a problem of (K+1)-classes: K-class without noise plus the (K+1)th class of noise. Experimental verifications with both low frequency background noise and high fre-quency shot noise show that the new filter design is reliable.

  16. Design and implementation of a large-scale multi-class text classifier

    Institute of Scientific and Technical Information of China (English)

    YU Shui; ZHANG Liang; MA Fan-yuan

    2005-01-01

    Although, researchers in the ATC field have done a wide range of work based on SVM, almost all existing approaches utilize an empirical model of selection algorithms. Their attempts to model automatic selection in practical, large-scale, text classification systems have been limited. In this paper, we propose a new model selection algorithm that utilizes the DDAG learning architecture. This architecture derives a new large-scale text classifier with very good performance. Experimental results show that the proposed algorithm has good efficiency and the necessary generalization capability while handling large-scale multi-class text classification tasks.

  17. A Review of Hybrid Brain-Computer Interface Systems

    Directory of Open Access Journals (Sweden)

    Setare Amiri

    2013-01-01

    Full Text Available Increasing number of research activities and different types of studies in brain-computer interface (BCI systems show potential in this young research area. Research teams have studied features of different data acquisition techniques, brain activity patterns, feature extraction techniques, methods of classifications, and many other aspects of a BCI system. However, conventional BCIs have not become totally applicable, due to the lack of high accuracy, reliability, low information transfer rate, and user acceptability. A new approach to create a more reliable BCI that takes advantage of each system is to combine two or more BCI systems with different brain activity patterns or different input signal sources. This type of BCI, called hybrid BCI, may reduce disadvantages of each conventional BCI system. In addition, hybrid BCIs may create more applications and possibly increase the accuracy and the information transfer rate. However, the type of BCIs and their combinations should be considered carefully. In this paper, after introducing several types of BCIs and their combinations, we review and discuss hybrid BCIs, different possibilities to combine them, and their advantages and disadvantages.

  18. EEG processing and its application in brain-computer interface

    Institute of Scientific and Technical Information of China (English)

    Wang Jing; Xu Guanghua; Xie Jun; Zhang Feng; Li Lili; Han Chengcheng; Li Yeping; Sun Jingjing

    2013-01-01

    Electroencephalogram (EEG) is an efficient tool in exploring human brains.It plays a very important role in diagnosis of disorders related to epilepsy and development of new interaction techniques between machines and human beings,namely,brain-computer interface (BCI).The purpose of this review is to illustrate the recent researches in EEG processing and EEG-based BCI.First,we outline several methods in removing artifacts from EEGs,and classical algorithms for fatigue detection are discussed.Then,two BCI paradigms including motor imagery and steady-state motion visual evoked potentials (SSMVEP) produced by oscillating Newton' s rings are introduced.Finally,BCI systems including wheelchair controlling and electronic car navigation are elaborated.As a new technique to control equipments,BCI has promising potential in rehabilitation of disorders in central nervous system,such as stroke and spinal cord injury,treatment of attention deficit hyperactivity disorder (ADHD) in children and development of novel games such as brain-controlled auto racings.

  19. Vibrotactile Feedback for Brain-Computer Interface Operation

    Directory of Open Access Journals (Sweden)

    Febo Cincotti

    2007-01-01

    Full Text Available To be correctly mastered, brain-computer interfaces (BCIs need an uninterrupted flow of feedback to the user. This feedback is usually delivered through the visual channel. Our aim was to explore the benefits of vibrotactile feedback during users' training and control of EEG-based BCI applications. A protocol for delivering vibrotactile feedback, including specific hardware and software arrangements, was specified. In three studies with 33 subjects (including 3 with spinal cord injury, we compared vibrotactile and visual feedback, addressing: (I the feasibility of subjects' training to master their EEG rhythms using tactile feedback; (II the compatibility of this form of feedback in presence of a visual distracter; (III the performance in presence of a complex visual task on the same (visual or different (tactile sensory channel. The stimulation protocol we developed supports a general usage of the tactors; preliminary experimentations. All studies indicated that the vibrotactile channel can function as a valuable feedback modality with reliability comparable to the classical visual feedback. Advantages of using a vibrotactile feedback emerged when the visual channel was highly loaded by a complex task. In all experiments, vibrotactile feedback felt, after some training, more natural for both controls and SCI users.

  20. A bidirectional brain-computer interface for effective epilepsy control

    Institute of Scientific and Technical Information of China (English)

    Yu QI; Fei-qiang MA; Ting-ting GE; Yue-ming WANG; Jun-ming ZHU; Jian-min ZHANG; Xiao-xiang ZHENG; Zhao-hui WU

    2014-01-01

    Brain-computer interfaces (BCIs) can provide direct bidirectional communication between the brain and a machine. Recently, the BCI technique has been used in seizure control. Usually, a closed-loop system based on BCI is set up which delivers a therapic electrical stimulus only in response to seizure onsets. In this way, the side effects of neurostimulation can be greatly reduced. In this paper, a new BCI-based responsive stimulation system is proposed. With an efficient morphology-based seizure detector, seizure events can be identifi ed in the early stages which trigger electrical stimulations to be sent to the cortex of the brain. The proposed system was tested on rats with penicillin-induced epileptic seizures. Online experiments show that 83%of the seizures could be detected successfully with a short average time delay of 3.11 s. With the therapy of the BCI-based seizure control system, most seizures were suppressed within 10 s. Compared with the control group, the average seizure duration was reduced by 30.7%. Therefore, the proposed system can control epileptic seizures effectively and has potential in clinical applications.

  1. Adaptive Offset Correction for Intracortical Brain Computer Interfaces

    Science.gov (United States)

    Homer, Mark L.; Perge, János A.; Black, Michael J.; Harrison, Matthew T.; Cash, Sydney S.; Hochberg, Leigh R.

    2014-01-01

    Intracortical brain computer interfaces (iBCIs) decode intended movement from neural activity for the control of external devices such as a robotic arm. Standard approaches include a calibration phase to estimate decoding parameters. During iBCI operation, the statistical properties of the neural activity can depart from those observed during calibration, sometimes hindering a user’s ability to control the iBCI. To address this problem, we adaptively correct the offset terms within a Kalman filter decoder via penalized maximum likelihood estimation. The approach can handle rapid shifts in neural signal behavior (on the order of seconds) and requires no knowledge of the intended movement. The algorithm, called MOCA, was tested using simulated neural activity and evaluated retrospectively using data collected from two people with tetraplegia operating an iBCI. In 19 clinical research test cases, where a nonadaptive Kalman filter yielded relatively high decoding errors, MOCA significantly reduced these errors (10.6 ±10.1%; p<0.05, pairwise t-test). MOCA did not significantly change the error in the remaining 23 cases where a nonadaptive Kalman filter already performed well. These results suggest that MOCA provides more robust decoding than the standard Kalman filter for iBCIs. PMID:24196868

  2. Multiresolution analysis over simple graphs for brain computer interfaces

    Science.gov (United States)

    Asensio-Cubero, J.; Gan, J. Q.; Palaniappan, R.

    2013-08-01

    Objective. Multiresolution analysis (MRA) offers a useful framework for signal analysis in the temporal and spectral domains, although commonly employed MRA methods may not be the best approach for brain computer interface (BCI) applications. This study aims to develop a new MRA system for extracting tempo-spatial-spectral features for BCI applications based on wavelet lifting over graphs. Approach. This paper proposes a new graph-based transform for wavelet lifting and a tailored simple graph representation for electroencephalography (EEG) data, which results in an MRA system where temporal, spectral and spatial characteristics are used to extract motor imagery features from EEG data. The transformed data is processed within a simple experimental framework to test the classification performance of the new method. Main Results. The proposed method can significantly improve the classification results obtained by various wavelet families using the same methodology. Preliminary results using common spatial patterns as feature extraction method show that we can achieve comparable classification accuracy to more sophisticated methodologies. From the analysis of the results we can obtain insights into the pattern development in the EEG data, which provide useful information for feature basis selection and thus for improving classification performance. Significance. Applying wavelet lifting over graphs is a new approach for handling BCI data. The inherent flexibility of the lifting scheme could lead to new approaches based on the hereby proposed method for further classification performance improvement.

  3. Comparing Different Classifiers in Sensory Motor Brain Computer Interfaces.

    Directory of Open Access Journals (Sweden)

    Hossein Bashashati

    Full Text Available A problem that impedes the progress in Brain-Computer Interface (BCI research is the difficulty in reproducing the results of different papers. Comparing different algorithms at present is very difficult. Some improvements have been made by the use of standard datasets to evaluate different algorithms. However, the lack of a comparison framework still exists. In this paper, we construct a new general comparison framework to compare different algorithms on several standard datasets. All these datasets correspond to sensory motor BCIs, and are obtained from 21 subjects during their operation of synchronous BCIs and 8 subjects using self-paced BCIs. Other researchers can use our framework to compare their own algorithms on their own datasets. We have compared the performance of different popular classification algorithms over these 29 subjects and performed statistical tests to validate our results. Our findings suggest that, for a given subject, the choice of the classifier for a BCI system depends on the feature extraction method used in that BCI system. This is in contrary to most of publications in the field that have used Linear Discriminant Analysis (LDA as the classifier of choice for BCI systems.

  4. Language Model Applications to Spelling with Brain-Computer Interfaces

    Directory of Open Access Journals (Sweden)

    Anderson Mora-Cortes

    2014-03-01

    Full Text Available Within the Ambient Assisted Living (AAL community, Brain-Computer Interfaces (BCIs have raised great hopes as they provide alternative communication means for persons with disabilities bypassing the need for speech and other motor activities. Although significant advancements have been realized in the last decade, applications of language models (e.g., word prediction, completion have only recently started to appear in BCI systems. The main goal of this article is to review the language model applications that supplement non-invasive BCI-based communication systems by discussing their potential and limitations, and to discern future trends. First, a brief overview of the most prominent BCI spelling systems is given, followed by an in-depth discussion of the language models applied to them. These language models are classified according to their functionality in the context of BCI-based spelling: the static/dynamic nature of the user interface, the use of error correction and predictive spelling, and the potential to improve their classification performance by using language models. To conclude, the review offers an overview of the advantages and challenges when implementing language models in BCI-based communication systems when implemented in conjunction with other AAL technologies.

  5. Comparing Different Classifiers in Sensory Motor Brain Computer Interfaces.

    Science.gov (United States)

    Bashashati, Hossein; Ward, Rabab K; Birch, Gary E; Bashashati, Ali

    2015-01-01

    A problem that impedes the progress in Brain-Computer Interface (BCI) research is the difficulty in reproducing the results of different papers. Comparing different algorithms at present is very difficult. Some improvements have been made by the use of standard datasets to evaluate different algorithms. However, the lack of a comparison framework still exists. In this paper, we construct a new general comparison framework to compare different algorithms on several standard datasets. All these datasets correspond to sensory motor BCIs, and are obtained from 21 subjects during their operation of synchronous BCIs and 8 subjects using self-paced BCIs. Other researchers can use our framework to compare their own algorithms on their own datasets. We have compared the performance of different popular classification algorithms over these 29 subjects and performed statistical tests to validate our results. Our findings suggest that, for a given subject, the choice of the classifier for a BCI system depends on the feature extraction method used in that BCI system. This is in contrary to most of publications in the field that have used Linear Discriminant Analysis (LDA) as the classifier of choice for BCI systems.

  6. Optimization of Electrode Channels in Brain Computer Interfaces

    Science.gov (United States)

    Kamrunnahar, M.; Dias, N. S.; Schiff, S. J.

    2010-01-01

    What is the optimal number of electrodes one can use in discrimination of tasks for a Brain Computer Interface(BCI)? To address this question, the number and location of scalp electrodes in the acquisition of human electroencephalography (EEG) and discrimination of motor imagery tasks were optimized by using a systematic optimization approach. The systematic analysis results in the most reliable procedure in electrode optimization as well as a validating means for the other feature selection techniques. We acquired human scalp EEG in response to cue-based motor imagery tasks. We employed a systematic analysis by using all possible combinations of the channels and calculating task discrimination errors for each of these combinations by using linear discriminant analysis (LDA) for feature classification. Channel combination that resulted in the smallest discrimination error was selected as the optimum number of channels to be used in BCI applications. Results from the systematic analysis were compared with another feature selection algorithm: forward stepwise feature selection combined with LDA feature classification. Our results demonstrate the usefulness of the fully optimized technique for a reliable selection of scalp electrodes in BCI applications. PMID:19964437

  7. Maze learning by a hybrid brain-computer system

    Science.gov (United States)

    Wu, Zhaohui; Zheng, Nenggan; Zhang, Shaowu; Zheng, Xiaoxiang; Gao, Liqiang; Su, Lijuan

    2016-09-01

    The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity. Here, we show how rule operations conducted by computing components contribute to a novel hybrid brain-computer system, i.e., ratbots, exhibit superior learning abilities in a maze learning task, even when their vision and whisker sensation were blocked. We anticipate that our study will encourage other researchers to investigate combinations of various rule operations and other artificial intelligence algorithms with the learning and memory processes of organic brains to develop more powerful cyborg intelligence systems. Our results potentially have profound implications for a variety of applications in intelligent systems and neural rehabilitation.

  8. Modern Electrophysiological Methods for Brain-Computer Interfaces

    Directory of Open Access Journals (Sweden)

    Rolando Grave de Peralta Menendez

    2007-01-01

    Full Text Available Modern electrophysiological studies in animals show that the spectrum of neural oscillations encoding relevant information is broader than previously thought and that many diverse areas are engaged for very simple tasks. However, EEG-based brain-computer interfaces (BCI still employ as control modality relatively slow brain rhythms or features derived from preselected frequencies and scalp locations. Here, we describe the strategy and the algorithms we have developed for the analysis of electrophysiological data and demonstrate their capacity to lead to faster accurate decisions based on linear classifiers. To illustrate this strategy, we analyzed two typical BCI tasks. (1 Mu-rhythm control of a cursor movement by a paraplegic patient. For this data, we show that although the patient received extensive training in mu-rhythm control, valuable information about movement imagination is present on the untrained high-frequency rhythms. This is the first demonstration of the importance of high-frequency rhythms in imagined limb movements. (2 Self-paced finger tapping task in three healthy subjects including the data set used in the BCI-2003 competition. We show that by selecting electrodes and frequency ranges based on their discriminative power, the classification rates can be systematically improved with respect to results published thus far.

  9. 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.

  10. [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). PMID:27188145

  11. Maze learning by a hybrid brain-computer system

    Science.gov (United States)

    Wu, Zhaohui; Zheng, Nenggan; Zhang, Shaowu; Zheng, Xiaoxiang; Gao, Liqiang; Su, Lijuan

    2016-01-01

    The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity. Here, we show how rule operations conducted by computing components contribute to a novel hybrid brain-computer system, i.e., ratbots, exhibit superior learning abilities in a maze learning task, even when their vision and whisker sensation were blocked. We anticipate that our study will encourage other researchers to investigate combinations of various rule operations and other artificial intelligence algorithms with the learning and memory processes of organic brains to develop more powerful cyborg intelligence systems. Our results potentially have profound implications for a variety of applications in intelligent systems and neural rehabilitation. PMID:27619326

  12. Brain computer interface to enhance episodic memory in human participants

    Directory of Open Access Journals (Sweden)

    John F Burke

    2015-01-01

    Full Text Available Recent research has revealed that neural oscillations in the theta (4-8 Hz and alpha (9-14 Hz bands are predictive of future success in memory encoding. Because these signals occur before the presentation of an upcoming stimulus, they are considered stimulus-independent in that they correlate with enhanced memory encoding independent of the item being encoded. Thus, such stimulus-independent activity has important implications for the neural mechanisms underlying episodic memory as well as the development of cognitive neural prosthetics. Here, we developed a brain computer interface (BCI to test the ability of such pre-stimulus activity to modulate subsequent memory encoding. We recorded intracranial electroencephalography (iEEG in neurosurgical patients as they performed a free recall memory task, and detected iEEG theta and alpha oscillations that correlated with optimal memory encoding. We then used these detected oscillatory changes to trigger the presentation of items in the free recall task. We found that item presentation contingent upon the presence of prestimulus theta and alpha oscillations modulated memory performance in more sessions than expected by chance. Our results suggest that an electrophysiological signal may be causally linked to a specific behavioral condition, and contingent stimulus presentation has the potential to modulate human memory encoding.

  13. Brain-computer interfaces for communication and rehabilitation.

    Science.gov (United States)

    Chaudhary, Ujwal; Birbaumer, Niels; Ramos-Murguialday, Ander

    2016-09-01

    Brain-computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs. PMID:27539560

  14. [Artificial Feedback for Invasive Brain-Computer Interfaces].

    Science.gov (United States)

    Badakva, A M; Miller, N V; Zobova, L N

    2016-01-01

    During the last two decades, considerable progress has been made in the studies of brain-computer interfaces (BCIs)--devices in which motor signals from the brain are registered by multi-electrode arrays and transformed into commands for articial actuators such as cursors and robotic devices. This review is focused on one problem. Voluntary motor control is based on neurophysiological processes which depend heavily on the afferent innervation of skin, muscles and joints. Thus, invasive BCI has to be based on a bidirectional system in which motor control signals are registered by multi-channel micro-electrodes implanted in motor areas, while tactile, proprioceptive and other useful signals are transported back to the brain through spatial-temporal patterns of intracortical microstimulation (ICMS) delivered to sensory areas. In general, the studies of invasive BCIs have advanced in several directions. The progress of BCIs with articial sensory feedback will not only help patients, but will also expand knowledge base in the field of human cortical functions. PMID:27188155

  15. Maze learning by a hybrid brain-computer system.

    Science.gov (United States)

    Wu, Zhaohui; Zheng, Nenggan; Zhang, Shaowu; Zheng, Xiaoxiang; Gao, Liqiang; Su, Lijuan

    2016-01-01

    The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity. Here, we show how rule operations conducted by computing components contribute to a novel hybrid brain-computer system, i.e., ratbots, exhibit superior learning abilities in a maze learning task, even when their vision and whisker sensation were blocked. We anticipate that our study will encourage other researchers to investigate combinations of various rule operations and other artificial intelligence algorithms with the learning and memory processes of organic brains to develop more powerful cyborg intelligence systems. Our results potentially have profound implications for a variety of applications in intelligent systems and neural rehabilitation. PMID:27619326

  16. A brain-computer interface to support functional recovery.

    Science.gov (United States)

    Kjaer, Troels W; Sørensen, Helge B

    2013-01-01

    Brain-computer interfaces (BCI) register changes in brain activity and utilize this to control computers. The most widely used method is based on registration of electrical signals from the cerebral cortex using extracranially placed electrodes also called electroencephalography (EEG). The features extracted from the EEG may, besides controlling the computer, also be fed back to the patient for instance as visual input. This facilitates a learning process. BCI allow us to utilize brain activity in the rehabilitation of patients after stroke. The activity of the cerebral cortex varies with the type of movement we imagine, and by letting the patient know the type of brain activity best associated with the intended movement the rehabilitation process may be faster and more efficient. The focus of BCI utilization in medicine has changed in recent years. While we previously focused on devices facilitating communication in the rather few patients with locked-in syndrome, much interest is now devoted to the therapeutic use of BCI in rehabilitation. For this latter group of patients, the device is not intended to be a lifelong assistive companion but rather a 'teacher' during the rehabilitation period. PMID:23859968

  17. An MEG-based brain-computer interface (BCI).

    Science.gov (United States)

    Mellinger, Jürgen; Schalk, Gerwin; Braun, Christoph; Preissl, Hubert; Rosenstiel, Wolfgang; Birbaumer, Niels; Kübler, Andrea

    2007-07-01

    Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography (EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.

  18. A multi-purpose brain-computer interface output device.

    Science.gov (United States)

    Thompson, David E; Huggins, Jane E

    2011-10-01

    While brain-computer interfaces (BCIs) are a promising alternative access pathway for individuals with severe motor impairments, many BCI systems are designed as stand-alone communication and control systems, rather than as interfaces to existing systems built for these purposes. An individual communication and control system may be powerful or flexible, but no single system can compete with the variety of options available in the commercial assistive technology (AT) market. BCls could instead be used as an interface to these existing AT devices and products, which are designed for improving access and agency of people with disabilities and are highly configurable to individual user needs. However, interfacing with each AT device and program requires significant time and effort on the part of researchers and clinicians. This work presents the Multi-Purpose BCI Output Device (MBOD), a tool to help researchers and clinicians provide BCI control of many forms of AT in a plug-and-play fashion, i.e., without the installation of drivers or software on the AT device, and a proof-of-concept of the practicality of such an approach. The MBOD was designed to meet the goals of target device compatibility, BCI input device compatibility, convenience, and intuitive command structure. The MBOD was successfully used to interface a BCI with multiple AT devices (including two wheelchair seating systems), as well as computers running Windows (XP and 7), Mac and Ubuntu Linux operating systems.

  19. P300 brain computer interface: current challenges and emerging trends

    Directory of Open Access Journals (Sweden)

    Reza eFazel-Rezai

    2012-07-01

    Full Text Available A brain-computer interface (BCI enables communication without movement based on brain signals measured with electroencephalography (EEG. BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP, steady state visual evoked potential (SSVEP, or event related desynchronization (ERD. Although P300 BCIs were introduced over twenty years ago, the past few years have seen a strong increase in P300 BCI research. This closed-loop BCI approach relies on the P300 and other components of the event-related potential (ERP, based on an oddball paradigm presented to the subject. In this paper, we overview the current status of P300 BCI technology, and then discuss new directions: paradigms for eliciting P300s; signal processing methods; applications; and hybrid BCIs. We conclude that P300 BCIs are quite promising, as several emerging directions have not yet been fully explored and could lead to improvements in bit rate, reliability, usability, and flexibility.

  20. TOPICAL REVIEW: The brain-computer interface cycle

    Science.gov (United States)

    Gerven, Marcel van; Farquhar, Jason; Schaefer, Rebecca; Vlek, Rutger; Geuze, Jeroen; Nijholt, Anton; Ramsey, Nick; Haselager, Pim; Vuurpijl, Louis; Gielen, Stan; Desain, Peter

    2009-08-01

    Brain-computer interfaces (BCIs) have attracted much attention recently, triggered by new scientific progress in understanding brain function and by impressive applications. The aim of this review is to give an overview of the various steps in the BCI cycle, i.e., the loop from the measurement of brain activity, classification of data, feedback to the subject and the effect of feedback on brain activity. In this article we will review the critical steps of the BCI cycle, the present issues and state-of-the-art results. Moreover, we will develop a vision on how recently obtained results may contribute to new insights in neurocognition and, in particular, in the neural representation of perceived stimuli, intended actions and emotions. Now is the right time to explore what can be gained by embracing real-time, online BCI and by adding it to the set of experimental tools already available to the cognitive neuroscientist. We close by pointing out some unresolved issues and present our view on how BCI could become an important new tool for probing human cognition.

  1. Toward unsupervised adaptation of LDA for brain-computer interfaces.

    Science.gov (United States)

    Vidaurre, C; Kawanabe, M; von Bünau, P; Blankertz, B; Müller, K R

    2011-03-01

    There is a step of significant difficulty experienced by brain-computer interface (BCI) users when going from the calibration recording to the feedback application. This effect has been previously studied and a supervised adaptation solution has been proposed. In this paper, we suggest a simple unsupervised adaptation method of the linear discriminant analysis (LDA) classifier that effectively solves this problem by counteracting the harmful effect of nonclass-related nonstationarities in electroencephalography (EEG) during BCI sessions performed with motor imagery tasks. For this, we first introduce three types of adaptation procedures and investigate them in an offline study with 19 datasets. Then, we select one of the proposed methods and analyze it further. The chosen classifier is offline tested in data from 80 healthy users and four high spinal cord injury patients. Finally, for the first time in BCI literature, we apply this unsupervised classifier in online experiments. Additionally, we show that its performance is significantly better than the state-of-the-art supervised approach.

  2. A heuristic method for consumable resource allocation in multi-class dynamic PERT networks

    Science.gov (United States)

    Yaghoubi, Saeed; Noori, Siamak; Mazdeh, Mohammad Mahdavi

    2013-06-01

    This investigation presents a heuristic method for consumable resource allocation problem in multi-class dynamic Project Evaluation and Review Technique (PERT) networks, where new projects from different classes (types) arrive to system according to independent Poisson processes with different arrival rates. Each activity of any project is operated at a devoted service station located in a node of the network with exponential distribution according to its class. Indeed, each project arrives to the first service station and continues its routing according to precedence network of its class. Such system can be represented as a queuing network, while the discipline of queues is first come, first served. On the basis of presented method, a multi-class system is decomposed into several single-class dynamic PERT networks, whereas each class is considered separately as a minisystem. In modeling of single-class dynamic PERT network, we use Markov process and a multi-objective model investigated by Azaron and Tavakkoli-Moghaddam in 2007. Then, after obtaining the resources allocated to service stations in every minisystem, the final resources allocated to activities are calculated by the proposed method.

  3. Investigating the role of combined acoustic-visual feedback in one-dimensional synchronous brain computer interfaces, a preliminary study

    Directory of Open Access Journals (Sweden)

    Gargiulo GD

    2012-09-01

    Full Text Available Gaetano D Gargiulo,1–3 Armin Mohamed,1 Alistair L McEwan,1 Paolo Bifulco,2 Mario Cesarelli,2 Craig T Jin,1 Mariano Ruffo,2 Jonathan Tapson,3 André van Schaik31School of Electrical and Information Engineering, The University of Sydney, New South Wales, Australia; 2Dipartimento di Ingegneria Elettronica e delle Telecomunicazioni "Federico II" University of Naples, Naples, Italy; 3BENS Laboratory, MARCS Institute, The University of Western Sydney, New South Wales, AustraliaAbstract: Feedback plays an important role when learning to use a brain computer interface (BCI, particularly in the case of synchronous feedback that relies on the interaction subject. In this preliminary study, we investigate the role of combined auditory-visual feedback during synchronous µ rhythm-based BCI sessions to help the subject to remain focused on the selected imaginary task. This new combined feedback, now integrated within the general purpose BCI2000 software, has been tested on eight untrained and three trained subjects during a monodimensional left-right control task. In order to reduce the setup burden and maximize subject comfort, an electroencephalographic device suitable for dry electrodes that required no skin preparation was used. Quality and index of improvement was evaluated based on a personal self-assessment questionnaire from each subject and quantitative data based on subject performance. Results for this preliminary study show that the combined feedback was well tolerated by the subjects and improved performance in 75% of the naïve subjects compared with visual feedback alone.Keywords: brain computer interface, dry electrodes, subject feedback

  4. 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.

  5. 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

  6. 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.

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

    Science.gov (United States)

    Naseer, Noman; Hong, Keum-Shik

    2015-01-01

    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 (ICA), 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 (SVM), hidden Markov model (HMM), 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. PMID:25674060

  8. Retrieving clinically relevant diabetic retinopathy images using a multi-class multiple-instance framework

    Science.gov (United States)

    Chandakkar, Parag S.; Venkatesan, Ragav; Li, Baoxin

    2013-02-01

    Diabetic retinopathy (DR) is a vision-threatening complication from diabetes mellitus, a medical condition that is rising globally. Unfortunately, many patients are unaware of this complication because of absence of symptoms. Regular screening of DR is necessary to detect the condition for timely treatment. Content-based image retrieval, using archived and diagnosed fundus (retinal) camera DR images can improve screening efficiency of DR. This content-based image retrieval study focuses on two DR clinical findings, microaneurysm and neovascularization, which are clinical signs of non-proliferative and proliferative diabetic retinopathy. The authors propose a multi-class multiple-instance image retrieval framework which deploys a modified color correlogram and statistics of steerable Gaussian Filter responses, for retrieving clinically relevant images from a database of DR fundus image database.

  9. Automatic SLEEP staging: From young aduslts to elderly patients using multi-class support vector machine

    DEFF Research Database (Denmark)

    Kempfner, Jacob; Jennum, Poul; Sorensen, Helge B. D.;

    2013-01-01

    an automatic sleep stage detector, which can separate wakefulness, rapid-eye-movement (REM) sleep and non-REM (NREM) sleep using only EEG and EOG. Most sleep events, which define the sleep stages, are reduced with age. This is addressed by focusing on the amplitude of the clinical EEG bands......Aging is a process that is inevitable, and makes our body vulnerable to age-related diseases. Age is the most consistent factor affecting the sleep structure. Therefore, new automatic sleep staging methods, to be used in both of young and elderly patients, are needed. This study proposes......, and not the affected sleep events. The age-related influences are then reduced by robust subject-specific scaling. The classification of the three sleep stages are achieved by a multi-class support vector machine using the one-versus-rest scheme. It was possible to obtain a high classification accuracy of 0...

  10. Electroencephalography (EEG)-based brain-computer interface (BCI): a 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control.

    Science.gov (United States)

    Huang, Dandan; Qian, Kai; Fei, Ding-Yu; Jia, Wenchuan; Chen, Xuedong; Bai, Ou

    2012-05-01

    This study aims to propose an effective and practical paradigm for a brain-computer interface (BCI)-based 2-D virtual wheelchair control. The paradigm was based on the multi-class discrimination of spatiotemporally distinguishable phenomenon of event-related desynchronization/synchronization (ERD/ERS) in electroencephalogram signals associated with motor execution/imagery of right/left hand movement. Comparing with traditional method using ERD only, where bilateral ERDs appear during left/right hand mental tasks, the 2-D control exhibited high accuracy within a short time, as incorporating ERS into the paradigm hypothetically enhanced the spatiotemoral feature contrast of ERS versus ERD. We also expected users to experience ease of control by including a noncontrol state. In this study, the control command was sent discretely whereas the virtual wheelchair was moving continuously. We tested five healthy subjects in a single visit with two sessions, i.e., motor execution and motor imagery. Each session included a 20 min calibration and two sets of games that were less than 30 min. Average target hit rate was as high as 98.4% with motor imagery. Every subject achieved 100% hit rate in the second set of wheelchair control games. The average time to hit a target 10 m away was about 59 s, with 39 s for the best set. The superior control performance in subjects without intensive BCI training suggested a practical wheelchair control paradigm for BCI users. PMID:22498703

  11. 多类核共空间模式特征提取方法研究%Research on the Methods for Multi-class Kernel CSP-based Feature Extraction

    Institute of Scientific and Technical Information of China (English)

    王金甲; 张玲智; 胡备

    2012-01-01

    To relax the presumption of strictly linear patterns in the common spatial patterns CCSP)" we studied the kernel CSP (KCSP). A new multi-class KCSP (MKCSP) approach was proposed in this paper, which combines the kernel approach with multi-class CSP technique. In this approach, we used kernel spatial patterns for each class a-gainst all others, and extracted signal components specific to one condition from EEG data sets of multiple conditions. Then we performed classification using the Logistic linear classifier. Brain computer interface (BCD competition Ⅲ_3a was used in the experiment Through the experiment, it can be proved that this approach could decompose the raw EEG singles into spatial patterns extracted from multi-class of single trial EEG, and could obtain good classification results.%为了缓解共空间模式(CSP)下,对脑内的源信号和记录的脑电(EEG)信号之间严格的线性模式的假设关系,需要研究一种核共空间模式(KCSP)的特征提取方法.考虑到脑-机接口(BCI)研究已经逐渐从两类的模式识别发展为多类的模式识别,因而提出了多类核共空间模式(MKCSP)的方法,该方法将KCSP方法和多类CSP方法结合起来.我们用Logistic线性分类器对提取的特征进行了分类.实验使用的数据是2005年BCI竞赛Ⅲ的数据集Ⅲ_3a.通过实验表明,本文中的方法能够从多类别的单次试验的EEG数据中提取相应的特征,并得到了较好分类结果.

  12. Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring.

    Science.gov (United States)

    Ghose, Soumya; Mitra, Jhimli; Karunanithi, Mohan; Dowling, Jason

    2015-01-01

    Home monitoring of chronically ill or elderly patient can reduce frequent hospitalisations and hence provide improved quality of care at a reduced cost to the community, therefore reducing the burden on the healthcare system. Activity recognition of such patients is of high importance in such a design. In this work, a system for automatic human physical activity recognition from smart-phone inertial sensors data is proposed. An ensemble of decision trees framework is adopted to train and predict the multi-class human activity system. A comparison of our proposed method with a multi-class traditional support vector machine shows significant improvement in activity recognition accuracies.

  13. Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future

    OpenAIRE

    Huggins, Jane E.; Guger, Christoph; Allison, Brendan; Anderson, Charles W.; Batista, Aaron; Brouwer, Anne-Marie; Brunner, Clemens; Chavarriaga, Ricardo; Fried-Oken, Melanie; Gunduz, Aysegul; Gupta, Disha; Kübler, Andrea; Leeb, Robert; Lotte, Fabien; Lee E Miller

    2014-01-01

    International audience; The Fifth International Brain-Computer Interface (BCI) Meeting met on 3-7 June 2013 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 19 workshops covering topics in brain-computer interface and brain-machine interface research. Topics included translation of BCIs into clinical use, standardization and certification, types of brain activity to use for BCI, recording methods, the effects of plasticity, special interest topics in...

  14. Improving players' control over the NeuroSky brain-computer interface

    OpenAIRE

    Kristín Guðmundsdóttir 1963

    2011-01-01

    Abstract In mid-2009, NeuroSky released the first consumer brain-computer interface (BCI). MindGames, since that time, has been developing games which players control with their powers of concentration and relaxation via consumer brain-computer interfaces. At present, all users of these novel interfaces are inexperienced, and have trouble controlling them. Therefore MindGames would like to develop a method for helping as people to learn as quickly as possible to activate the "relaxation" a...

  15. An Efficient ERP-Based Brain-Computer Interface Using Random Set Presentation and Face Familiarity

    OpenAIRE

    Seul-Ki Yeom; Siamac Fazli; Klaus-Robert Müller; Seong-Whan Lee

    2014-01-01

    Event-related potential (ERP)-based P300 spellers are commonly used in the field of brain-computer interfaces as an alternative channel of communication for people with severe neuro-muscular diseases. This study introduces a novel P300 based brain-computer interface (BCI) stimulus paradigm using a random set presentation pattern and exploiting the effects of face familiarity. The effect of face familiarity is widely studied in the cognitive neurosciences and has recently been addressed for th...

  16. Investigating the role of combined acoustic-visual feedback in one-dimensional synchronous brain computer interfaces, a preliminary study

    Science.gov (United States)

    Gargiulo, Gaetano D; Mohamed, Armin; McEwan, Alistair L; Bifulco, Paolo; Cesarelli, Mario; Jin, Craig T; Ruffo, Mariano; Tapson, Jonathan; van Schaik, André

    2012-01-01

    Feedback plays an important role when learning to use a brain computer interface (BCI), particularly in the case of synchronous feedback that relies on the interaction subject. In this preliminary study, we investigate the role of combined auditory-visual feedback during synchronous μ rhythm-based BCI sessions to help the subject to remain focused on the selected imaginary task. This new combined feedback, now integrated within the general purpose BCI2000 software, has been tested on eight untrained and three trained subjects during a monodimensional left-right control task. In order to reduce the setup burden and maximize subject comfort, an electroencephalographic device suitable for dry electrodes that required no skin preparation was used. Quality and index of improvement was evaluated based on a personal self-assessment questionnaire from each subject and quantitative data based on subject performance. Results for this preliminary study show that the combined feedback was well tolerated by the subjects and improved performance in 75% of the naïve subjects compared with visual feedback alone. PMID:23152713

  17. Pre-frontal control of closed-loop limbic neurostimulation by rodents using a brain-computer interface

    Science.gov (United States)

    Widge, Alik S.; Moritz, Chet T.

    2014-04-01

    Objective. There is great interest in closed-loop neurostimulators that sense and respond to a patient's brain state. Such systems may have value for neurological and psychiatric illnesses where symptoms have high intraday variability. Animal models of closed-loop stimulators would aid preclinical testing. We therefore sought to demonstrate that rodents can directly control a closed-loop limbic neurostimulator via a brain-computer interface (BCI). Approach. We trained rats to use an auditory BCI controlled by single units in prefrontal cortex (PFC). The BCI controlled electrical stimulation in the medial forebrain bundle, a limbic structure involved in reward-seeking. Rigorous offline analyses were performed to confirm volitional control of the neurostimulator. Main results. All animals successfully learned to use the BCI and neurostimulator, with closed-loop control of this challenging task demonstrated at 80% of PFC recording locations. Analysis across sessions and animals confirmed statistically robust BCI control and specific, rapid modulation of PFC activity. Significance. Our results provide a preliminary demonstration of a method for emotion-regulating closed-loop neurostimulation. They further suggest that activity in PFC can be used to control a BCI without pre-training on a predicate task. This offers the potential for BCI-based treatments in refractory neurological and mental illness.

  18. Canonical feature selection for joint regression and multi-class identification in Alzheimer's disease diagnosis.

    Science.gov (United States)

    Zhu, Xiaofeng; Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

    2016-09-01

    Fusing information from different imaging modalities is crucial for more accurate identification of the brain state because imaging data of different modalities can provide complementary perspectives on the complex nature of brain disorders. However, most existing fusion methods often extract features independently from each modality, and then simply concatenate them into a long vector for classification, without appropriate consideration of the correlation among modalities. In this paper, we propose a novel method to transform the original features from different modalities to a common space, where the transformed features become comparable and easy to find their relation, by canonical correlation analysis. We then perform the sparse multi-task learning for discriminative feature selection by using the canonical features as regressors and penalizing a loss function with a canonical regularizer. In our experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we use Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images to jointly predict clinical scores of Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) and also identify multi-class disease status for Alzheimer's disease diagnosis. The experimental results showed that the proposed canonical feature selection method helped enhance the performance of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods. PMID:26254746

  19. Incremental multi-class semi-supervised clustering regularized by Kalman filtering.

    Science.gov (United States)

    Mehrkanoon, Siamak; Agudelo, Oscar Mauricio; Suykens, Johan A K

    2015-11-01

    This paper introduces an on-line semi-supervised learning algorithm formulated as a regularized kernel spectral clustering (KSC) approach. We consider the case where new data arrive sequentially but only a small fraction of it is labeled. The available labeled data act as prototypes and help to improve the performance of the algorithm to estimate the labels of the unlabeled data points. We adopt a recently proposed multi-class semi-supervised KSC based algorithm (MSS-KSC) and make it applicable for on-line data clustering. Given a few user-labeled data points the initial model is learned and then the class membership of the remaining data points in the current and subsequent time instants are estimated and propagated in an on-line fashion. The update of the memberships is carried out mainly using the out-of-sample extension property of the model. Initially the algorithm is tested on computer-generated data sets, then we show that video segmentation can be cast as a semi-supervised learning problem. Furthermore we show how the tracking capabilities of the Kalman filter can be used to provide the labels of objects in motion and thus regularizing the solution obtained by the MSS-KSC algorithm. In the experiments, we demonstrate the performance of the proposed method on synthetic data sets and real-life videos where the clusters evolve in a smooth fashion over time.

  20. Incremental multi-class semi-supervised clustering regularized by Kalman filtering.

    Science.gov (United States)

    Mehrkanoon, Siamak; Agudelo, Oscar Mauricio; Suykens, Johan A K

    2015-11-01

    This paper introduces an on-line semi-supervised learning algorithm formulated as a regularized kernel spectral clustering (KSC) approach. We consider the case where new data arrive sequentially but only a small fraction of it is labeled. The available labeled data act as prototypes and help to improve the performance of the algorithm to estimate the labels of the unlabeled data points. We adopt a recently proposed multi-class semi-supervised KSC based algorithm (MSS-KSC) and make it applicable for on-line data clustering. Given a few user-labeled data points the initial model is learned and then the class membership of the remaining data points in the current and subsequent time instants are estimated and propagated in an on-line fashion. The update of the memberships is carried out mainly using the out-of-sample extension property of the model. Initially the algorithm is tested on computer-generated data sets, then we show that video segmentation can be cast as a semi-supervised learning problem. Furthermore we show how the tracking capabilities of the Kalman filter can be used to provide the labels of objects in motion and thus regularizing the solution obtained by the MSS-KSC algorithm. In the experiments, we demonstrate the performance of the proposed method on synthetic data sets and real-life videos where the clusters evolve in a smooth fashion over time. PMID:26319050

  1. Brain Computer Interaction of Indian Facial Expressions Recognition Through Digital Electroencephalography

    Directory of Open Access Journals (Sweden)

    Dinesh Chandra Jain

    2011-02-01

    Full Text Available The brain computer interaction could be the interface medium of the future, instead of using peripheral input output devices .So The brain computer interaction is a path way in which through digital EEG technique the brain signals of human subject have been recorded under different poses by using Digital Electroencephalograph (EEG 2400NP instrument. Under experimental setup The subjects have given different expressions corresponding brain signals that have been recorded through a popular technique Digital EEG. An attempt has been done to correlate these results to the facial action coding System (FACS.

  2. Reducing Dataset Size in Frequency Domain for Brain Computer Interface Motor Imagery Classification

    Directory of Open Access Journals (Sweden)

    Ch.Aparna

    2010-12-01

    Full Text Available Brain computer interface is an emerging area of research where the BCI system is able to detect and interpret the mental activity into computer interpretable signals opening a wide area of applications where activities can be completed without using muscular movement. In Brain Computer Interface research, for classification of EEG signals the raw signals captured has to undergo some preprocessing, to obtain the right attributes for classification. In this paper, we present a system which allows for classification of mental tasks based on a statistical data obtained in frequency domain using Discrete cosine transform and extracting useful frequencies from the same with application of decision tree algorithms for classification.

  3. Multi-Class Simultaneous Adaptive Segmentation and Quality Control of Point Cloud Data

    Directory of Open Access Journals (Sweden)

    Ayman Habib

    2016-01-01

    Full Text Available 3D modeling of a given site is an important activity for a wide range of applications including urban planning, as-built mapping of industrial sites, heritage documentation, military simulation, and outdoor/indoor analysis of airflow. Point clouds, which could be either derived from passive or active imaging systems, are an important source for 3D modeling. Such point clouds need to undergo a sequence of data processing steps to derive the necessary information for the 3D modeling process. Segmentation is usually the first step in the data processing chain. This paper presents a region-growing multi-class simultaneous segmentation procedure, where planar, pole-like, and rough regions are identified while considering the internal characteristics (i.e., local point density/spacing and noise level of the point cloud in question. The segmentation starts with point cloud organization into a kd-tree data structure and characterization process to estimate the local point density/spacing. Then, proceeding from randomly-distributed seed points, a set of seed regions is derived through distance-based region growing, which is followed by modeling of such seed regions into planar and pole-like features. Starting from optimally-selected seed regions, planar and pole-like features are then segmented. The paper also introduces a list of hypothesized artifacts/problems that might take place during the region-growing process. Finally, a quality control process is devised to detect, quantify, and mitigate instances of partially/fully misclassified planar and pole-like features. Experimental results from airborne and terrestrial laser scanning as well as image-based point clouds are presented to illustrate the performance of the proposed segmentation and quality control framework.

  4. Enhanced CellClassifier: a multi-class classification tool for microscopy images

    Directory of Open Access Journals (Sweden)

    Horvath Peter

    2010-01-01

    Full Text Available Abstract Background Light microscopy is of central importance in cell biology. The recent introduction of automated high content screening has expanded this technology towards automation of experiments and performing large scale perturbation assays. Nevertheless, evaluation of microscopy data continues to be a bottleneck in many projects. Currently, among open source software, CellProfiler and its extension Analyst are widely used in automated image processing. Even though revolutionizing image analysis in current biology, some routine and many advanced tasks are either not supported or require programming skills of the researcher. This represents a significant obstacle in many biology laboratories. Results We have developed a tool, Enhanced CellClassifier, which circumvents this obstacle. Enhanced CellClassifier starts from images analyzed by CellProfiler, and allows multi-class classification using a Support Vector Machine algorithm. Training of objects can be done by clicking directly "on the microscopy image" in several intuitive training modes. Many routine tasks like out-of focus exclusion and well summary are also supported. Classification results can be integrated with other object measurements including inter-object relationships. This makes a detailed interpretation of the image possible, allowing the differentiation of many complex phenotypes. For the generation of the output, image, well and plate data are dynamically extracted and summarized. The output can be generated as graphs, Excel-files, images with projections of the final analysis and exported as variables. Conclusion Here we describe Enhanced CellClassifier which allows multiple class classification, elucidating complex phenotypes. Our tool is designed for the biologist who wants both, simple and flexible analysis of images without requiring programming skills. This should facilitate the implementation of automated high-content screening.

  5. Multi-TGDR: a regularization method for multi-class classification in microarray experiments.

    Directory of Open Access Journals (Sweden)

    Suyan Tian

    Full Text Available BACKGROUND: As microarray technology has become mature and popular, the selection and use of a small number of relevant genes for accurate classification of samples has arisen as a hot topic in the circles of biostatistics and bioinformatics. However, most of the developed algorithms lack the ability to handle multiple classes, arguably a common application. Here, we propose an extension to an existing regularization algorithm, called Threshold Gradient Descent Regularization (TGDR, to specifically tackle multi-class classification of microarray data. When there are several microarray experiments addressing the same/similar objectives, one option is to use a meta-analysis version of TGDR (Meta-TGDR, which considers the classification task as a combination of classifiers with the same structure/model while allowing the parameters to vary across studies. However, the original Meta-TGDR extension did not offer a solution to the prediction on independent samples. Here, we propose an explicit method to estimate the overall coefficients of the biomarkers selected by Meta-TGDR. This extension permits broader applicability and allows a comparison between the predictive performance of Meta-TGDR and TGDR using an independent testing set. RESULTS: Using real-world applications, we demonstrated the proposed multi-TGDR framework works well and the number of selected genes is less than the sum of all individualized binary TGDRs. Additionally, Meta-TGDR and TGDR on the batch-effect adjusted pooled data approximately provided same results. By adding Bagging procedure in each application, the stability and good predictive performance are warranted. CONCLUSIONS: Compared with Meta-TGDR, TGDR is less computing time intensive, and requires no samples of all classes in each study. On the adjusted data, it has approximate same predictive performance with Meta-TGDR. Thus, it is highly recommended.

  6. LMD method and multi-class RWSVM of fault diagnosis for rotating machinery using condition monitoring information.

    Science.gov (United States)

    Liu, Zhiwen; Chen, Xuefeng; He, Zhengjia; Shen, Zhongjie

    2013-07-05

    Timely and accurate condition monitoring and fault diagnosis of rotating machinery are very important to maintain a high degree of availability, reliability and operational safety. This paper presents a novel intelligent method based on local mean decomposition (LMD) and multi-class reproducing wavelet support vector machines (RWSVM), which is applied to diagnose rotating machinery faults. First, the sensor-based vibration signals measured from the rotating machinery are preprocessed by the LMD method and product functions (PFs) are produced. Second, statistic features are extracted to acquire more fault characteristic information from the sensitive PF. Finally, these features are fed into a multi-class RWSVM to identify the rotating machinery health conditions. The experimental results validate the effectiveness of the proposed RWSVM method in identifying rotating machinery fault patterns accurately and effectively and its superiority over that based on the general SVM.

  7. EDITORIAL: Special section on gaze-independent brain-computer interfaces Special section on gaze-independent brain-computer interfaces

    Science.gov (United States)

    Treder, Matthias S.

    2012-08-01

    Restoring the ability to communicate and interact with the environment in patients with severe motor disabilities is a vision that has been the main catalyst of early brain-computer interface (BCI) research. The past decade has brought a diversification of the field. BCIs have been examined as a tool for motor rehabilitation and their benefit in non-medical applications such as mental-state monitoring for improved human-computer interaction and gaming has been confirmed. At the same time, the weaknesses of some approaches have been pointed out. One of these weaknesses is gaze-dependence, that is, the requirement that the user of a BCI system voluntarily directs his or her eye gaze towards a visual target in order to efficiently operate a BCI. This not only contradicts the main doctrine of BCI research, namely that BCIs should be independent of muscle activity, but it can also limit its real-world applicability both in clinical and non-medical settings. It is only in a scenario devoid of any motor activity that a BCI solution is without alternative. Gaze-dependencies have surfaced at two different points in the BCI loop. Firstly, a BCI that relies on visual stimulation may require users to fixate on the target location. Secondly, feedback is often presented visually, which implies that the user may have to move his or her eyes in order to perceive the feedback. This special section was borne out of a BCI workshop on gaze-independent BCIs held at the 2011 Society for Applied Neurosciences (SAN) Conference and has then been extended with additional contributions from other research groups. It compiles experimental and methodological work that aims toward gaze-independent communication and mental-state monitoring. Riccio et al review the current state-of-the-art in research on gaze-independent BCIs [1]. Van der Waal et al present a tactile speller that builds on the stimulation of the fingers of the right and left hand [2]. H¨ohne et al analyze the ergonomic aspects

  8. 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...

  9. Multi-modal affect induction for affective brain-computer interfaces

    NARCIS (Netherlands)

    Mühl, C.; Broek, E.L. van den; Brouwer, A.M.; Nijboer, F.; Wouwe, N.C. van; Heylen, D.

    2011-01-01

    Reliable applications of affective brain-computer interfaces (aBCI) in realistic, multi-modal environments require a detailed understanding of the processes involved in emotions. To explore the modalityspecific nature of affective responses, we studied neurophysiological responses (i.e., EEG) of 24

  10. Design requirements and potential target users for brain-computer interfaces – recommendations from rehabilitation professionals

    NARCIS (Netherlands)

    Nijboer, F.; Plass-Oude Bos, D.; Blokland, Y.M.; Wijk, R. van; Farquhar, J.D.R.

    2014-01-01

    It is an implicit assumption in the field of brain-computer interfacing (BCI) that BCIs can be satisfactorily used to access augmentative and alternative communication (AAC) methods by people with severe physical disabilities. A one-day workshop and focus group interview was held to investigate this

  11. Ownership and Agency of an Independent Supernumerary Hand Induced by an Imitation Brain-Computer Interface.

    Directory of Open Access Journals (Sweden)

    Luke Bashford

    Full Text Available To study body ownership and control, illusions that elicit these feelings in non-body objects are widely used. Classically introduced with the Rubber Hand Illusion, these illusions have been replicated more recently in virtual reality and by using brain-computer interfaces. Traditionally these illusions investigate the replacement of a body part by an artificial counterpart, however as brain-computer interface research develops it offers us the possibility to explore the case where non-body objects are controlled in addition to movements of our own limbs. Therefore we propose a new illusion designed to test the feeling of ownership and control of an independent supernumerary hand. Subjects are under the impression they control a virtual reality hand via a brain-computer interface, but in reality there is no causal connection between brain activity and virtual hand movement but correct movements are observed with 80% probability. These imitation brain-computer interface trials are interspersed with movements in both the subjects' real hands, which are in view throughout the experiment. We show that subjects develop strong feelings of ownership and control over the third hand, despite only receiving visual feedback with no causal link to the actual brain signals. Our illusion is crucially different from previously reported studies as we demonstrate independent ownership and control of the third hand without loss of ownership in the real hands.

  12. Value of Whole Brain Computed Tomography Perfusion for Predicting Outcome after TIA or Minor Ischemic Stroke

    NARCIS (Netherlands)

    Van Den Wijngaard, Ido R.; Algra, Ale; Lycklama À Nijeholt, Geert J.; Boiten, Jelis; Wermer, Marieke J H; Van Walderveen, Marianne A A

    2015-01-01

    Introduction About 15% of patients with transient ischemic attack (TIA) or minor ischemic stroke have functional impairment after 3 months. We studied the role of whole brain computed tomography perfusion (WB-CTP) in the emergency diagnosis of TIA or minor stroke in predicting disability at 3 months

  13. Hacking the brain: Brain-computer interfacing technology and the ethics of neurosecurity

    NARCIS (Netherlands)

    Ienca, M.; Haselager, W.F.G.

    2016-01-01

    Brain-computer interfacing technologies are used as assistive technologies for patients as well as healthy subjects to control devices solely by brain activity. Yet the risks associated with the misuse of these technologies remain largely unexplored. Recent findings have shown that BCIs are potentia

  14. Using brain-computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience

    NARCIS (Netherlands)

    Jensen, O.; Bahramisharif, A.; Oostenveld, R.; Klanke, S.; Hadjipapas, A.; Okazaki, Y.O.; Gerven, M.A.J. van

    2011-01-01

    Large efforts are currently being made to develop and improve online analysis of brain activity which can be used, e.g., for brain-computer interfacing (BCI). A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for ai

  15. Toward affective brain-computer interfaces : exploring the neurophysiology of affect during human media interaction

    NARCIS (Netherlands)

    Mühl, Christian

    2012-01-01

    Affective Brain-Computer Interfaces (aBCI), the sensing of emotions from brain activity, seems a fantasy from the realm of science fiction. But unlike faster-than-light travel or teleportation, aBCI seems almost within reach due to novel sensor technologies, the advancement of neuroscience, and the

  16. Brain-computer interfacing using modulations of alpha activity induced by covert shifts of attention

    NARCIS (Netherlands)

    Treder, M.S.; Bahramisharif, A.; Schmidt, N.M.; Gerven, M.A.J. van; Blankertz, B.

    2011-01-01

    Background Visual brain-computer interfaces (BCIs) often yield high performance only when targets are fixated with the eyes. Furthermore, many paradigms use intense visual stimulation, which can be irritating especially in long BCI sessions. However, BCIs can more directly directly tap the neural pr

  17. Beyond maximum speed—a novel two-stimulus paradigm for brain-computer interfaces based on event-related potentials (P300-BCI)

    Science.gov (United States)

    Kaufmann, Tobias; Kübler, Andrea

    2014-10-01

    Objective. The speed of brain-computer interfaces (BCI), based on event-related potentials (ERP), is inherently limited by the commonly used one-stimulus paradigm. In this paper, we introduce a novel paradigm that can increase the spelling speed by a factor of 2, thereby extending the one-stimulus paradigm to a two-stimulus paradigm. Two different stimuli (a face and a symbol) are presented at the same time, superimposed on different characters and ERPs are classified using a multi-class classifier. Here, we present the proof-of-principle that is achieved with healthy participants. Approach. Eight participants were confronted with the novel two-stimulus paradigm and, for comparison, with two one-stimulus paradigms that used either one of the stimuli. Classification accuracies (percentage of correctly predicted letters) and elicited ERPs from the three paradigms were compared in a comprehensive offline analysis. Main results. The accuracies slightly decreased with the novel system compared to the established one-stimulus face paradigm. However, the use of two stimuli allowed for spelling at twice the maximum speed of the one-stimulus paradigms, and participants still achieved an average accuracy of 81.25%. This study introduced an alternative way of increasing the spelling speed in ERP-BCIs and illustrated that ERP-BCIs may not yet have reached their speed limit. Future research is needed in order to improve the reliability of the novel approach, as some participants displayed reduced accuracies. Furthermore, a comparison to the most recent BCI systems with individually adjusted, rapid stimulus timing is needed to draw conclusions about the practical relevance of the proposed paradigm. Significance. We introduced a novel two-stimulus paradigm that might be of high value for users who have reached the speed limit with the current one-stimulus ERP-BCI systems.

  18. Python Executable Script for Estimating Two Effective Parameters to Individualize Brain-Computer Interfaces: Individual Alpha Frequency and Neurophysiological Predictor.

    Science.gov (United States)

    Alonso-Valerdi, Luz María

    2016-01-01

    A brain-computer interface (BCI) aims to establish communication between the human brain and a computing system so as to enable the interaction between an individual and his environment without using the brain output pathways. Individuals control a BCI system by modulating their brain signals through mental tasks (e.g., motor imagery or mental calculation) or sensory stimulation (e.g., auditory, visual, or tactile). As users modulate their brain signals at different frequencies and at different levels, the appropriate characterization of those signals is necessary. The modulation of brain signals through mental tasks is furthermore a skill that requires training. Unfortunately, not all the users acquire such skill. A practical solution to this problem is to assess the user probability of controlling a BCI system. Another possible solution is to set the bandwidth of the brain oscillations, which is highly sensitive to the users' age, sex and anatomy. With this in mind, NeuroIndex, a Python executable script, estimates a neurophysiological prediction index and the individual alpha frequency (IAF) of the user in question. These two parameters are useful to characterize the user EEG signals, and decide how to go through the complex process of adapting the human brain and the computing system on the basis of previously proposed methods. NeuroIndeX is not only the implementation of those methods, but it also complements the methods each other and provides an alternative way to obtain the prediction parameter. However, an important limitation of this application is its dependency on the IAF value, and some results should be interpreted with caution. The script along with some electroencephalographic datasets are available on a GitHub repository in order to corroborate the functionality and usability of this application. PMID:27445783

  19. Neuroengineering tools/applications for bidirectional interfaces, brain computer interfaces, and neuroprosthetic implants - a review of recent progress

    Directory of Open Access Journals (Sweden)

    Ryan M Rothschild

    2010-10-01

    Full Text Available The main focus of this review is to provide a holistic amalgamated overview of the most recent human in vivo techniques for implementing brain-computer interfaces (BCIs, bidirectional interfaces and neuroprosthetics. Neuroengineering is providing new methods for tackling current difficulties; however neuroprosthetics have been studied for decades. Recent progresses are permitting the design of better systems with higher accuracies, repeatability and system robustness. Bidirectional interfaces integrate recording and the relaying of information from and to the brain for the development of BCIs. The concepts of non-invasive and invasive recording of brain activity are introduced. This includes classical and innovative techniques like electroencephalography (EEG and near-infrared spectroscopy (NIRS. Then the problem of gliosis and solutions for (semi- permanent implant biocompatibility such as innovative implant coatings, materials and shapes are discussed. Implant power and the transmission of their data through implanted pulse generators (IPGs and wireless telemetry are taken into account. How sensation can be relayed back to the brain to increase integration of the neuroengineered systems with the body by methods such as micro-stimulation and transcranial magnetic stimulation (TMS are then addressed. The neuroprosthetic section discusses some of the various types and how they operate. Visual prosthetics are discussed and the three types, dependant on implant location, are examined. Auditory prosthetics, being cochlear or cortical, are then addressed. Replacement hand and limb prosthetics are then considered. These are followed by sections concentrating on the control of wheelchairs, computers and robotics directly from brain activity as recorded by non-invasive and invasive techniques.

  20. Role of the auditory system in speech production.

    Science.gov (United States)

    Guenther, Frank H; Hickok, Gregory

    2015-01-01

    This chapter reviews evidence regarding the role of auditory perception in shaping speech output. Evidence indicates that speech movements are planned to follow auditory trajectories. This in turn is followed by a description of the Directions Into Velocities of Articulators (DIVA) model, which provides a detailed account of the role of auditory feedback in speech motor development and control. A brief description of the higher-order brain areas involved in speech sequencing (including the pre-supplementary motor area and inferior frontal sulcus) is then provided, followed by a description of the Hierarchical State Feedback Control (HSFC) model, which posits internal error detection and correction processes that can detect and correct speech production errors prior to articulation. The chapter closes with a treatment of promising future directions of research into auditory-motor interactions in speech, including the use of intracranial recording techniques such as electrocorticography in humans, the investigation of the potential roles of various large-scale brain rhythms in speech perception and production, and the development of brain-computer interfaces that use auditory feedback to allow profoundly paralyzed users to learn to produce speech using a speech synthesizer.

  1. Effects of Background Music on Objective and Subjective Performance Measures in an Auditory BCI

    Science.gov (United States)

    Zhou, Sijie; Allison, Brendan Z.; Kübler, Andrea; Cichocki, Andrzej; Wang, Xingyu; Jin, Jing

    2016-01-01

    Several studies have explored brain computer interface (BCI) systems based on auditory stimuli, which could help patients with visual impairments. Usability and user satisfaction are important considerations in any BCI. Although background music can influence emotion and performance in other task environments, and many users may wish to listen to music while using a BCI, auditory, and other BCIs are typically studied without background music. Some work has explored the possibility of using polyphonic music in auditory BCI systems. However, this approach requires users with good musical skills, and has not been explored in online experiments. Our hypothesis was that an auditory BCI with background music would be preferred by subjects over a similar BCI without background music, without any difference in BCI performance. We introduce a simple paradigm (which does not require musical skill) using percussion instrument sound stimuli and background music, and evaluated it in both offline and online experiments. The result showed that subjects preferred the auditory BCI with background music. Different performance measures did not reveal any significant performance effect when comparing background music vs. no background. Since the addition of background music does not impair BCI performance but is preferred by users, auditory (and perhaps other) BCIs should consider including it. Our study also indicates that auditory BCIs can be effective even if the auditory channel is simultaneously otherwise engaged. PMID:27790111

  2. Single-trial EEG classification using in-phase average for brain-computer interface

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Communication signals should be estimated by a single trial in a brain-computer interface.Since the relativity of visual evoked potentials from different sites should be stronger than those of the spontaneous electro encephalogram(EEG),this paper adopted the time-lock averaged signals from multi-channels as features.200 trials of EEG recordings evoked by target or non-target stimuli were classified by the support vector machine(SVM).Results show that a classification accuracy of higher than 97% can be obtained by merely using the 250-550 ms time section of the averaged signals with channel Cz and Pz as features.It suggests that a possible approach to boost communication speed and simplify the designation of the brain-computer interface(BCI)system is worthy of an attempt in this way.

  3. Robotics, Stem Cells and Brain Computer Interfaces in Rehabilitation and Recovery from Stroke; Updates and Advances

    Science.gov (United States)

    Boninger, Michael L; Wechsler, Lawrence R.; Stein, Joel

    2014-01-01

    Objective To describe the current state and latest advances in robotics, stem cells, and brain computer interfaces in rehabilitation and recovery for stroke. Design The authors of this summary recently reviewed this work as part of a national presentation. The paper represents the information included in each area. Results Each area has seen great advances and challenges as products move to market and experiments are ongoing. Conclusion Robotics, stem cells, and brain computer interfaces all have tremendous potential to reduce disability and lead to better outcomes for patients with stroke. Continued research and investment will be needed as the field moves forward. With this investment, the potential for recovery of function is likely substantial PMID:25313662

  4. Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future

    Science.gov (United States)

    Huggins, Jane E.; Guger, Christoph; Allison, Brendan; Anderson, Charles W.; Batista, Aaron; Brouwer, Anne-Marie (A.-M.); Brunner, Clemens; Chavarriaga, Ricardo; Fried-Oken, Melanie; Gunduz, Aysegul; Gupta, Disha; Kübler, Andrea; Leeb, Robert; Lotte, Fabien; Miller, Lee E.; Müller-Putz, Gernot; Rutkowski, Tomasz; Tangermann, Michael; Thompson, David Edward

    2014-01-01

    The Fifth International Brain-Computer Interface (BCI) Meeting met June 3–7th, 2013 at the Asilomar Conference Grounds, Pacific Grove, California. The conference included 19 workshops covering topics in brain-computer interface and brain-machine interface research. Topics included translation of BCIs into clinical use, standardization and certification, types of brain activity to use for BCI, recording methods, the effects of plasticity, special interest topics in BCIs applications, and future BCI directions. BCI research is well established and transitioning to practical use to benefit people with physical impairments. At the same time, new applications are being explored, both for people with physical impairments and beyond. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and high-lighting important issues for future research and development. PMID:25485284

  5. Improved Targeting Through Collaborative Decision-Making and Brain Computer Interfaces

    Science.gov (United States)

    Stoica, Adrian; Barrero, David F.; McDonald-Maier, Klaus

    2013-01-01

    This paper reports a first step toward a brain-computer interface (BCI) for collaborative targeting. Specifically, we explore, from a broad perspective, how the collaboration of a group of people can increase the performance on a simple target identification task. To this end, we requested a group of people to identify the location and color of a sequence of targets appearing on the screen and measured the time and accuracy of the response. The individual results are compared to a collective identification result determined by simple majority voting, with random choice in case of drawn. The results are promising, as the identification becomes significantly more reliable even with this simple voting and a small number of people (either odd or even number) involved in the decision. In addition, the paper briefly analyzes the role of brain-computer interfaces in collaborative targeting, extending the targeting task by using a BCI instead of a mechanical response.

  6. 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.

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

    OpenAIRE

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

    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/ER...

  8. Combining Object Detection And Brain Computer Interfacing: Towards A New Way Of Subject-Environment Interaction

    OpenAIRE

    Robben, Arne; Chumerin, Nikolay; Manyakov, Nikolay V.; Combaz, Adrien; van Vliet, Marijn; Hulle, Marc van

    2011-01-01

    In this paper we propose an application which combines two research disciplines: object detection and brain-computer interfacing. It is in particular useful for patients suffering from a severe motor impairment which prevents them to interact with their surrounding environment. The application shows an image of e.g., the room of the patient, on a computer screen and searches for instances of certain objects in the image. When these are found, a flashing dot appears on top of them, flickering ...

  9. An Algorithm for Idle-State Detection in Motor-Imagery-Based Brain-Computer Interface

    OpenAIRE

    Yijun Wang; Dan Zhang; Xiaorong Gao; Bo Hong; Shangkai Gao

    2007-01-01

    For a robust brain-computer interface (BCI) system based on motor imagery (MI), it should be able to tell when the subject is not concentrating on MI tasks (the “idle state”) so that real MI tasks could be extracted accurately. Moreover, because of the diversity of idle state, detecting idle state without training samples is as important as classifying MI tasks. In this paper, we propose an algorithm for solving this ...

  10. Brain Computer Interfaces for Communication in Paralysis: a Clinical-Experimental Approach

    OpenAIRE

    Hinterberger, T.; F. Nijboer; Kübler, A; Matuz, T.; Furdea, A.; Mochty, U.; Jordan, M.; Lal, T.N; Hill, J.; MELLINGER, J.; Bensch, M.; Tangermann, M.; Widmann, G; Elger, C; Rosenstiel, W.

    2007-01-01

    An overview of different approaches to brain-computer interfaces (BCIs) developed in our laboratory is given. An important clinical application of BCIs is to enable communication or environmental control in severely paralyzed patients. The BCI 'Thought-Translation Device (TTD)' allows verbal communication through the voluntary self-regulation of brain signals (e.g., slow cortical potentials (SCPs)), which is achieved by operant feedback train-ing. Humans' ability to self-regulate their SCPs i...

  11. Brain computer tomography in critically ill patients -- a prospective cohort study

    OpenAIRE

    Purmer Ilse M; van Iperen Erik P; Beenen Ludo F M; Kuiper Michael J; Binnekade Jan M; Vandertop Peter W; Schultz Marcus J; Horn Janneke

    2012-01-01

    Abstract Background Brain computer tomography (brain CT) is an important imaging tool in patients with intracranial disorders. In ICU patients, a brain CT implies an intrahospital transport which has inherent risks. The proceeds and consequences of a brain CT in a critically ill patient should outweigh these risks. The aim of this study was to critically evaluate the diagnostic and therapeutic yield of brain CT in ICU patients. Methods In a prospective observational study data were collected ...

  12. Subject Combination and Electrode Selection in Cooperative Brain-Computer Interface Based on Event Related Potentials

    OpenAIRE

    Hubert Cecotti; Bertrand Rivet

    2014-01-01

    New paradigms are required in Brain-Computer Interface (BCI) systems for the needs and expectations of healthy people. To solve this issue, we explore the emerging field of cooperative BCIs, which involves several users in a single BCI system. Contrary to classical BCIs that are dependent on the unique subject’s will, cooperative BCIs are used for problem solving tasks where several people shall be engaged by sharing a common goal. Similarly as combining trials over time improves performance,...

  13. Steady state visually evoked potentials based Brain computer interface test outside the lab

    OpenAIRE

    Eduardo Francisco Caicedo Bravo; Jaiber Evelio Cardona Aristizábal

    2016-01-01

    Context: Steady State Visually Evoked Potentials (SSVEP) are brain signals which are one of the most promising signals for Brain Computer Interfaces (BCIs) implementation, however, SSVEP based BCI generally are proven in a controlled environment and there are a few tests in demanding conditions.Method: We present a SSVEP based BCI system that was used outside the lab in a noisy environment with distractions, and with the presence of public. For the tests, we showed a maze in a laptop where th...

  14. sBCI-Headset—Wearable and Modular Device for Hybrid Brain-Computer Interface

    OpenAIRE

    Tatsiana Malechka; Tobias Tetzel; Ulrich Krebs; Diana Feuser; Axel Graeser

    2015-01-01

    Severely disabled people, like completely paralyzed persons either with tetraplegia or similar disabilities who cannot use their arms and hands, are often considered as a user group of Brain Computer Interfaces (BCI). In order to achieve high acceptance of the BCI by this user group and their supporters, the BCI system has to be integrated into their support infrastructure. Critical disadvantages of a BCI are the time consuming preparation of the user for the electroencephalography (EEG) meas...

  15. Classification of EEG Signals in a Brain-Computer Interface System

    OpenAIRE

    Larsen, Erik Andreas

    2011-01-01

    Electroencephalography (EEG) equipment are becoming more available on thepublic market, which enables more diverse research in a currently narrow field.The Brain-Computer Interface (BCI) community recognize the need for systemsthat makes BCI more user-friendly, real-time, manageable and suited for peoplethat are not forced to use them, like clinical patients, and those who are disabled.Thus, this project is an effort to seek such improvements, having a newly availablemarket product to experim...

  16. (r)Evolution in Brain-Computer Interface Technologies for Play: (non)Users in Mind

    OpenAIRE

    Cloyd, Tristan Dane

    2014-01-01

    This dissertation addresses user responses to the introduction of Brain-Computer Interface technologies (BCI) for gaming and consumer applications in the early part of the 21st century. BCI technology has emerged from the contexts of interrelated medical, academic, and military research networks including an established computer and gaming industry. First, I show that the emergence and development of BCI technology are based on specific economic, socio-cultural, and material factors, and seco...

  17. 4-CLASS MOTOR IMAGERY CLASSIFICATION FOR POST STROKE REHABILITATION USING BRAIN-COMPUTER INTERFACE

    OpenAIRE

    Aarathi Kumar*, Nisha. P. V

    2016-01-01

    Brain-Computer Interface (BCI) is a mechanism that helps in the control/communication of one’s environment through the brain signals obtained directly from the brain via an EEG signal acquisition unit. A BCI incorporating Motor Imagery for post-stroke rehabilitation of upper limbs and knee in fully disabled patients is designed. It helps in restoring some of the activities of the daily living. It aids post-stroke sufferers to carry out functionalities like movement of right an...

  18. Affective Interaction with a Virtual Character through an fNIRS Brain-Computer Interface

    OpenAIRE

    Aranyi, Gabor; Pecune, Florian; Charles, Fred; Pelachaud, Catherine; Cavazza, Marc

    2016-01-01

    Affective brain-computer interfaces (BCI) harness Neuroscience knowledge to develop affective interaction from first principles. In this article, we explore affective engagement with a virtual agent through Neurofeedback (NF). We report an experiment where subjects engage with a virtual agent by expressing positive attitudes towards her under a NF paradigm. We use for affective input the asymmetric activity in the dorsolateral prefrontal cortex (DL-PFC), which has been previously found to be ...

  19. Manipulating Attention via Mindfulness Induction Improves P300-based Brain-Computer Interface Performance

    OpenAIRE

    Lakey, Chad E.; Berry, Daniel R.; Sellers, Eric W.

    2011-01-01

    In this study, we examined the effects of a short mindfulness meditation induction (MMI) on the performance of a P300-based brain-computer interface (BCI) task. We expected that MMI would harness present moment attentional resources, resulting in two positive consequences for P300-based BCI use. Specifically, we believed MMI would facilitate increases in task accuracy and promote the production of robust P300 amplitudes. Sixteen-channel electroencephalographic data were recorded from 18 subje...

  20. The Use of a Brain Computer Interface Remote Control to Navigate a Recreational Device

    OpenAIRE

    Shih Chung Chen; Aaron Raymond See; Yeou Jiunn Chen; Chia Hong Yeng; Chih Kuo Liang

    2013-01-01

    People suffering from paralysis caused by serious neural disorder or spinal cord injury also need to be given a means of recreation other than general living aids. Although there have been a proliferation of brain computer interface (BCI) applications, developments for recreational activities are scarcely seen. The objective of this study is to develop a BCI-based remote control integrated with commercial devices such as the remote controlled Air Swimmer. The brain is visually stimulated usin...

  1. Home Automation Using SSVEP & Eye-Blink Detection Based Brain-Computer Interface

    OpenAIRE

    Goel, Kratarth; Vohra, Raunaq; Kamath, Anant; Baths, Veeky

    2014-01-01

    In this paper, we present a novel brain computer interface based home automation system using two responses - Steady State Visually Evoked Potential (SSVEP) and the eye-blink artifact, which is augmented by a Bluetooth based indoor localization system, to greatly increase the number of controllable devices. The hardware implementation of this system to control a table lamp and table fan using brain signals has also been discussed and state-of-the-art results have been achieved.

  2. Using a brain-computer interface for rehabilitation : a case study on a patient with implanted electrodes

    OpenAIRE

    Van Langhenhove, Aurélien; Bekaert, Marie-Hélène; N'Guyen, Jean-Paul

    2008-01-01

    Brain-computer interfaces (BCIs) allow direct communication between men and computers thanks to the analysis of brain activity. Current applications of BCIs in assistive technologies are: palliative communication systems for patients with complete muscular paralysis and restoration of movement for people with a motor in firmity (orthetic or prosthetic devices controlled by the thought). It appears today that brain-computer interfaces can also be used in therapeutic approaches to rehabilitatio...

  3. Auditory imagery: empirical findings.

    Science.gov (United States)

    Hubbard, Timothy L

    2010-03-01

    The empirical literature on auditory imagery is reviewed. Data on (a) imagery for auditory features (pitch, timbre, loudness), (b) imagery for complex nonverbal auditory stimuli (musical contour, melody, harmony, tempo, notational audiation, environmental sounds), (c) imagery for verbal stimuli (speech, text, in dreams, interior monologue), (d) auditory imagery's relationship to perception and memory (detection, encoding, recall, mnemonic properties, phonological loop), and (e) individual differences in auditory imagery (in vividness, musical ability and experience, synesthesia, musical hallucinosis, schizophrenia, amusia) are considered. It is concluded that auditory imagery (a) preserves many structural and temporal properties of auditory stimuli, (b) can facilitate auditory discrimination but interfere with auditory detection, (c) involves many of the same brain areas as auditory perception, (d) is often but not necessarily influenced by subvocalization, (e) involves semantically interpreted information and expectancies, (f) involves depictive components and descriptive components, (g) can function as a mnemonic but is distinct from rehearsal, and (h) is related to musical ability and experience (although the mechanisms of that relationship are not clear). PMID:20192565

  4. A novel algorithm of super-resolution image reconstruction based on multi-class dictionaries for natural scene

    Science.gov (United States)

    Wu, Wei; Zhao, Dewei; Zhang, Huan

    2015-12-01

    Super-resolution image reconstruction is an effective method to improve the image quality. It has important research significance in the field of image processing. However, the choice of the dictionary directly affects the efficiency of image reconstruction. A sparse representation theory is introduced into the problem of the nearest neighbor selection. Based on the sparse representation of super-resolution image reconstruction method, a super-resolution image reconstruction algorithm based on multi-class dictionary is analyzed. This method avoids the redundancy problem of only training a hyper complete dictionary, and makes the sub-dictionary more representatives, and then replaces the traditional Euclidean distance computing method to improve the quality of the whole image reconstruction. In addition, the ill-posed problem is introduced into non-local self-similarity regularization. Experimental results show that the algorithm is much better results than state-of-the-art algorithm in terms of both PSNR and visual perception.

  5. Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface

    DEFF Research Database (Denmark)

    Mrachacz-Kersting, Natalie; Jiang, Ning; Stevenson, Andrew James Thomas;

    2016-01-01

    Brain-computer interfaces (BCIs) have the potential to improve functionality in chronic stoke patients when applied over a large number of sessions. Here, we evaluate the effect and the underlying mechanisms of three BCI training sessions in a double-blind-sham-controlled design. The applied BCI......-associative group. Fugl-Meyer motor scores (0.8±0.46 point difference p=0.01), foot (but not finger) tapping frequency, and 10-m walking speed improved significantly for the BCIassociative group, indicating clinically relevant improvements. For the BCI as applied here, the precise coupling between the brain command...

  6. 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.

  7. An efficient approach of EEG feature extraction and classification for brain computer interface

    Institute of Scientific and Technical Information of China (English)

    Wu Ting; Yan Guozheng; Yang Banghua

    2009-01-01

    In the study of brain-computer interfaces, a method of feature extraction and classification used for two kinds of imaginations is proposed. It considers Euclidean distance between mean traces recorded from the channels with two kinds of imaginations as a feature, and determines imagination classes using threshold value. It analyzed the background of experiment and theoretical foundation referring to the data sets of BCI 2003, and compared the classification precision with the best result of the competition. The result shows that the method has a high precision and is advantageous for being applied to practical systems.

  8. Real-Time Brain-Computer Interface System Based on Motor Imagery

    Institute of Scientific and Technical Information of China (English)

    Tie-Jun Liu; Ping Yang; Xu-Yong Peng; Yu Huang; De-Zhong Yao

    2009-01-01

    A brain-computer interface (BCI) real-time system based on motor imagery translates the user's motor intention into a real-time control signal for peripheral equipments.A key problem to be solved for practical applications is real-time data collection and processing.In this paper,a real-time BCI system is implemented on computer with electroencephalogram amplifier.In our implementation,the on-line voting method is adopted for feedback control strategy,and the voting results are used to control the cursor horizontal movement.Three subjects take part in the experiment.The results indicate that the best accuracy is 90%.

  9. 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...

  10. An efficient P300-based brain-computer interface for disabled subjects

    OpenAIRE

    Hoffmann, Ulrich; Vesin, Jean-Marc; Ebrahimi, Touradj; Diserens, Karin

    2008-01-01

    A brain-computer interface (BCI) is a communication system that translates brain-activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain-activity, without using peripheral nerves and muscles. In this paper, we present a BCI that achieves high classification accuracy and high bitrates for both disabled and able-bodied subjects. The system is based on the P300 evoked potential and is tested with five severely dis...

  11. Brain-computer interface research a state-of-the-art summary 3

    CERN Document Server

    Guger, Christoph; Allison, Brendan

    2014-01-01

    This book provides a cutting-edge overview of the latest developments in Brain-Computer-Interfaces (BCIs), reported by leading research groups. As the reader will discover, BCI research is moving ahead rapidly, with many new ideas, research initiatives, and improved technologies, e.g. BCIs that enable people to communicate just by thinking - without any movement at all. Several different groups are helping severely disabled users communicate using BCIs, and BCI technology is also being extended to facilitate recovery from stroke, epilepsy, and other conditions. Each year, hundreds of the top

  12. [Research of controlling of smart home system based on P300 brain-computer interface].

    Science.gov (United States)

    Wang, Jinjia; Yang, Chengjie

    2014-08-01

    Using electroencephalogram (EEG) signal to control external devices has always been the research focus in the field of brain-computer interface (BCI). This is especially significant for those disabilities who have lost capacity of movements. In this paper, the P300-based BCI and the microcontroller-based wireless radio frequency (RF) technology are utilized to design a smart home control system, which can be used to control household appliances, lighting system, and security devices directly. Experiment results showed that the system was simple, reliable and easy to be populirised.

  13. Using brain-computer interfaces to induce neural plasticity and restore function

    Science.gov (United States)

    Grosse-Wentrup, Moritz; Mattia, Donatella; Oweiss, Karim

    2011-04-01

    Analyzing neural signals and providing feedback in realtime is one of the core characteristics of a brain-computer interface (BCI). As this feature may be employed to induce neural plasticity, utilizing BCI technology for therapeutic purposes is increasingly gaining popularity in the BCI community. In this paper, we discuss the state-of-the-art of research on this topic, address the principles of and challenges in inducing neural plasticity by means of a BCI, and delineate the problems of study design and outcome evaluation arising in this context. We conclude with a list of open questions and recommendations for future research in this field.

  14. Brain-computer interface research a state-of-the-art summary

    CERN Document Server

    Allison, Brendan; Edlinger, Günter; Leuthardt, E C

    Brain-computer interfaces (BCIs) are rapidly developing into a mainstream, worldwide research endeavor. With so many new groups and projects, it can be difficult to identify the best ones. This book summarizes ten leading projects from around the world. About 60 submissions were received in 2011 for the highly competitive BCI Research Award, and an international jury selected the top ten. This Brief gives a concise but carefully illustrated and fully up-to-date description of each of these projects, together with an introduction and concluding chapter by the editors.

  15. Tools for Brain-Computer Interaction: a general concept for a hybrid BCI (hBCI)

    OpenAIRE

    Mueller-Putz, Gernot R.; Christian eBreitwieser; Febo eCincotti; Robert eLeeb; Martijn eSchreuder; Francesco eLeotta; Michele eTavella; Luigi eBianchi; Alex eKreilinger; Andrew eRamsay; Martin eRohm; Max eSagebaum; Luca eTonin; Christa eNeuper; José del R. eMillán

    2011-01-01

    The aim of this work is to present the development of a hybrid Brain-Computer Interface (hBCI) which combines existing input devices with a BCI. Thereby, the BCI should be available if the user wishes to extend the types of inputs available to an assistive technology system, but the user can also choose not to use the BCI at all; the BCI is active in the background. The hBCI might decide on the one hand which input channel(s) offer the most reliable signal(s) and switch between input channels...

  16. Using the Generalized Index of Dissimilarity to Detect Gene-Gene Interactions in Multi-Class Phenotypes.

    Science.gov (United States)

    Yee, Jaeyong; Kim, Yongkang; Park, Taesung; Park, Mira

    2016-01-01

    To find genetic association between complex diseases and phenotypic traits, one important procedure is conducting a joint analysis. Multifactor dimensionality reduction (MDR) is an efficient method of examining the interactions between genes in genetic association studies. It commonly assumes a dichotomous classification of the binary phenotypes. Its usual approach to determining the genomic association is to construct a confusion matrix to estimate a classification error, where a binary risk status is determined and assigned to each genotypic multifactor class. While multi-class phenotypes are commonly observed, the current MDR approach does not handle these phenotypes appropriately because the thresholds for the risk statuses may not be clear. In this study, we suggest a new method for estimating gene-gene interactions for multi-class phenotypes. Our approach adopts the index of dissimilarity (IDS) as an evaluation measure. This is analytically equivalent to the common association measure of balanced accuracy (BA) for the binary traits, while it is not required to determine the risk status for the estimation. Moreover, it is easily expandable to the generalized index of dissimilarity (GIDS), which has an explicit form that can handle any number of categories. The performance of the proposed method was compared with those of other approaches via simulation studies in which fifteen genetic models were generated with three class outcomes. A consistently better performance was observed using the proposed method. The effect of a varying number of categories was examined. The proposed method was also illustrated using real genome-wide association studies (GWAS) data from the Korean Association Resource (KARE) project. PMID:27556585

  17. Multi-class mycotoxins analysis in Angelica sinensis by ultra fast liquid chromatography coupled with tandem mass spectrometry.

    Science.gov (United States)

    Liu, Qiutao; Kong, Weijun; Guo, Weiying; Yang, Meihua

    2015-04-15

    An ultra fast liquid chromatography coupled with tandem mass spectrometry (UFLC-MS/MS) method was developed and validated for simultaneous analysis of multi-class mycotoxins including aflatoxins (AFB1, AFB2, AFG1 and AFG2), ochratoxin A (OTA), fumonisins (FB1 and FB2) and zearalanone (ZEN) in 20 batches of Angelica sinensis samples collected from different markets and stores in China. The eight mycotoxins were extracted and cleaned up by using QuEChERS-based procedure, and then were quantified under the multiple reaction monitoring (MRM) together with positive and negative ionization modes. Focusing on the optimization of extraction and clean-up conditions, as well as UFLC separation and MS/MS parameters of targeted analytes, the developed method expressed good linearity for the eight mycotoxins within their respective linear ranges with correlation coefficients all higher than 0.9974. The limits of detection (LODs) and quantification (LOQs) ranged from 0.005 to 0.125 μg/kg and from 0.0625 to 0.25 μg/kg, respectively. Recoveries for spiked A. sinensis sample at three different levels were all above 78.9% with relative standard deviations (RSDs) below 6.36% for all analytes. Analysis of real samples demonstrated that two visibly moldy A. sinensis samples were detected with AFB1 of 2.07 and 2.92 μg/kg, and AFG1 of 2.84 and 1.53 μg/kg. The proposed quantitative method with significant advantages including simple pretreatment, rapid determination and high sensitivity would be the preferred candidate for the determination and quantification of multi-class mycotoxin contaminants in complex matrixes, which well fulfilled the maximum residue limits (MRLs) from various countries.

  18. 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.

  19. As We May Think and Be: Brain-computer interfaces to expand the substrate of mind

    Directory of Open Access Journals (Sweden)

    Mijail Demian Serruya

    2015-04-01

    Full Text Available Over a half-century ago, the scientist Vannevar Bush explored the conundrum of how to tap the exponentially rising sea of human knowledge for the betterment of humanity. In his description of a hypothetical electronic library he dubbed the memex, he anticipated internet search and online encyclopedias (Bush, 1945. By blurring the boundary between brain and computer, brain-computer interfaces (BCI could lead to more efficient use of electronic resources (Schalk, 2008. We could expand the substrate of the mind itself rather than merely interfacing it to external computers. Components of brain-computer interfaces could be re-arranged to create brain-brain interfaces, or tightly interconnected links between a person’s brain and ectopic neural modules. Such modules – whether sitting in a bubbling Petri dish, rendered in reciprocally linked integrated circuits, or implanted in our belly – would mark the first step on to a path of breaking out of the limitations imposed by our phylogenetic past Novel BCI architectures could generate novel abilities to navigate and access information that might speed translational science efforts and push the boundaries of human knowledge in an unprecedented manner.

  20. An efficient ERP-based brain-computer interface using random set presentation and face familiarity.

    Directory of Open Access Journals (Sweden)

    Seul-Ki Yeom

    Full Text Available Event-related potential (ERP-based P300 spellers are commonly used in the field of brain-computer interfaces as an alternative channel of communication for people with severe neuro-muscular diseases. This study introduces a novel P300 based brain-computer interface (BCI stimulus paradigm using a random set presentation pattern and exploiting the effects of face familiarity. The effect of face familiarity is widely studied in the cognitive neurosciences and has recently been addressed for the purpose of BCI. In this study we compare P300-based BCI performances of a conventional row-column (RC-based paradigm with our approach that combines a random set presentation paradigm with (non- self-face stimuli. Our experimental results indicate stronger deflections of the ERPs in response to face stimuli, which are further enhanced when using the self-face images, and thereby improving P300-based spelling performance. This lead to a significant reduction of stimulus sequences required 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-based BCI setup.

  1. Toward high performance, weakly invasive brain computer interfaces using selective visual attention.

    Science.gov (United States)

    Rotermund, David; Ernst, Udo A; Mandon, Sunita; Taylor, Katja; Smiyukha, Yulia; Kreiter, Andreas K; Pawelzik, Klaus R

    2013-04-01

    Brain-computer interfaces have been proposed as a solution for paralyzed persons to communicate and interact with their environment. However, the neural signals used for controlling such prostheses are often noisy and unreliable, resulting in a low performance of real-world applications. Here we propose neural signatures of selective visual attention in epidural recordings as a fast, reliable, and high-performance control signal for brain prostheses. We recorded epidural field potentials with chronically implanted electrode arrays from two macaque monkeys engaged in a shape-tracking task. For single trials, we classified the direction of attention to one of two visual stimuli based on spectral amplitude, coherence, and phase difference in time windows fixed relative to stimulus onset. Classification performances reached up to 99.9%, and the information about attentional states could be transferred at rates exceeding 580 bits/min. Good classification can already be achieved in time windows as short as 200 ms. The classification performance changed dynamically over the trial and modulated with the task's varying demands for attention. For all three signal features, the information about the direction of attention was contained in the γ-band. The most informative feature was spectral amplitude. Together, these findings establish a novel paradigm for constructing brain prostheses as, for example, virtual spelling boards, promising a major gain in performance and robustness for human brain-computer interfaces.

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

    Directory of Open Access Journals (Sweden)

    Chi-Chun Lo

    2016-02-01

    Full Text Available 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.

  4. Brain-computer interface devices for patients with paralysis and amputation: a meeting report

    Science.gov (United States)

    Bowsher, K.; Civillico, E. F.; Coburn, J.; Collinger, J.; Contreras-Vidal, J. L.; Denison, T.; Donoghue, J.; French, J.; Getzoff, N.; Hochberg, L. R.; Hoffmann, M.; Judy, J.; Kleitman, N.; Knaack, G.; Krauthamer, V.; Ludwig, K.; Moynahan, M.; Pancrazio, J. J.; Peckham, P. H.; Pena, C.; Pinto, V.; Ryan, T.; Saha, D.; Scharen, H.; Shermer, S.; Skodacek, K.; Takmakov, P.; Tyler, D.; Vasudevan, S.; Wachrathit, K.; Weber, D.; Welle, C. G.; Ye, M.

    2016-04-01

    Objective. The Food and Drug Administration’s (FDA) Center for Devices and Radiological Health (CDRH) believes it is important to help stakeholders (e.g., manufacturers, health-care professionals, patients, patient advocates, academia, and other government agencies) navigate the regulatory landscape for medical devices. For innovative devices involving brain-computer interfaces, this is particularly important. Approach. Towards this goal, on 21 November, 2014, CDRH held an open public workshop on its White Oak, MD campus with the aim of fostering an open discussion on the scientific and clinical considerations associated with the development of brain-computer interface (BCI) devices, defined for the purposes of this workshop as neuroprostheses that interface with the central or peripheral nervous system to restore lost motor or sensory capabilities. Main results. This paper summarizes the presentations and discussions from that workshop. Significance. CDRH plans to use this information to develop regulatory considerations that will promote innovation while maintaining appropriate patient protections. FDA plans to build on advances in regulatory science and input provided in this workshop to develop guidance that provides recommendations for premarket submissions for BCI devices. These proceedings will be a resource for the BCI community during the development of medical devices for consumers.

  5. Auditory-motor learning influences auditory memory for music.

    Science.gov (United States)

    Brown, Rachel M; Palmer, Caroline

    2012-05-01

    In two experiments, we investigated how auditory-motor learning influences performers' memory for music. Skilled pianists learned novel melodies in four conditions: auditory only (listening), motor only (performing without sound), strongly coupled auditory-motor (normal performance), and weakly coupled auditory-motor (performing along with auditory recordings). Pianists' recognition of the learned melodies was better following auditory-only or auditory-motor (weakly coupled and strongly coupled) learning than following motor-only learning, and better following strongly coupled auditory-motor learning than following auditory-only learning. Auditory and motor imagery abilities modulated the learning effects: Pianists with high auditory imagery scores had better recognition following motor-only learning, suggesting that auditory imagery compensated for missing auditory feedback at the learning stage. Experiment 2 replicated the findings of Experiment 1 with melodies that contained greater variation in acoustic features. Melodies that were slower and less variable in tempo and intensity were remembered better following weakly coupled auditory-motor learning. These findings suggest that motor learning can aid performers' auditory recognition of music beyond auditory learning alone, and that motor learning is influenced by individual abilities in mental imagery and by variation in acoustic features. PMID:22271265

  6. Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks

    Directory of Open Access Journals (Sweden)

    Martin Alberto JM

    2009-01-01

    Full Text Available Abstract Background Prediction of protein structures from their sequences is still one of the open grand challenges of computational biology. Some approaches to protein structure prediction, especially ab initio ones, rely to some extent on the prediction of residue contact maps. Residue contact map predictions have been assessed at the CASP competition for several years now. Although it has been shown that exact contact maps generally yield correct three-dimensional structures, this is true only at a relatively low resolution (3–4 Å from the native structure. Another known weakness of contact maps is that they are generally predicted ab initio, that is not exploiting information about potential homologues of known structure. Results We introduce a new class of distance restraints for protein structures: multi-class distance maps. We show that Cα trace reconstructions based on 4-class native maps are significantly better than those from residue contact maps. We then build two predictors of 4-class maps based on recursive neural networks: one ab initio, or relying on the sequence and on evolutionary information; one template-based, or in which homology information to known structures is provided as a further input. We show that virtually any level of sequence similarity to structural templates (down to less than 10% yields more accurate 4-class maps than the ab initio predictor. We show that template-based predictions by recursive neural networks are consistently better than the best template and than a number of combinations of the best available templates. We also extract binary residue contact maps at an 8 Å threshold (as per CASP assessment from the 4-class predictors and show that the template-based version is also more accurate than the best template and consistently better than the ab initio one, down to very low levels of sequence identity to structural templates. Furthermore, we test both ab-initio and template-based 8

  7. Quantifying attentional modulation of auditory-evoked cortical responses from single-trial electroencephalography

    Directory of Open Access Journals (Sweden)

    Inyong eChoi

    2013-04-01

    Full Text Available Selective auditory attention is essential for human listeners to be able to communicate in multi-source environments. Selective attention is known to modulate the neural representation of the auditory scene, boosting the representation of a target sound relative to the background, but the strength of this modulation, and the mechanisms contributing to it, are not well understood. Here, listeners performed a behavioral experiment demanding sustained, focused spatial auditory attention while we measured cortical responses using electroencephalography (EEG. We presented three concurrent melodic streams; listeners were asked to attend and analyze the melodic contour of one of the streams, randomly selected from trial to trial. In a control task, listeners heard the same sound mixtures, but performed the contour judgment task on a series of visual arrows, ignoring all auditory streams. We found that the cortical responses could be fit as weighted sum of event-related potentials evoked by the stimulus onsets in the competing streams. The weighting to a given stream was roughly 10 dB higher when it was attended compared to when another auditory stream was attended; during the visual task, the auditory gains were intermediate. We then used a template-matching classification scheme to classify single-trial EEG results. We found that in all subjects, we could determine which stream the subject was attending significantly better than by chance. By directly quantifying the effect of selective attention on auditory cortical responses, these results reveal that focused auditory attention both suppresses the response to an unattended stream and enhances the response to an attended stream. The single-trial classification results add to the growing body of literature suggesting that auditory attentional modulation is sufficiently robust that it could be used as a control mechanism in brain-computer interfaces.

  8. Multi-Class Classification Methods of Cost-Conscious LS-SVM for Fault Diagnosis of Blast Furnace%Multi-Class Classification Methods of Cost-Conscious LS-SVM for Fault Diagnosis of Blast Furnace

    Institute of Scientific and Technical Information of China (English)

    LIU Li-mei; WANG An-na; SHA Mo; ZHAO Feng-yun

    2011-01-01

    Aiming at the limitations of rapid fault diagnosis of blast furnace, a novel strategy based on cost-conscious least squares support vector machine (LS-SVM) is proposed to solve this problem. Firstly, modified discrete particle swarm optimization is applied to optimize the feature selection and the LS-SVM parameters. Secondly, cost-con- scious formula is presented for fitness function and it contains in detail training time, recognition accuracy and the feature selection. The CLS-SVM algorithm is presented to increase the performance of the LS-SVM classifier. The new method can select the best fault features in much shorter time and have fewer support vectbrs and better general- ization performance in the application of fault diagnosis of the blast furnace. Thirdly, a gradual change binary tree is established for blast furnace faults diagnosis. It is a multi-class classification method based on center-of-gravity formula distance of cluster. A gradual change classification percentage ia used to select sample randomly. The proposed new metbod raises the sped of diagnosis, optimizes the classifieation scraraey and has good generalization ability for fault diagnosis of the application of blast furnace.

  9. A multi-class predictor based on a probabilistic model: application to gene expression profiling-based diagnosis of thyroid tumors

    Directory of Open Access Journals (Sweden)

    Noguchi Shinzaburo

    2006-07-01

    Full Text Available Abstract Background Although microscopic diagnosis has been playing the decisive role in cancer diagnostics, there have been cases in which it does not satisfy the clinical need. Differential diagnosis of malignant and benign thyroid tissues is one such case, and supplementary diagnosis such as that by gene expression profile is expected. Results With four thyroid tissue types, i.e., papillary carcinoma, follicular carcinoma, follicular adenoma, and normal thyroid, we performed gene expression profiling with adaptor-tagged competitive PCR, a high-throughput RT-PCR technique. For differential diagnosis, we applied a novel multi-class predictor, introducing probabilistic outputs. Multi-class predictors were constructed using various combinations of binary classifiers. The learning set included 119 samples, and the predictors were evaluated by strict leave-one-out cross validation. Trials included classical combinations, i.e., one-to-one, one-to-the-rest, but the predictor using more combination exhibited the better prediction accuracy. This characteristic was consistent with other gene expression data sets. The performance of the selected predictor was then tested with an independent set consisting of 49 samples. The resulting test prediction accuracy was 85.7%. Conclusion Molecular diagnosis of thyroid tissues is feasible by gene expression profiling, and the current level is promising towards the automatic diagnostic tool to complement the present medical procedures. A multi-class predictor with an exhaustive combination of binary classifiers could achieve a higher prediction accuracy than those with classical combinations and other predictors such as multi-class SVM. The probabilistic outputs of the predictor offer more detailed information for each sample, which enables visualization of each sample in low-dimensional classification spaces. These new concepts should help to improve the multi-class classification including that of cancer tissues.

  10. Common Spatial Pattern Ensemble Classifier and Its Application in Brain-Computer Interface

    Institute of Scientific and Technical Information of China (English)

    Xu Lei; Ping Yang; Peng Xu; Tie-Jun Liu; De-Zhong Yao

    2009-01-01

    Common spatial pattern (CSP) algorithm is a successful tool in feature estimate of brain-computer interface (BCI).However,CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials.In this paper,we propose a simple yet effective approach,named common spatial pattern ensemble (CSPE) classifier,to improve CSP performance.Through division of recording channels,multiple CSP filters are constructed.By projection,log-operation,and subtraction on the original signal,an ensemble classifier,majority voting,is achieved and outlier contaminations are alleviated.Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.

  11. 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...... 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...... 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...

  12. Joint Time-Frequency-Space Classification of EEG in a Brain-Computer Interface Application

    Directory of Open Access Journals (Sweden)

    Molina Gary N Garcia

    2003-01-01

    Full Text Available Brain-computer interface is a growing field of interest in human-computer interaction with diverse applications ranging from medicine to entertainment. In this paper, we present a system which allows for classification of mental tasks based on a joint time-frequency-space decorrelation, in which mental tasks are measured via electroencephalogram (EEG signals. The efficiency of this approach was evaluated by means of real-time experimentations on two subjects performing three different mental tasks. To do so, a number of protocols for visualization, as well as training with and without feedback, were also developed. Obtained results show that it is possible to obtain good classification of simple mental tasks, in view of command and control, after a relatively small amount of training, with accuracies around 80%, and in real time.

  13. Design of a Workstation for People with Upper-Limb Disabilities Using a Brain Computer Interface

    Directory of Open Access Journals (Sweden)

    John E. Muñoz-Cardona

    2013-11-01

    Full Text Available  This paper shows the design of work-station for work-related inclusion people upper-limb disability. The system involves the use of novel brain computer interface used to bridge the user-computer interaction. Our hope objective is elucidating functional, technological, ergonomic and procedural aspects to runaway operation station; with propose to scratch barrier to impossibility access to TIC’s tools and work done for individual disability person. We found access facility ergonomics, adaptability and portable issue of workstation are most important design criteria. Prototype implementations in workplace environment have TIR estimate of 43% for retrieve. Finally we list a typology of services that could be the most appropriate for the process of labor including: telemarketing, telesales, telephone surveys, order taking, social assistance in disasters, general information and inquiries, reservations at tourist sites, technical support, emergency, online support and after-sales services.

  14. Recursive N-way partial least squares for brain-computer interface.

    Directory of Open Access Journals (Sweden)

    Andrey Eliseyev

    Full Text Available In the article tensor-input/tensor-output blockwise Recursive N-way Partial Least Squares (RNPLS regression is considered. It combines the multi-way tensors decomposition with a consecutive calculation scheme and allows blockwise treatment of tensor data arrays with huge dimensions, as well as the adaptive modeling of time-dependent processes with tensor variables. In the article the numerical study of the algorithm is undertaken. The RNPLS algorithm demonstrates fast and stable convergence of regression coefficients. Applied to Brain Computer Interface system calibration, the algorithm provides an efficient adjustment of the decoding model. Combining the online adaptation with easy interpretation of results, the method can be effectively applied in a variety of multi-modal neural activity flow modeling tasks.

  15. Coordinated control of an intelligent wheelchair based on a brain-computer interface and speech recognition

    Institute of Scientific and Technical Information of China (English)

    Hong-tao WANG; Yuan-qing LI; Tian-you YU

    2014-01-01

    An intelligent wheelchair is devised, which is controlled by a coordinated mechanism based on a brain-computer interface (BCI) and speech recognition. By performing appropriate activities, users can navigate the wheelchair with four steering behaviors (start, stop, turn left, and turn right). Five healthy subjects participated in an indoor experiment. The results demonstrate the efficiency of the coordinated control mechanism with satisfactory path and time optimality ratios, and show that speech recognition is a fast and accurate supplement for BCI-based control systems. The proposed intelligent wheelchair is especially suitable for patients suffering from paralysis (especially those with aphasia) who can learn to pronounce only a single sound (e.g.,‘ah’).

  16. 脑-机接口技术综述%Review of brain-computer interface technology

    Institute of Scientific and Technical Information of China (English)

    沈敏

    2007-01-01

    脑-机接口(brain computer interface,BCI)是在人脑与计算机或其它电子设备之间建立的直接的交流和控制通道,通过这种通道,人就可以直接通过脑来表达想法或操纵设备,而不需要语言或动作.脑-机接口是一种全新的通讯和控制技术.对脑-机接口技术的发展、研究现状、工作原理以及涉及的关键技术进行了较为详细地综述.

  17. Towards Development of a 3-State Self-Paced Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Ali Bashashati

    2007-01-01

    the presence of a right- or a left-hand movement and the second classifies the detected movement as a right or a left one. In an offline analysis of the EEG data collected from four able-bodied individuals, the 3-state brain-computer interface shows a comparable performance with a 2-state system and significant performance improvement if used as a 2-state BCI, that is, in detecting the presence of a right- or a left-hand movement (regardless of the type of movement. It has an average true positive rate of 37.5% and 42.8% (at false positives rate of 1% in detecting right- and left-hand extensions, respectively, in the context of a 3-state self-paced BCI and average detection rate of 58.1% (at false positive rate of 1% in the context of a 2-state self-paced BCI.

  18. Z-score linear discriminant analysis for EEG based brain-computer interfaces.

    Directory of Open Access Journals (Sweden)

    Rui Zhang

    Full Text Available Linear discriminant analysis (LDA is one of the most popular classification algorithms for brain-computer interfaces (BCI. LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA, which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.

  19. Low-latency multi-threaded processing of neuronal signals for brain-computer interfaces

    Directory of Open Access Journals (Sweden)

    Jörg eFischer

    2014-01-01

    Full Text Available Brain-computer interfaces (BCIs require demanding numerical computations to transfer brain signals into control signals driving an external actuator. Increasing the computational performance of the BCI algorithms carrying out these calculations enables faster reaction to user inputs and allows using more demanding decoding algorithms. Here we introduce a modular and extensible software architecture with a multi-threaded signal processing pipeline suitable for BCI applications. The computational load and latency (the time that the system needs to react to user input are measured for different pipeline implementations in typical BCI applications with realistic parameter settings. We show that BCIs can benefit substantially from the proposed parallelization: firstly, by reducing the latency and secondly, by increasing the amount of recording channels and signal features that can be used for decoding beyond the amount which can be handled by a single thread. The proposed software architecture provides a simple, yet flexible solution for BCI applications.

  20. Model based generalization analysis of common spatial pattern in brain computer interfaces

    Science.gov (United States)

    Liu, Guangquan; Meng, Jianjun; Zhang, Dingguo; Zhu, Xiangyang

    2010-01-01

    In the motor imagery based Brain Computer Interface (BCI) research, Common Spatial Pattern (CSP) algorithm is used widely as a spatial filter on multi-channel electroencephalogram (EEG) recordings. Recently the overfitting effect of CSP has been gradually noticed, but what influence the overfitting is still unclear. In this work, the generalization of CSP is investigated by a simple linear mixing model. Several factors in this model are discussed, and the simulation results indicate that channel numbers and the correlation between signals influence the generalization of CSP significantly. A larger number of training trials and a longer time length of the trial would prevent overfitting. The experiments on real data also verify our conclusion. PMID:21886674

  1. Time sparsification of EEG signals in motor-imagery based brain computer interfaces.

    Science.gov (United States)

    Higashi, Hiroshi; Tanaka, Toshihisa

    2012-01-01

    We propose a method of sparsifying EEG signals in the time domain for common spatial patterns (CSP) which are often used for feature extraction in brain computer interfaces (BCI). For accurate classification, it is important to analyze the period of time when a BCI user performs a mental task. We address this problem by optimizing the CSP cost with a time sparsification that removes unnecessary samples from the classification. We design a cost function that has CSP spatial weights and time window as optimization parameters. To find these parameters, we use alternating optimization. In an experiment on classification of motor-imagery EEG signals, the proposed method increased classification accuracy by 6% averaged over five subjects.

  2. Ensemble regularized linear discriminant analysis classifier for P300-based brain-computer interface.

    Science.gov (United States)

    Onishi, Akinari; Natsume, Kiyohisa

    2013-01-01

    This paper demonstrates a better classification performance of an ensemble classifier using a regularized linear discriminant analysis (LDA) for P300-based brain-computer interface (BCI). The ensemble classifier with an LDA is sensitive to the lack of training data because covariance matrices are estimated imprecisely. One of the solution against the lack of training data is to employ a regularized LDA. Thus we employed the regularized LDA for the ensemble classifier of the P300-based BCI. The principal component analysis (PCA) was used for the dimension reduction. As a result, an ensemble regularized LDA classifier showed significantly better classification performance than an ensemble un-regularized LDA classifier. Therefore the proposed ensemble regularized LDA classifier is robust against the lack of training data.

  3. Towards Practical Brain-Computer Interfaces Bridging the Gap from Research to Real-World Applications

    CERN Document Server

    Dunne, Stephen; Leeb, Robert; Millán, José; Nijholt, Anton

    2013-01-01

    Brain-computer interfaces (BCIs) are devices that enable people to communicate via thought alone. Brain signals can be directly translated into messages or commands. Until recently, these devices were used primarily to help people who could not move. However, BCIs are now becoming practical tools for a wide variety of people, in many different situations. What will BCIs in the future be like? Who will use them, and why? This book, written by many of the top BCI researchers and developers, reviews the latest progress in the different components of BCIs. Chapters also discuss practical issues in an emerging BCI enabled community. The book is intended both for professionals and for interested laypeople who are not experts in BCI research.

  4. 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.

  5. Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders.

    Science.gov (United States)

    Deco, Gustavo; Kringelbach, Morten L

    2014-12-01

    The study of human brain networks with in vivo neuroimaging has given rise to the field of connectomics, furthered by advances in network science and graph theory informing our understanding of the topology and function of the healthy brain. Here our focus is on the disruption in neuropsychiatric disorders (pathoconnectomics) and how whole-brain computational models can help generate and predict the dynamical interactions and consequences of brain networks over many timescales. We review methods and emerging results that exhibit remarkable accuracy in mapping and predicting both spontaneous and task-based healthy network dynamics. This raises great expectations that whole-brain modeling and computational connectomics may provide an entry point for understanding brain disorders at a causal mechanistic level, and that computational neuropsychiatry can ultimately be leveraged to provide novel, more effective therapeutic interventions, e.g., through drug discovery and new targets for deep brain stimulation.

  6. The brain-computer: origin of the idea and progress in its realization.

    Science.gov (United States)

    Ichikawa, Michinori; Matsumoto, Gen

    2004-06-01

    The Brain-Computer is a physical analogue of a real organism which uses both a brain-inspired memory-based architecture and an output-driven learning algorithm. This system can be realized by creating a scaled-down model car that learns how to drive by heuristically connecting image processing with behavior control. This study proves that learning efficiency progresses rapidly when the acquired behaviors are prioritized. We develop a small real-world device that moves about purposefully in an artificial environment. The robot uses imaging information acquired through its random actions to make a mental map. This map, then, provides the cognitive structure for acquiring necessary information for autonomous behavior.

  7. A development architecture for serious games using BCI (brain computer interface) sensors.

    Science.gov (United States)

    Sung, Yunsick; Cho, Kyungeun; Um, Kyhyun

    2012-11-12

    Games that use brainwaves via brain-computer interface (BCI) devices, to improve brain functions are known as BCI serious games. Due to the difficulty of developing BCI serious games, various BCI engines and authoring tools are required, and these reduce the development time and cost. However, it is desirable to reduce the amount of technical knowledge of brain functions and BCI devices needed by game developers. Moreover, a systematic BCI serious game development process is required. In this paper, we present a methodology for the development of BCI serious games. We describe an architecture, authoring tools, and development process of the proposed methodology, and apply it to a game development approach for patients with mild cognitive impairment as an example. This application demonstrates that BCI serious games can be developed on the basis of expert-verified theories.

  8. DARPA-funded efforts in the development of novel brain-computer interface technologies.

    Science.gov (United States)

    Miranda, Robbin A; Casebeer, William D; Hein, Amy M; Judy, Jack W; Krotkov, Eric P; Laabs, Tracy L; Manzo, Justin E; Pankratz, Kent G; Pratt, Gill A; Sanchez, Justin C; Weber, Douglas J; Wheeler, Tracey L; Ling, Geoffrey S F

    2015-04-15

    The Defense Advanced Research Projects Agency (DARPA) has funded innovative scientific research and technology developments in the field of brain-computer interfaces (BCI) since the 1970s. This review highlights some of DARPA's major advances in the field of BCI, particularly those made in recent years. Two broad categories of DARPA programs are presented with respect to the ultimate goals of supporting the nation's warfighters: (1) BCI efforts aimed at restoring neural and/or behavioral function, and (2) BCI efforts aimed at improving human training and performance. The programs discussed are synergistic and complementary to one another, and, moreover, promote interdisciplinary collaborations among researchers, engineers, and clinicians. Finally, this review includes a summary of some of the remaining challenges for the field of BCI, as well as the goals of new DARPA efforts in this domain. PMID:25107852

  9. DARPA-funded efforts in the development of novel brain-computer interface technologies.

    Science.gov (United States)

    Miranda, Robbin A; Casebeer, William D; Hein, Amy M; Judy, Jack W; Krotkov, Eric P; Laabs, Tracy L; Manzo, Justin E; Pankratz, Kent G; Pratt, Gill A; Sanchez, Justin C; Weber, Douglas J; Wheeler, Tracey L; Ling, Geoffrey S F

    2015-04-15

    The Defense Advanced Research Projects Agency (DARPA) has funded innovative scientific research and technology developments in the field of brain-computer interfaces (BCI) since the 1970s. This review highlights some of DARPA's major advances in the field of BCI, particularly those made in recent years. Two broad categories of DARPA programs are presented with respect to the ultimate goals of supporting the nation's warfighters: (1) BCI efforts aimed at restoring neural and/or behavioral function, and (2) BCI efforts aimed at improving human training and performance. The programs discussed are synergistic and complementary to one another, and, moreover, promote interdisciplinary collaborations among researchers, engineers, and clinicians. Finally, this review includes a summary of some of the remaining challenges for the field of BCI, as well as the goals of new DARPA efforts in this domain.

  10. A two-class brain computer interface to freely navigate through virtual worlds.

    Science.gov (United States)

    Ron-Angevin, Ricardo; Díaz-Estrella, Antonio; Velasco-Alvarez, Francisco

    2009-06-01

    A brain computer interface that enables navigation through a virtual environment (VE) using four different navigation commands (turn right, turn left, move forward and move back) is presented. A graphical interface allows subjects to select a specific command. In this interface, the different navigation commands are surrounding a circle. A bar in the center of the circle is continuously rotating. The subject controls, by only two mental tasks, the bar extension to reach the chosen command. In this study, after an initial training based on three sessions, 8 out of 15 naive subjects were able to navigate through the VE discriminating between imagination of right-hand movements and relaxed state. All subjects (except one) improved their performance in each run and a mean error rate of 23.75% was obtained. PMID:19469662

  11. Cognitive assessment of executive functions using brain computer interface and eye-tracking

    Directory of Open Access Journals (Sweden)

    P. Cipresso

    2013-03-01

    Full Text Available New technologies to enable augmentative and alternative communication in Amyotrophic Lateral Sclerosis (ALS have been recently used in several studies. However, a comprehensive battery for cognitive assessment has not been implemented yet. Brain computer interfaces are innovative systems able to generate a control signal from brain responses conveying messages directly to a computer. Another available technology for communication purposes is the Eye-tracker system, that conveys messages from eye-movement to a computer. In this study we explored the use of these two technologies for the cognitive assessment of executive functions in a healthy population and in a ALS patient, also verifying usability, pleasantness, fatigue, and emotional aspects related to the setting. Our preliminary results may have interesting implications for both clinical practice (the availability of an effective tool for neuropsychological evaluation of ALS patients and ethical issues.

  12. Does distracting pain justify performing brain computed tomography in multiple traumas with mild head injury?

    Science.gov (United States)

    Sadeghian, Homa; Motiei-Langroudi, Rouzbeh

    2016-06-01

    Traumatic brain injury (TBI) is a significant health concern classified as mild, moderate, and severe. Although the indications to perform brain computed tomography (CT) are clear in moderate and severe cases, there still exists controversy in mild TBI (mTBI). We designed the study to evaluate the significance of distracting pain in patients with mTBI. The study population included patients with mild traumatic brain injury (GCS ≥13). Moderate and high risk factors including age cerebral edema in brain CT (p > 0.99). No patients had any neurologic symptoms or signs in follow-up. Our results show that in the absence of any other risk factors, distracting pain from other organs (limbs, pelvis, and non-cervical spine) cannot be regarded as a brain CT indication in patients with mild TBI, as it is never associated with significant intracranial lesions. PMID:26931118

  13. EEG Signal Denoising and Feature Extraction Using Wavelet Transform in Brain Computer Interface

    Institute of Scientific and Technical Information of China (English)

    WU Ting; YAN Guo-zheng; YANG Bang-hua; SUN Hong

    2007-01-01

    Electroencephalogram (EEG) signal preprocessing is one of the most important techniques in brain computer interface (BCI). The target is to increase signal-to-noise ratio and make it more favorable for feature extraction and pattern recognition. Wavelet transform is a method of multi-resolution time-frequency analysis, it can decompose the mixed signals which consist of different frequencies into different frequency band. EEG signal is analyzed and denoised using wavelet transform. Moreover, wavelet transform can be used for EEG feature extraction. The energies of specific sub-bands and corresponding decomposition coefficients which have maximal separability according to the Fisher distance criterion are selected as features. The eigenvector for classification is obtained by combining the effective features from different channels. The performance is evaluated by separability and pattern recognition accuracy using the data set of BCI 2003 Competition, the final classification results have proved the effectiveness of this technology for EEG denoising and feature extraction.

  14. Improvements of a Brain-Computer Interface Applied to a Robotic Wheelchair

    Science.gov (United States)

    Ferreira, André; Bastos-Filho, Teodiano Freire; Sarcinelli-Filho, Mário; Sánchez, José Luis Martín; García, Juan Carlos García; Quintas, Manuel Mazo

    Two distinct signal features suitable to be used as input to a Support-Vector Machine (SVM) classifier in an application involving hands motor imagery and the correspondent EEG signal are evaluated in this paper. Such features are the Power Spectral Density (PSD) components and the Adaptive Autoregressive (AAR) parameters. The best result (an accuracy of 97.1%) is obtained when using PSD components, while the AAR parameters generated an accuracy of 91.4%. The results also demonstrate that it is possible to use only two EEG channels (bipolar configuration around C 3 and C 4), discarding the bipolar configuration around C z . The algorithms were tested with a proprietary EEG data set involving 4 individuals and with a data set provided by the University of Graz (Austria) as well. The resulting classification system is now being implemented in a Brain-Computer Interface (BCI) used to guide a robotic wheelchair.

  15. Bipolar electrode selection for a motor imagery based brain computer interface

    Science.gov (United States)

    Lou, Bin; Hong, Bo; Gao, Xiaorong; Gao, Shangkai

    2008-09-01

    A motor imagery based brain-computer interface (BCI) provides a non-muscular communication channel that enables people with paralysis to control external devices using their motor imagination. Reducing the number of electrodes is critical to improving the portability and practicability of the BCI system. A novel method is proposed to reduce the number of electrodes to a total of four by finding the optimal positions of two bipolar electrodes. Independent component analysis (ICA) is applied to find the source components of mu and alpha rhythms, and optimal electrodes are chosen by comparing the projection weights of sources on each channel. The results of eight subjects demonstrate the better classification performance of the optimal layout compared with traditional layouts, and the stability of this optimal layout over a one week interval was further verified.

  16. [Research on magnetoencephalography-brain computer interface based on the PCA and LDA data reduction].

    Science.gov (United States)

    Wang, Jinjia; Zhou, Lina

    2011-12-01

    The magnetoencephalography (MEG) can be used as a control signal for brain computer interface (BCI). The BCI also includes the pattern information of the direction of hand movement. In the MEG signal classification, the feature extraction based on signal processing and linear classification is usually used. But the recognition rate has been difficult to improve. In the present paper, a principal component analysis (PCA) and linear discriminant analysis (LDA) method has been proposed for the feature extraction, and the non-linear nearest neighbor classification is introduced for the classifier. The confusion matrix is analyzed based on the results. The experimental results show that the PCA + LDA method is effective in the analysis of multi-channel MEG signals, improves the recognition rate to the extent of the average recognition rate 55.7%, which is better than the recognition rate 46.9% in the BCI competition IV.

  17. Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects

    Directory of Open Access Journals (Sweden)

    Nikolay V. Manyakov

    2011-01-01

    Full Text Available We report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI on a group of amyotrophic lateral sclerosis (ALS, middle cerebral artery (MCA stroke, and subarachnoid hemorrhage (SAH patients, suffering from motor and speech disabilities. We investigate the achieved typing accuracy given the individual patient's disorder, and how it correlates with the type of classifier used. We considered 7 types of classifiers, linear as well as nonlinear ones, and found that, overall, one type of linear classifier yielded a higher classification accuracy. In addition to the selection of the classifier, we also suggest and discuss a number of recommendations to be considered when building a P300-based typing system for disabled subjects.

  18. Brain-computer interface using P300 and virtual reality: a gaming approach for treating ADHD.

    Science.gov (United States)

    Rohani, Darius Adam; Sorensen, Helge B D; Puthusserypady, Sadasivan

    2014-01-01

    This paper presents a novel brain-computer interface (BCI) system aiming at the rehabilitation of attention-deficit/hyperactive disorder in children. It uses the P300 potential in a series of feedback games to improve the subjects' attention. We applied a support vector machine (SVM) using temporal and template-based features to detect these P300 responses. In an experimental setup using five subjects, an average error below 30% was achieved. To make it more challenging the BCI system has been embedded inside an immersive 3D virtual reality (VR) classroom with simulated distractions, which was created by combining a low-cost infrared camera and an "off-axis perspective projection" algorithm. This system is intended for kids by operating with four electrodes, as well as a non-intrusive VR setting. With the promising results, and considering the simplicity of the scheme, we hope to encourage future studies to adapt the techniques presented in this study.

  19. Write, read and answer emails with a dry 'n' wireless brain-computer interface system.

    Science.gov (United States)

    Pinegger, Andreas; Deckert, Lisa; Halder, Sebastian; Barry, Norbert; Faller, Josef; Käthner, Ivo; Hintermüller, Christoph; Wriessnegger, Selina C; Kübler, Andrea; Müller-Putz, Gernot R

    2014-01-01

    Brain-computer interface (BCI) users can control very complex applications such as multimedia players or even web browsers. Therefore, different biosignal acquisition systems are available to noninvasively measure the electrical activity of the brain, the electroencephalogram (EEG). To make BCIs more practical, hardware and software are nowadays designed more user centered and user friendly. In this paper we evaluated one of the latest innovations in the area of BCI: A wireless EEG amplifier with dry electrode technology combined with a web browser which enables BCI users to use standard webmail. With this system ten volunteers performed a daily life task: Write, read and answer an email. Experimental results of this study demonstrate the power of the introduced BCI system.

  20. Motor prediction in Brain-Computer Interfaces for controlling mobile robots.

    Science.gov (United States)

    Geng, Tao; Gan, John Q

    2008-01-01

    EEG-based Brain-Computer Interface (BCI) can be regarded as a new channel for motor control except that it does not involve muscles. Normal neuromuscular motor control has two fundamental components: (1) to control the body, and (2) to predict the consequences of the control command, which is called motor prediction. In this study, after training with a specially designed BCI paradigm based on motor imagery, two subjects learnt to predict the time course of some features of the EEG signals. It is shown that, with this newly-obtained motor prediction skill, subjects can use motor imagery of feet to directly control a mobile robot to avoid obstacles and reach a small target in a time-critical scenario.

  1. Latency Assurances for Multi-Class Traffic at the QoS-Capable Home Network Wireless Access Point (Tutorial

    Directory of Open Access Journals (Sweden)

    Chaiwat OOTTAMAKORN

    2004-06-01

    Full Text Available Emerging broadband networks and technologies have changed the way of home network provision. Provisioning the broadband services has merely accelerated the complexity of home networks to grow. The services demanded by applications are still inadequate under current home network infrastructures. These services require different Quality of Service (QoS requirements from the networks. As wireless-based communication within the home (e.g. 802.11´ gains wide acceptance because of its mature protocol, becoming the predominant technology for home networking, the question of efficient QoS-aware wireless access point resource allocation and scheduling function implementation arises. In this article we present a resource management and scheduling algorithm for a QoS-aware wireless access point for use in enterprise and home network solutions. The presented algorithm provides delay and packet loss service guarantees to a diverse set of application traffic classes, produced as a result of convergence of home entertainment and data networks. The algorithm is simple, controllable, and scalable for implementation with the support of both absolute and relative QoS guarantees to multi-class traffic.

  2. Canonical feature selection for joint regression and multi-class identification in Alzheimer’s disease diagnosis

    Science.gov (United States)

    Zhu, Xiaofeng; Suk, Heung-Il

    2016-01-01

    Fusing information from different imaging modalities is crucial for more accurate identification of the brain state because imaging data of different modalities can provide complementary perspectives on the complex nature of brain disorders. However, most existing fusion methods often extract features independently from each modality, and then simply concatenate them into a long vector for classification, without appropriate consideration of the correlation among modalities. In this paper, we propose a novel method to transform the original features from different modalities to a common space, where the transformed features become comparable and easy to find their relation, by canonical correlation analysis. We then perform the sparse multi-task learning for discriminative feature selection by using the canonical features as regressors and penalizing a loss function with a canonical regularizer. In our experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, we use Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images to jointly predict clinical scores of Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) and also identify multi-class disease status for Alzheimer’s disease diagnosis. The experimental results showed that the proposed canonical feature selection method helped enhance the performance of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods. PMID:26254746

  3. Auditory Responses of Infants

    Science.gov (United States)

    Watrous, Betty Springer; And Others

    1975-01-01

    Forty infants, 3- to 12-months-old, participated in a study designed to differentiate the auditory response characteristics of normally developing infants in the age ranges 3 - 5 months, 6 - 8 months, and 9 - 12 months. (Author)

  4. [Central auditory prosthesis].

    Science.gov (United States)

    Lenarz, T; Lim, H; Joseph, G; Reuter, G; Lenarz, M

    2009-06-01

    Deaf patients with severe sensory hearing loss can benefit from a cochlear implant (CI), which stimulates the auditory nerve fibers. However, patients who do not have an intact auditory nerve cannot benefit from a CI. The majority of these patients are neurofibromatosis type 2 (NF2) patients who developed neural deafness due to growth or surgical removal of a bilateral acoustic neuroma. The only current solution is the auditory brainstem implant (ABI), which stimulates the surface of the cochlear nucleus in the brainstem. Although the ABI provides improvement in environmental awareness and lip-reading capabilities, only a few NF2 patients have achieved some limited open set speech perception. In the search for alternative procedures our research group in collaboration with Cochlear Ltd. (Australia) developed a human prototype auditory midbrain implant (AMI), which is designed to electrically stimulate the inferior colliculus (IC). The IC has the potential as a new target for an auditory prosthesis as it provides access to neural projections necessary for speech perception as well as a systematic map of spectral information. In this paper the present status of research and development in the field of central auditory prostheses is presented with respect to technology, surgical technique and hearing results as well as the background concepts of ABI and AMI. PMID:19517084

  5. EDITORIAL: Special issue containing contributions from the Fourth International Brain-Computer Interface Meeting Special issue containing contributions from the Fourth International Brain-Computer Interface Meeting

    Science.gov (United States)

    Vaughan, Theresa M.; Wolpaw, Jonathan R.

    2011-04-01

    This special issue of Journal of Neural Engineering is a result of the Fourth International Brain-Computer Interface Meeting, which was held at the Asilomar Conference Center in Monterey, California, USA from 31 May to 4 June, 2010. The meeting was sponsored by the National Institutes of Health, The National Science Foundation and the Department of Defense, and was organized by the Wadsworth Center of the New York State Department of Health. It attracted over 260 participants from 17 countries—including many graduate students and postdoctoral fellows—and featured 19 workshops, platform presentations from 26 research groups, 170 posters, multiple brain-computer interface (BCI) demonstrations, and a keynote address by W Zev Rymer of the Rehabilitation Institute of Chicago. The number of participants and the diversity of the topics covered greatly exceeded those of the previous meeting in 2005, and testified to the continuing rapid expansion and growing sophistication of this exciting and still relatively new research field. BCI research focuses primarily on using brain signals to replace or restore the motor functions that people have lost due to amyotrophic lateral sclerosis (ALS), a brainstem stroke, or some other devastating neuromuscular disorder. In the last few years, attention has also turned towards using BCIs to improve rehabilitation after a stroke, and beyond that to enhancing or supplementing the capabilities of even those without disabilities. These diverse interests were represented in the wide range of topics covered in the workshops. While some workshops addressed broad traditional topics, such as signal acquisition, feature extraction and translation, and software development, many addressed topics that were entirely new or focused sharply on areas that have become important only recently. These included workshops on optimizing P300-based BCIs; improving the mutual adaptations of the BCI and the user; BCIs that can control neuroprostheses

  6. Mining multi-channel EEG for its information content: An ANN-based method for a brain-computer interface

    DEFF Research Database (Denmark)

    Peters, B.O.; Pfurtscheller, G.; Flyvbjerg, H.

    1998-01-01

    . This high recognition rate makes the classifier suitable for a so-called 'Brain-Computer Interface', a system that allows one to control a computer, or another device, with ones brain waves. Our classifier Laplace filters the EEG spatially, but makes use of its entire frequency range, and automatically...

  7. Simulation of a real-time brain computer interface for detecting a self-paced hitting task

    DEFF Research Database (Denmark)

    Hammad, Sofyan H.; Kamavuako, Ernest N.; Farina, Dario;

    2016-01-01

    OBJECTIVES: An invasive brain-computer interface (BCI) is a promising neurorehabilitation device for severely disabled patients. Although some systems have been shown to work well in restricted laboratory settings, their utility must be tested in less controlled, real-time environments. Our...

  8. A Brain Computer Interface for Robust Wheelchair Control Application Based on Pseudorandom Code Modulated Visual Evoked Potential

    DEFF Research Database (Denmark)

    Mohebbi, Ali; Engelsholm, Signe K.D.; Puthusserypady, Sadasivan;

    2015-01-01

    In this pilot study, a novel and minimalistic Brain Computer Interface (BCI) based wheelchair control application was developed. The system was based on pseudorandom code modulated Visual Evoked Potentials (c-VEPs). The visual stimuli in the scheme were generated based on the Gold code...

  9. A Review of EEG-Based Brain-Computer Interfaces as Access Pathways for Individuals with Severe Disabilities

    Science.gov (United States)

    Moghimi, Saba; Kushki, Azadeh; Guerguerian, Anne Marie; Chau, Tom

    2013-01-01

    Electroencephalography (EEG) is a non-invasive method for measuring brain activity and is a strong candidate for brain-computer interface (BCI) development. While BCIs can be used as a means of communication for individuals with severe disabilities, the majority of existing studies have reported BCI evaluations by able-bodied individuals.…

  10. Modulation of Posterior Alpha Activity by Spatial Attention Allows for Controlling A Continuous Brain-Computer Interface

    NARCIS (Netherlands)

    Horschig, J.M.; Oosterheert, W.; Oostenveld, R.; Jensen, O.

    2014-01-01

    Here we report that the modulation of alpha activity by covert attention can be used as a control signal in an online brain-computer interface, that it is reliable, and that it is robust. Subjects were instructed to orient covert visual attention to the left or right hemifield. We decoded the direct

  11. Multi-class ERP-based BCI data analysis using a discriminant space self-organizing map.

    Science.gov (United States)

    Onishi, Akinari; Natsume, Kiyohisa

    2014-01-01

    Emotional or non-emotional image stimulus is recently applied to event-related potential (ERP) based brain computer interfaces (BCI). Though the classification performance is over 80% in a single trial, a discrimination between those ERPs has not been considered. In this research we tried to clarify the discriminability of four-class ERP-based BCI target data elicited by desk, seal, spider images and letter intensifications. A conventional self organizing map (SOM) and newly proposed discriminant space SOM (ds-SOM) were applied, then the discriminabilites were visualized. We also classify all pairs of those ERPs by stepwise linear discriminant analysis (SWLDA) and verify the visualization of discriminabilities. As a result, the ds-SOM showed understandable visualization of the data with a shorter computational time than the traditional SOM. We also confirmed the clear boundary between the letter cluster and the other clusters. The result was coherent with the classification performances by SWLDA. The method might be helpful not only for developing a new BCI paradigm, but also for the big data analysis.

  12. Trace analysis of multi-class pesticide residues in Chinese medicinal health wines using gas chromatography with electron capture detection

    Science.gov (United States)

    Kong, Wei-Jun; Liu, Qiu-Tao; Kong, Dan-Dan; Liu, Qian-Zhen; Ma, Xin-Ping; Yang, Mei-Hua

    2016-02-01

    A method is described for multi-residue, high-throughput determination of trace levels of 22 organochlorine pesticides (OCPs) and 5 pyrethroid pesticides (PYPs) in Chinese medicinal (CM) health wines using a QuEChERS (quick, easy, cheap, effective, rugged, and safe) based extraction method and gas chromatography-electron capture detection (GC-ECD). Several parameters were optimized to improve preparation and separation time while still maintaining high sensitivity. Validation tests of spiked samples showed good linearities for 27 pesticides (R = 0.9909-0.9996) over wide concentration ranges. Limits of detection (LODs) and quantification (LOQs) were measured at ng/L levels, 0.06-2 ng/L and 0.2-6 ng/L for OCPs and 0.02-3 ng/L and 0.06-7 ng/L for PYPs, respectively. Inter- and intra-day precision tests showed variations of 0.65-9.89% for OCPs and 0.98-13.99% for PYPs, respectively. Average recoveries were in the range of 47.74-120.31%, with relative standard deviations below 20%. The developed method was then applied to analyze 80 CM wine samples. Beta-BHC (Benzene hexachloride) was the most frequently detected pesticide at concentration levels of 5.67-31.55 mg/L, followed by delta-BHC, trans-chlordane, gamma-BHC, and alpha-BHC. The validated method is simple and economical, with adequate sensitivity for trace levels of multi-class pesticides. It could be adopted by laboratories for this and other types of complex matrices analysis.

  13. Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    This paper presents an effective and efficient combination of feature extraction and multi-class classifier for motion classification by analyzing the surface electromyografic (sEMG) signals. In contrast to the existing methods, considering the non-stationary and nonlinear characteristics of EMG signals, to get the more separable feature set, we introduce the empirical mode decomposition (EMD) to decompose the original EMG signals into several intrinsic mode functions (IMFs) and then compute the coefficients of autoregressive models of each IMF to form the feature set. Based on the least squares support vector machines (LS-SVMs), the multi-class classifier is designed and constructed to classify various motions. The results of contrastive experiments showed that the accuracy of motion recognition is improved with the described classification scheme. Furthermore,compared with other classifiers using different features, the excellent performance indicated the potential of the SVM techniques embedding the EMD-AR kernel in motion classification.

  14. A multi-class SVM based on FCOWA-ER%一种基于FCOWA-ER的SVM多分类方法

    Institute of Scientific and Technical Information of China (English)

    刘卫兵; 杨艺; 韩德强

    2015-01-01

    支持向量机(SVM)在处理多分类问题时,需要综合利用多个二分类SVM,以获得多分类判决结果。传统多分类拓展方法使用的是SVM的硬输出,在一定程度上造成了信息的丢失。为了更加充分地利用信息,提出一种基于证据推理-多属性决策方法的SVM多分类算法,将多分类问题视为一个多属性决策问题,使用证据推理-模糊谨慎有序加权平均方法(FCOWA-ER)实现SVM的多分类判决。实验结果表明,所提出方法可以获得更高的分类精度。%Multiple bi-class SVMs are used together to obtain the final decision when the support vector machine(SVM) is applied to multi-class classification problems. The conventional methods of applying the SVM to multiple classification tasks are all based on the hard output of SVM, which can bring the loss of information to some extent. Therefore, a multi-class SVM based on an evidential reasoning based multiple attribute decision approach is proposed to use more information. The multi-class classification problem is modelled as a multi-criteria decision making problem. Then a fuzzy-cautious OWA(ordered weighted averaging) approach with evidential reasoning(FCOWA-ER) is used to implement multi-class classification and obtain the final decision. The simulation results show that the method proposed has better accuracy compared with conventional methods.

  15. Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Joonwhoan Lee

    2013-06-01

    Full Text Available Facial expressions are widely used in the behavioral interpretation of emotions, cognitive science, and social interactions. In this paper, we present a novel method for fully automatic facial expression recognition in facial image sequences. As the facial expression evolves over time facial landmarks are automatically tracked in consecutive video frames, using displacements based on elastic bunch graph matching displacement estimation. Feature vectors from individual landmarks, as well as pairs of landmarks tracking results are extracted, and normalized, with respect to the first frame in the sequence. The prototypical expression sequence for each class of facial expression is formed, by taking the median of the landmark tracking results from the training facial expression sequences. Multi-class AdaBoost with dynamic time warping similarity distance between the feature vector of input facial expression and prototypical facial expression, is used as a weak classifier to select the subset of discriminative feature vectors. Finally, two methods for facial expression recognition are presented, either by using multi-class AdaBoost with dynamic time warping, or by using support vector machine on the boosted feature vectors. The results on the Cohn-Kanade (CK+ facial expression database show a recognition accuracy of 95.17% and 97.35% using multi-class AdaBoost and support vector machines, respectively.

  16. Comparison of Two Output-Coding Strategies for Multi-Class Tumor Classification Using Gene Expression Data and Latent Variable Model as Binary Classifier

    Directory of Open Access Journals (Sweden)

    Sandeep J. Joseph

    2010-03-01

    Full Text Available Multi-class cancer classification based on microarray data is described. A generalized output-coding scheme based on One Versus One (OVO combined with Latent Variable Model (LVM is used. Results from the proposed One Versus One (OVO output- coding strategy is compared with the results obtained from the generalized One Versus All (OVA method and their efficiencies of using them for multi-class tumor classification have been studied. This comparative study was done using two microarray gene expression data: Global Cancer Map (GCM dataset and brain cancer (BC dataset. Primary feature selection was based on fold change and penalized t-statistics. Evaluation was conducted with varying feature numbers. The OVO coding strategy worked quite well with the BC data, while both OVO and OVA results seemed to be similar for the GCM data. The selection of output coding methods for combining binary classifiers for multi-class tumor classification depends on the number of tumor types considered, the discrepancies between the tumor samples used for training as well as the heterogeneity of expression within the cancer subtypes used as training data.

  17. 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.

  18. Research of brain computer interface technology%脑-机接口技术研究

    Institute of Scientific and Technical Information of China (English)

    洪杰; 秦现生; 谭小群; 王文杰; 牛军龙

    2014-01-01

    脑-机接口是在人脑与计算机或其他电子设备之间建立直接的交流和控制的通道,它直接通过脑来表达想法,而不需要语言和动作,为思维正常但有严重运动障碍的患者提供了与外界交流和控制的途径,提高了他们的生活质量.本文对脑-机接口(brain computer interface,BCI)技术、研究方法、分类以及涉及的关键技术、应用领域等进行了较为详细的综述,并在此基础上分析目前BCI存在的问题,最后指出该领域的发展趋势.

  19. A submatrix-based P300 brain-computer interface stimulus presentation paradigm

    Institute of Scientific and Technical Information of China (English)

    Jin-he SHI; Ji-zhong SHEN; Yu JI; Feng-lei DU

    2012-01-01

    The P300 event-related potential (ERP),with advantages of high stability and no need for initial training,is one of the most commonly used responses in brain-computer interface (BCI) applications.The row/column paradigm (RCP) that flashes an entire colunm or row of a visual matrix has been used successfully to help patients to spell words.However,RCP remains subject to errors that slow down communication,such as adjacency-distraction and double-flash errors.In this paper,a new visual stimulus presentation paradigm called the submatrix-based paradigm (SBP) is proposed.SBP divides a 6×6 matrix into several submatrices.Each submatrix flashes in single cell paradigm (SCP) mode and separately performs an ensemble averaging method according to the sequences.The parameter of sequence number is used to improve further the accuracy and information transfer rate (ITR).SBP has advantages of flexibility in division of the matrix and better expansion capability,which were confirmed with different divisions of the 6×6 matrix and expansion to a 6x9 matrix.Stimulation results show that SBP is superior to RCP in performance and user acceptability.

  20. A Fuzzy Integral Ensemble Method in Visual P300 Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Francesco Cavrini

    2016-01-01

    Full Text Available We evaluate the possibility of application of combination of classifiers using fuzzy measures and integrals to Brain-Computer Interface (BCI based on electroencephalography. In particular, we present an ensemble method that can be applied to a variety of systems and evaluate it in the context of a visual P300-based BCI. Offline analysis of data relative to 5 subjects lets us argue that the proposed classification strategy is suitable for BCI. Indeed, the achieved performance is significantly greater than the average of the base classifiers and, broadly speaking, similar to that of the best one. Thus the proposed methodology allows realizing systems that can be used by different subjects without the need for a preliminary configuration phase in which the best classifier for each user has to be identified. Moreover, the ensemble is often capable of detecting uncertain situations and turning them from misclassifications into abstentions, thereby improving the level of safety in BCI for environmental or device control.

  1. Affective Interaction with a Virtual Character through an fNIRS Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Gabor Aranyi

    2016-07-01

    Full Text Available Affective Brain-Computer Interfaces (BCI harness Neuroscience knowledge to develop affective interaction from first principles. In this paper, we explore affective engagement with a virtual agent through Neurofeedback (NF. We report an experiment where subjects engage with a virtual agent by expressing positive attitudes towards her under a NF paradigm. We use for affective input the asymmetric activity in the dorsolateral prefrontal cortex (DL-PFC, which has been previously found to be related to the high-level affective-motivational dimension of approach/avoidance. The magnitude of left-asymmetric DL-PFC activity, measured using fNIRS and treated as a proxy for approach, is mapped onto a control mechanism for the virtual agent’s facial expressions, in which Action Units are activated through a neural network. We carried out an experiment with 18 subjects, which demonstrated that subjects are able to successfully engage with the virtual agent by controlling their mental disposition through NF, and that they perceived the agent’s responses as realistic and consistent with their projected mental disposition. This interaction paradigm is particularly relevant in the case of affective BCI as it facilitates the volitional activation of specific areas normally not under conscious control. Overall, our contribution reconciles a model of affect derived from brain metabolic data with an ecologically valid, yet computationally controllable, virtual affective communication environment.

  2. Tools for Brain-Computer Interaction: a general concept for a hybrid BCI (hBCI

    Directory of Open Access Journals (Sweden)

    Gernot R. Mueller-Putz

    2011-11-01

    Full Text Available The aim of this work is to present the development of a hybrid Brain-Computer Interface (hBCI which combines existing input devices with a BCI. Thereby, the BCI should be available if the user wishes to extend the types of inputs available to an assistive technology system, but the user can also choose not to use the BCI at all; the BCI is active in the background. The hBCI might decide on the one hand which input channel(s offer the most reliable signal(s and switch between input channels to improve information transfer rate, usability, or other factors, or on the other hand fuse various input channels. One major goal therefore is to bring the BCI technology to a level where it can be used in a maximum number of scenarios in a simple way. To achieve this, it is of great importance that the hBCI is able to operate reliably for long periods, recognizing and adapting to changes as it does so. This goal is only possible if many different subsystems in the hBCI can work together. Since one research institute alone cannot provide such different functionality, collaboration between institutes is necessary. To allow for such a collaboration, a common software framework was investigated.

  3. A brain-computer interface based attention training program for treating attention deficit hyperactivity disorder.

    Directory of Open Access Journals (Sweden)

    Choon Guan Lim

    Full Text Available UNLABELLED: Attention deficit hyperactivity disorder (ADHD symptoms can be difficult to treat. We previously reported that a 20-session brain-computer interface (BCI attention training programme improved ADHD symptoms. Here, we investigated a new more intensive BCI-based attention training game system on 20 unmedicated ADHD children (16 males, 4 females with significant inattentive symptoms (combined and inattentive ADHD subtypes. This new system monitored attention through a head band with dry EEG sensors, which was used to drive a feed forward game. The system was calibrated for each user by measuring the EEG parameters during a Stroop task. Treatment consisted of an 8-week training comprising 24 sessions followed by 3 once-monthly booster training sessions. Following intervention, both parent-rated inattentive and hyperactive-impulsive symptoms on the ADHD Rating Scale showed significant improvement. At week 8, the mean improvement was -4.6 (5.9 and -4.7 (5.6 respectively for inattentive symptoms and hyperactive-impulsive symptoms (both p<0.01. Cohen's d effect size for inattentive symptoms was large at 0.78 at week 8 and 0.84 at week 24 (post-boosters. Further analysis showed that the change in the EEG based BCI ADHD severity measure correlated with the change ADHD Rating Scale scores. The BCI-based attention training game system is a potential new treatment for ADHD. TRIAL REGISTRATION: ClinicalTrials.gov NCT01344044.

  4. SSVEP and ANN based optimal speller design for Brain Computer Interface

    Directory of Open Access Journals (Sweden)

    Irshad Ahmad Ansari

    2015-07-01

    Full Text Available This work put forwards an optimal BCI (Brain Computer Interface speller design based on Steady State Visual Evoked Potentials (SSVEP and Artificial Neural Network (ANN in order to help the people with severe motor impairments. This work is carried out to enhance the accuracy and communication rate of  BCI system. To optimize the BCI system, the work has been divided into two steps: First, designing of an encoding technique to choose characters from the speller interface and the second is the development and implementation of feature extraction algorithm to acquire optimal features, which is used to train the BCI system for classification using neural network. Optimization of speller interface is focused on representation of character matrix and its designing parameters. Then again, a lot of deliberations made in order to optimize selection of features and user’s time window. Optimized system works nearly the same with the new user and gives character per minute (CPM of 13 ± 2 with an average accuracy of 94.5% by choosing first two harmonics of power spectral density as the feature vectors and using the 2 second time window for each selection. Optimized BCI performs better with experienced users with an average accuracy of 95.1%. Such a good accuracy has not been reported before in account of fair enough CPM.DOI: 10.15181/csat.v2i2.1059

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

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    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.

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

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    Lin Yao

    Full Text Available 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.

  7. Affective Interaction with a Virtual Character Through an fNIRS Brain-Computer Interface.

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    Aranyi, Gabor; Pecune, Florian; Charles, Fred; Pelachaud, Catherine; Cavazza, Marc

    2016-01-01

    Affective brain-computer interfaces (BCI) harness Neuroscience knowledge to develop affective interaction from first principles. In this article, we explore affective engagement with a virtual agent through Neurofeedback (NF). We report an experiment where subjects engage with a virtual agent by expressing positive attitudes towards her under a NF paradigm. We use for affective input the asymmetric activity in the dorsolateral prefrontal cortex (DL-PFC), which has been previously found to be related to the high-level affective-motivational dimension of approach/avoidance. The magnitude of left-asymmetric DL-PFC activity, measured using functional near infrared spectroscopy (fNIRS) and treated as a proxy for approach, is mapped onto a control mechanism for the virtual agent's facial expressions, in which action units (AUs) are activated through a neural network. We carried out an experiment with 18 subjects, which demonstrated that subjects are able to successfully engage with the virtual agent by controlling their mental disposition through NF, and that they perceived the agent's responses as realistic and consistent with their projected mental disposition. This interaction paradigm is particularly relevant in the case of affective BCI as it facilitates the volitional activation of specific areas normally not under conscious control. Overall, our contribution reconciles a model of affect derived from brain metabolic data with an ecologically valid, yet computationally controllable, virtual affective communication environment. PMID:27462216

  8. Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges

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    José del R. Millán

    2010-09-01

    Full Text Available In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT. In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication & Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI to improve BCI usability, and the development of novel BCI technology including better EEG devices.

  9. Understanding entangled cerebral networks: A prerequisite for restoring brain function with brain-computer interfaces

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    Emmanuel eMandonnet

    2014-05-01

    Full Text Available Historically, cerebral processing has been conceptualized as a framework based on statically localized functions. However, a growing amount of evidence supports a hodotopical (delocalized and flexible organization. A number of studies have reported absence of a permanent neurological deficit after massive surgical resections of eloquent brain tissue. These results highlight the tremendous plastic potential of the brain. Understanding anatomo-functional correlates underlying this cerebral reorganization is a prerequisite to restore brain functions through brain-computer interfaces (BCIs in patients with cerebral diseases, or even to potentiate brain functions in healthy individuals. Here, we review current knowledge of neural networks that could be utilized in the BCIs that enable movements and language. To this end, intraoperative electrical stimulation in awake patients provides valuable information on the cerebral functional maps, their connectomics and plasticity. Overall, these studies indicate that the complex cerebral circuitry that underpins interactions between action, cognition and behavior should be throughly investigated before progress in BCI approaches can be achieved.

  10. The Human Factors and Ergonomics of P300-Based Brain-Computer Interfaces

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    J. Clark Powers

    2015-08-01

    Full Text Available Individuals with severe neuromuscular impairments face many challenges in communication and manipulation of the environment. Brain-computer interfaces (BCIs show promise in presenting real-world applications that can provide such individuals with the means to interact with the world using only brain waves. Although there has been a growing body of research in recent years, much relates only to technology, and not to technology in use—i.e., real-world assistive technology employed by users. This review examined the literature to highlight studies that implicate the human factors and ergonomics (HFE of P300-based BCIs. We assessed 21 studies on three topics to speak directly to improving the HFE of these systems: (1 alternative signal evocation methods within the oddball paradigm; (2 environmental interventions to improve user performance and satisfaction within the constraints of current BCI systems; and (3 measures and methods of measuring user acceptance. We found that HFE is central to the performance of P300-based BCI systems, although researchers do not often make explicit this connection. Incorporation of measures of user acceptance and rigorous usability evaluations, increased engagement of disabled users as test participants, and greater realism in testing will help progress the advancement of P300-based BCI systems in assistive applications.

  11. Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces

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    Lal Thomas Navin

    2005-01-01

    Full Text Available Most EEG-based brain-computer interface (BCI paradigms come along with specific electrode positions, for example, for a visual-based BCI, electrode positions close to the primary visual cortex are used. For new BCI paradigms it is usually not known where task relevant activity can be measured from the scalp. For individual subjects, Lal et al. in 2004 showed that recording positions can be found without the use of prior knowledge about the paradigm used. However it remains unclear to what extent their method of recursive channel elimination (RCE can be generalized across subjects. In this paper we transfer channel rankings from a group of subjects to a new subject. For motor imagery tasks the results are promising, although cross-subject channel selection does not quite achieve the performance of channel selection on data of single subjects. Although the RCE method was not provided with prior knowledge about the mental task, channels that are well known to be important (from a physiological point of view were consistently selected whereas task-irrelevant channels were reliably disregarded.

  12. Challenges in clinical applications of brain computer interfaces in individuals with spinal cord injury

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    Rüdiger eRupp

    2014-09-01

    Full Text Available Brain computer interfaces (BCIs are devices that measure brain activities and translate them into control signals used for a variety of applications. Among them are systems for communication, environmental control, neuroprostheses, exoskeletons or restorative therapies. Over the last years the technology of BCIs has reached a level of matureness allowing them to be used not only in research experiments supervised by scientists, but also in clinical routine with patients with neurological impairments supervised by clinical personnel or caregivers. However, clinicians and patients face many challenges in the application of BCIs. This particularly applies to high spinal cord injured patients, in whom artificial ventilation, autonomic dysfunctions, neuropathic pain or the inability to achieve a sufficient level of control during a short-term training may limit the successful use of a BCI. Additionally, spasmolytic medication and the acute stress reaction with associated episodes of depression may have a negative influence on the modulation of brain waves and therefore the ability to concentrate over an extended period of time. Although BCIs seem to be a promising assistive technology for individuals with high spinal cord injury systematic investigations are highly needed to obtain realistic estimates of the percentage of users that for any reason may not be able to operate a BCI in a clinical setting.

  13. Adaptive Stacked Generalization for Multiclass Motor Imagery-Based Brain Computer Interfaces.

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    Nicolas-Alonso, Luis F; Corralejo, Rebeca; Gomez-Pilar, Javier; Álvarez, Daniel; Hornero, Roberto

    2015-07-01

    Practical motor imagery-based brain computer interface (MI-BCI) applications are limited by the difficult to decode brain signals in a reliable way. In this paper, we propose a processing framework to address non-stationarity, as well as handle spectral, temporal, and spatial characteristics associated with execution of motor tasks. Stacked generalization is used to exploit the power of classifier ensembles for combining information coming from multiple sources and reducing the existing uncertainty in EEG signals. The outputs of several regularized linear discriminant analysis (RLDA) models are combined to account for temporal, spatial, and spectral information. The resultant algorithm is called stacked RLDA (SRLDA). Additionally, an adaptive processing stage is introduced before classification to reduce the harmful effect of intersession non-stationarity. The benefits of the proposed method are evaluated on the BCI Competition IV dataset 2a. We demonstrate its effectiveness in binary and multiclass settings with four different motor imagery tasks: left-hand, right-hand, both feet, and tongue movements. The results show that adaptive SRLDA outperforms the winner of the competition and other approaches tested on this multiclass dataset.

  14. Stimulus Specificity of Brain-Computer Interfaces Based on Code Modulation Visual Evoked Potentials.

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    Qingguo Wei

    Full Text Available A brain-computer interface (BCI based on code modulated visual evoked potentials (c-VEP is among the fastest BCIs that have ever been reported, but it has not yet been given a thorough study. In this study, a pseudorandom binary M sequence and its time lag sequences are utilized for modulation of different stimuli and template matching is adopted as the method for target recognition. Five experiments were devised to investigate the effect of stimulus specificity on target recognition and we made an effort to find the optimal stimulus parameters for size, color and proximity of the stimuli, length of modulation sequence and its lag between two adjacent stimuli. By changing the values of these parameters and measuring classification accuracy of the c-VEP BCI, an optimal value of each parameter can be attained. Experimental results of ten subjects showed that stimulus size of visual angle 3.8°, white, spatial proximity of visual angle 4.8° center to center apart, modulation sequence of length 63 bits and the lag of 4 bits between adjacent stimuli yield individually superior performance. These findings provide a basis for determining stimulus presentation of a high-performance c-VEP based BCI system.

  15. Challenges in clinical applications of brain computer interfaces in individuals with spinal cord injury.

    Science.gov (United States)

    Rupp, Rüdiger

    2014-01-01

    Brain computer interfaces (BCIs) are devices that measure brain activities and translate them into control signals used for a variety of applications. Among them are systems for communication, environmental control, neuroprostheses, exoskeletons, or restorative therapies. Over the last years the technology of BCIs has reached a level of matureness allowing them to be used not only in research experiments supervised by scientists, but also in clinical routine with patients with neurological impairments supervised by clinical personnel or caregivers. However, clinicians and patients face many challenges in the application of BCIs. This particularly applies to high spinal cord injured patients, in whom artificial ventilation, autonomic dysfunctions, neuropathic pain, or the inability to achieve a sufficient level of control during a short-term training may limit the successful use of a BCI. Additionally, spasmolytic medication and the acute stress reaction with associated episodes of depression may have a negative influence on the modulation of brain waves and therefore the ability to concentrate over an extended period of time. Although BCIs seem to be a promising assistive technology for individuals with high spinal cord injury systematic investigations are highly needed to obtain realistic estimates of the percentage of users that for any reason may not be able to operate a BCI in a clinical setting.

  16. sBCI-Headset—Wearable and Modular Device for Hybrid Brain-Computer Interface

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    Tatsiana Malechka

    2015-02-01

    Full Text Available Severely disabled people, like completely paralyzed persons either with tetraplegia or similar disabilities who cannot use their arms and hands, are often considered as a user group of Brain Computer Interfaces (BCI. In order to achieve high acceptance of the BCI by this user group and their supporters, the BCI system has to be integrated into their support infrastructure. Critical disadvantages of a BCI are the time consuming preparation of the user for the electroencephalography (EEG measurements and the low information transfer rate of EEG based BCI. These disadvantages become apparent if a BCI is used to control complex devices. In this paper, a hybrid BCI is described that enables research for a Human Machine Interface (HMI that is optimally adapted to requirements of the user and the tasks to be carried out. The solution is based on the integration of a Steady-state visual evoked potential (SSVEP-BCI, an Event-related (de-synchronization (ERD/ERS-BCI, an eye tracker, an environmental observation camera, and a new EEG head cap for wearing comfort and easy preparation. The design of the new fast multimodal BCI (called sBCI system is described and first test results, obtained in experiments with six healthy subjects, are presented. The sBCI concept may also become useful for healthy people in cases where a “hands-free” handling of devices is necessary.

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

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    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.

  18. [The P300-based brain-computer interface: presentation of the complex "flash + movement" stimuli].

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    Ganin, I P; Kaplan, A Ia

    2014-01-01

    The P300 based brain-computer interface requires the detection of P300 wave of brain event-related potentials. Most of its users learn the BCI control in several minutes and after the short classifier training they can type a text on the computer screen or assemble an image of separate fragments in simple BCI-based video games. Nevertheless, insufficient attractiveness for users and conservative stimuli organization in this BCI may restrict its integration into real information processes control. At the same time initial movement of object (motion-onset stimuli) may be an independent factor that induces P300 wave. In current work we checked the hypothesis that complex "flash + movement" stimuli together with drastic and compact stimuli organization on the computer screen may be much more attractive for user while operating in P300 BCI. In 20 subjects research we showed the effectiveness of our interface. Both accuracy and P300 amplitude were higher for flashing stimuli and complex "flash + movement" stimuli compared to motion-onset stimuli. N200 amplitude was maximal for flashing stimuli, while for "flash + movement" stimuli and motion-onset stimuli it was only a half of it. Similar BCI with complex stimuli may be embedded into compact control systems requiring high level of user attention under impact of negative external effects obstructing the BCI control.

  19. A competitive brain computer interface: multi-person car racing system.

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    Li, Junhua; Liu, Ye; Lu, Zhen; Zhang, Liqing

    2013-01-01

    Brain computer interface (BCI) technique is successfully utilized to bridge the interruption between brain and peripheral nerves and muscles, and to establish a new pathway making brain directly output information (or command). Up to now, a majority of BCI systems are developed to restore communication ability or movement functionality for people with severe disabilities, especially for paralyzed patients. To our best knowledge, other researchers haven't developed a multi-person BCI with competitive mode. Therefore, in this paper, we introduced a multi-person car racing system, which allows more than one person to play game at the same time and they can compete with each other for the aim of first reaching destination. The reason of development of car racing system has two aspects. At one hand, we introduced BCI to entertainment industry and provided a prototype for entertainment. At the other hand, we proposed a competitive mode for BCI. According to practical evaluation, the results demonstrated that our proposed system achieved a good performance. PMID:24110159

  20. Hybrid Brain-Computer Interface (BCI) based on the EEG and EOG signals.

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    Jiang, Jun; Zhou, Zongtan; Yin, Erwei; Yu, Yang; Hu, Dewen

    2014-01-01

    Recently, the integration of different electrophysiological signals into an electroencephalogram (EEG) has become an effective approach to improve the practicality of brain-computer interface (BCI) systems, referred to as hybrid BCIs. In this paper, a hybrid BCI was designed by combining an EEG with electrocardiograph (EOG) signals and tested using a target selection experiment. Gaze direction from the EOG and the event-related (de)synchronization (ERD/ERS) induced by motor imagery from the EEG were simultaneously detected as the output of the BCI system. The target selection mechanism was based on the synthesis of the gaze direction and ERD activity. When an ERD activity was detected, the target corresponding to the gaze direction was selected; without ERD activity, no target was selected, even when a subjects gaze was directed at the target. With this mechanism, the operation of the BCI system is more flexible and voluntary. The accuracy and completion time of the target selection tasks during the online testing were 89.3% and 2.4 seconds, respectively. These results show the feasibility and practicality of this hybrid BCI system, which can potentially be used in the rehabilitation of disabled individuals. PMID:25226998

  1. EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme.

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    Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

    2016-01-01

    Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI. PMID:26880873

  2. Multi-channel linear descriptors for event-related EEG collected in brain computer interface

    Science.gov (United States)

    Pei, Xiao-mei; Zheng, Chong-xun; Xu, Jin; Bin, Guang-yu; Wang, Hong-wu

    2006-03-01

    By three multi-channel linear descriptors, i.e. spatial complexity (Ω), field power (Σ) and frequency of field changes (Φ), event-related EEG data within 8-30 Hz were investigated during imagination of left or right hand movement. Studies on the event-related EEG data indicate that a two-channel version of Ω, Σ and Φ could reflect the antagonistic ERD/ERS patterns over contralateral and ipsilateral areas and also characterize different phases of the changing brain states in the event-related paradigm. Based on the selective two-channel linear descriptors, the left and right hand motor imagery tasks are classified to obtain satisfactory results, which testify the validity of the three linear descriptors Ω, Σ and Φ for characterizing event-related EEG. The preliminary results show that Ω, Σ together with Φ have good separability for left and right hand motor imagery tasks, which could be considered for classification of two classes of EEG patterns in the application of brain computer interfaces.

  3. The Use of a Brain Computer Interface Remote Control to Navigate a Recreational Device

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    Shih Chung Chen

    2013-01-01

    Full Text Available People suffering from paralysis caused by serious neural disorder or spinal cord injury also need to be given a means of recreation other than general living aids. Although there have been a proliferation of brain computer interface (BCI applications, developments for recreational activities are scarcely seen. The objective of this study is to develop a BCI-based remote control integrated with commercial devices such as the remote controlled Air Swimmer. The brain is visually stimulated using boxes flickering at preprogrammed frequencies to activate a brain response. After acquiring and processing these brain signals, the frequency of the resulting peak, which corresponds to the user’s selection, is determined by a decision model. Consequently, a command signal is sent from the computer to the wireless remote controller via a data acquisition (DAQ module. A command selection training (CST and simulated path test (SPT were conducted by 12 subjects using the BCI control system and the experimental results showed a recognition accuracy rate of 89.51% and 92.31% for the CST and SPT, respectively. The fastest information transfer rate demonstrated a response of 105 bits/min and 41.79 bits/min for the CST and SPT, respectively. The BCI system was proven to be able to provide a fast and accurate response for a remote controller application.

  4. Convolutional neural networks for P300 detection with application to brain-computer interfaces.

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    Cecotti, Hubert; Gräser, Axel

    2011-03-01

    A Brain-Computer Interface (BCI) is a specific type of human-computer interface that enables the direct communication between human and computers by analyzing brain measurements. Oddball paradigms are used in BCI to generate event-related potentials (ERPs), like the P300 wave, on targets selected by the user. A P300 speller is based on this principle, where the detection of P300 waves allows the user to write characters. The P300 speller is composed of two classification problems. The first classification is to detect the presence of a P300 in the electroencephalogram (EEG). The second one corresponds to the combination of different P300 responses for determining the right character to spell. A new method for the detection of P300 waves is presented. This model is based on a convolutional neural network (CNN). The topology of the network is adapted to the detection of P300 waves in the time domain. Seven classifiers based on the CNN are proposed: four single classifiers with different features set and three multiclassifiers. These models are tested and compared on the Data set II of the third BCI competition. The best result is obtained with a multiclassifier solution with a recognition rate of 95.5 percent, without channel selection before the classification. The proposed approach provides also a new way for analyzing brain activities due to the receptive field of the CNN models.

  5. Overlapped partitioning for ensemble classifiers of P300-based brain-computer interfaces.

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    Onishi, Akinari; Natsume, Kiyohisa

    2014-01-01

    A P300-based brain-computer interface (BCI) enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300). In this study, we evaluated ensemble linear discriminant analysis (LDA) classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA), or none. The results show that an ensemble stepwise LDA (SWLDA) classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance.

  6. Overlapped partitioning for ensemble classifiers of P300-based brain-computer interfaces.

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    Akinari Onishi

    Full Text Available A P300-based brain-computer interface (BCI enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300. In this study, we evaluated ensemble linear discriminant analysis (LDA classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA, or none. The results show that an ensemble stepwise LDA (SWLDA classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance.

  7. Non-invasive Brain-Computer Interfaces for Semi-autonomous Assistive Devices

    Science.gov (United States)

    Graimann, Bernhard; Allison, Brendan; Mandel, Christian; Lüth, Thorsten; Valbuena, Diana; Gräser, Axel

    A brain-computer interface (BCI) transforms brain activity into commands that can control computers and other technologies. Because brain signals recorded non-invasively from the scalp are difficult to interpret, robust signal processing methods have to be applied. Although state-of-the-art signal processing methods are used in BCI research, the output of a BCI is still unreliable, and the information transfer rates are very small compared with conventional human interaction interfaces. Therefore, BCI applications have to compensate for the unreliability and low information content of the BCI output. Controlling a wheelchair or a robotic arm would be slow, frustrating, or even dangerous if it solely relied on BCI output. Intelligent devices, however, such as a wheelchair that can automatically avoid collisions and dangerous situations or a service robot that can autonomously conduct goal-directed tasks and independently detect and resolve safety issues, are much more suitable for being controlled by an "unreliable" control signal like that provided by a BCI.

  8. Prediction of motor imagery based brain computer interface performance using a reaction time test.

    Science.gov (United States)

    Darvishi, Sam; Abbott, Derek; Baumert, Mathias

    2015-08-01

    Brain computer interfaces (BCIs) enable human brains to interact directly with machines. Motor imagery based BCI (MI-BCI) encodes the motor intentions of human agents and provides feedback accordingly. However, 15-30% of people are not able to perform vivid motor imagery. To save time and monetary resources, a number of predictors have been proposed to screen for users with low BCI aptitude. While the proposed predictors provide some level of correlation with MI-BCI performance, simple, objective and accurate predictors are currently not available. Thus, in this study we have examined the utility of a simple reaction time (SRT) test for predicting MI-BCI performance. We enrolled 10 subjects and measured their motor imagery performance with either visual or proprioceptive feedback. Their reaction time was also measured using a SRT test. The results show a significant negative correlation (r ≈ -0.67) between SRT and MI-BCI performance. Therefore SRT may be used as a simple and reliable predictor of MI-BCI performance. PMID:26736893

  9. Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface

    Science.gov (United States)

    Waytowich, Nicholas R.; Lawhern, Vernon J.; Bohannon, Addison W.; Ball, Kenneth R.; Lance, Brent J.

    2016-01-01

    Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as outperform traditional within-subject calibration techniques when limited data is available. This method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.

  10. Semantic classical conditioning and brain-computer interface (BCI control: Encoding of affirmative and negative thinking

    Directory of Open Access Journals (Sweden)

    Carolin A. Ruf

    2013-03-01

    Full Text Available The aim of the study was to investigate conditioned electroencephalographic (EEG responses to factually correct and incorrect statements in order to enable binary communication by means of a brain-computer interface (BCI. In two experiments with healthy participants true and false statements (serving as conditioned stimuli, CSs were paired with two different tones which served as unconditioned stimuli (USs. The features of the USs were varied and tested for their effectiveness to elicit differentiable conditioned reactions (CRs. After acquisition of the CRs, these CRs to true and false statements were classified offline using a radial basis function kernel support vector machine. A mean single-trial classification accuracy of 50.5% was achieved for differentiating conditioned yes versus no thinking and mean accuracies of 65.4% for classification of yes and 68.8% for no thinking (both relative to baseline were found using the best US. Analysis of the area under the curve of the conditioned EEG responses revealed significant differences between conditioned yes and no answers. Even though improvements are necessary, these first results indicate that the semantic conditioning paradigm could be a useful basis for further research regarding BCI communication in patients with complete locked-in syndrome (CLIS.

  11. Improving the performance of brain-computer interface through meditation practicing.

    Science.gov (United States)

    Eskandari, Parvaneh; Erfanian, Abbas

    2008-01-01

    Cognitive tasks using motor imagery have been used for generating and controlling EEG activity in most brain-computer interface (BCI). Nevertheless, during the performance of a particular mental task, different factors such as concentration, attention, level of consciousness and the difficulty of the task, may be affecting the changes in the EEG activity. Accordingly, training the subject to consistently and reliably produce and control the changes in the EEG signals is a critical issue in developing a BCI system. In this work, we used meditation practice to enhance the mind controllability during the performance of a mental task in a BCI system. The mental states to be discriminated are the imaginative hand movement and the idle state. The experiments were conducted on two groups of subject, meditation group and control group. The time-frequency analysis of EEG signals for meditation practitioners showed an event-related desynchronization (ERD) of beta rhythm before imagination during resting state. In addition, a strong event-related synchronization (ERS) of beta rhythm was induced in frequency around 25 Hz during hand motor imagery. The results demonstrated that the meditation practice can improve the classification accuracy of EEG patterns. The average classification accuracy was 88.73% in the meditation group, while it was 70.28% in the control group. An accuracy as high as 98.0% was achieved in the meditation group.

  12. Manipulating attention via mindfulness induction improves P300-based brain-computer interface performance

    Science.gov (United States)

    Lakey, Chad E.; Berry, Daniel R.; Sellers, Eric W.

    2011-04-01

    In this study, we examined the effects of a short mindfulness meditation induction (MMI) on the performance of a P300-based brain-computer interface (BCI) task. We expected that MMI would harness present-moment attentional resources, resulting in two positive consequences for P300-based BCI use. Specifically, we believed that MMI would facilitate increases in task accuracy and promote the production of robust P300 amplitudes. Sixteen-channel electroencephalographic data were recorded from 18 subjects using a row/column speller task paradigm. Nine subjects participated in a 6 min MMI and an additional nine subjects served as a control group. Subjects were presented with a 6 × 6 matrix of alphanumeric characters on a computer monitor. Stimuli were flashed at a stimulus onset asynchrony (SOA) of 125 ms. Calibration data were collected on 21 items without providing feedback. These data were used to derive a stepwise linear discriminate analysis classifier that was applied to an additional 14 items to evaluate accuracy. Offline performance analyses revealed that MMI subjects were significantly more accurate than control subjects. Likewise, MMI subjects produced significantly larger P300 amplitudes than control subjects at Cz and PO7. The discussion focuses on the potential attentional benefits of MMI for P300-based BCI performance.

  13. Brain-computer interfaces: an overview of the hardware to record neural signals from the cortex.

    Science.gov (United States)

    Stieglitz, Thomas; Rubehn, Birthe; Henle, Christian; Kisban, Sebastian; Herwik, Stanislav; Ruther, Patrick; Schuettler, Martin

    2009-01-01

    Brain-computer interfaces (BCIs) record neural signals from cortical origin with the objective to control a user interface for communication purposes, a robotic artifact or artificial limb as actuator. One of the key components of such a neuroprosthetic system is the neuro-technical interface itself, the electrode array. In this chapter, different designs and manufacturing techniques will be compared and assessed with respect to scaling and assembling limitations. The overview includes electroencephalogram (EEG) electrodes and epicortical brain-machine interfaces to record local field potentials (LFPs) from the surface of the cortex as well as intracortical needle electrodes that are intended to record single-unit activity. Two exemplary complementary technologies for micromachining of polyimide-based arrays and laser manufacturing of silicone rubber are presented and discussed with respect to spatial resolution, scaling limitations, and system properties. Advanced silicon micromachining technologies have led to highly sophisticated intracortical electrode arrays for fundamental neuroscientific applications. In this chapter, major approaches from the USA and Europe will be introduced and compared concerning complexity, modularity, and reliability. An assessment of the different technological solutions comparable to a strength weaknesses opportunities, and threats (SWOT) analysis might serve as guidance to select the adequate electrode array configuration for each control paradigm and strategy to realize robust, fast, and reliable BCIs. PMID:19660664

  14. 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.

  15. Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation

    Science.gov (United States)

    Liu, Ju-Chi; Chou, Hung-Chyun; Chen, Chien-Hsiu; Lin, Yi-Tseng

    2016-01-01

    A high efficient time-shift correlation algorithm was proposed to deal with the peak time uncertainty of P300 evoked potential for a P300-based brain-computer interface (BCI). The time-shift correlation series data were collected as the input nodes of an artificial neural network (ANN), and the classification of four LED visual stimuli was selected as the output node. Two operating modes, including fast-recognition mode (FM) and accuracy-recognition mode (AM), were realized. The proposed BCI system was implemented on an embedded system for commanding an adult-size humanoid robot to evaluate the performance from investigating the ground truth trajectories of the humanoid robot. When the humanoid robot walked in a spacious area, the FM was used to control the robot with a higher information transfer rate (ITR). When the robot walked in a crowded area, the AM was used for high accuracy of recognition to reduce the risk of collision. The experimental results showed that, in 100 trials, the accuracy rate of FM was 87.8% and the average ITR was 52.73 bits/min. In addition, the accuracy rate was improved to 92% for the AM, and the average ITR decreased to 31.27 bits/min. due to strict recognition constraints.

  16. 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.

  17. Brain-computer interfaces in the completely locked-in state and chronic stroke.

    Science.gov (United States)

    Chaudhary, U; Birbaumer, N; Ramos-Murguialday, A

    2016-01-01

    Brain-computer interfaces (BCIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this chapter, we discuss the historical perspective of development of BCIs and the current advances of noninvasive BCIs for communication in patients with amyotrophic lateral sclerosis and for restoration of motor impairment after severe stroke. Distinct techniques have been explored to control a BCI in patient population especially electroencephalography (EEG) and more recently near-infrared spectroscopy (NIRS) because of their noninvasive nature and low cost. Previous studies demonstrated successful communication of patients with locked-in state (LIS) using EEG- and invasive electrocorticography-BCI and intracortical recordings when patients still showed residual eye control, but not with patients with complete LIS (ie, complete paralysis). Recently, a NIRS-BCI and classical conditioning procedure was introduced, allowing communication in patients in the complete locked-in state (CLIS). In severe chronic stroke without residual hand function first results indicate a possible superior motor rehabilitation to available treatment using BCI training. Here we present an overview of the available studies and recent results, which open new doors for communication, in the completely paralyzed and rehabilitation in severely affected stroke patients. We also reflect on and describe possible neuronal and learning mechanisms responsible for BCI control and perspective for future BMI research for communication in CLIS and stroke motor recovery. PMID:27590968

  18. 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.

  19. Gaze-independent brain-computer interfaces based on covert attention and feature attention

    Science.gov (United States)

    Treder, M. S.; Schmidt, N. M.; Blankertz, B.

    2011-10-01

    There is evidence that conventional visual brain-computer interfaces (BCIs) based on event-related potentials cannot be operated efficiently when eye movements are not allowed. To overcome this limitation, the aim of this study was to develop a visual speller that does not require eye movements. Three different variants of a two-stage visual speller based on covert spatial attention and non-spatial feature attention (i.e. attention to colour and form) were tested in an online experiment with 13 healthy participants. All participants achieved highly accurate BCI control. They could select one out of thirty symbols (chance level 3.3%) with mean accuracies of 88%-97% for the different spellers. The best results were obtained for a speller that was operated using non-spatial feature attention only. These results show that, using feature attention, it is possible to realize high-accuracy, fast-paced visual spellers that have a large vocabulary and are independent of eye gaze.

  20. A new (semantic) reflexive brain-computer interface: in search for a suitable classifier.

    Science.gov (United States)

    Furdea, A; Ruf, C A; Halder, S; De Massari, D; Bogdan, M; Rosenstiel, W; Matuz, T; Birbaumer, N

    2012-01-15

    The goal of the current study is to find a suitable classifier for electroencephalogram (EEG) data derived from a new learning paradigm which aims at communication in paralysis. A reflexive semantic classical (Pavlovian) conditioning paradigm is explored as an alternative to the operant learning paradigms, currently used in most brain-computer interfaces (BCIs). Comparable with a lie-detection experiment, subjects are presented with true and false statements. The EEG activity following true and false statements was classified with the aim to separate covert 'yes' from covert 'no' responses. Four classification algorithms are compared for classifying off-line data collected from a group of 14 healthy participants: (i) stepwise linear discriminant analysis (SWLDA), (ii) shrinkage linear discriminant analysis (SLDA), (iii) linear support vector machine (LIN-SVM) and (iv) radial basis function kernel support vector machine (RBF-SVM). The results indicate that all classifiers perform at chance level when separating conditioned 'yes' from conditioned 'no' responses. However, single conditioned reactions could be successfully classified on a single-trial basis (single conditioned reaction against a baseline interval). All of the four investigated classification methods achieve comparable performance, however results with RBF-SVM show the highest single-trial classification accuracy of 68.8%. The results suggest that the proposed paradigm may allow affirmative and negative (disapproving negative) communication in a BCI experiment.

  1. Using a cVEP-Based Brain-Computer Interface to Control a Virtual Agent.

    Science.gov (United States)

    Riechmann, Hannes; Finke, Andrea; Ritter, Helge

    2016-06-01

    Brain-computer interfaces provide a means for controlling a device by brain activity alone. One major drawback of noninvasive BCIs is their low information transfer rate, obstructing a wider deployment outside the lab. BCIs based on codebook visually evoked potentials (cVEP) outperform all other state-of-the-art systems in that regard. Previous work investigated cVEPs for spelling applications. We present the first cVEP-based BCI for use in real-world settings to accomplish everyday tasks such as navigation or action selection. To this end, we developed and evaluated a cVEP-based on-line BCI that controls a virtual agent in a simulated, but realistic, 3-D kitchen scenario. We show that cVEPs can be reliably triggered with stimuli in less restricted presentation schemes, such as on dynamic, changing backgrounds. We introduce a novel, dynamic repetition algorithm that allows for optimizing the balance between accuracy and speed individually for each user. Using these novel mechanisms in a 12-command cVEP-BCI in the 3-D simulation results in ITRs of 50 bits/min on average and 68 bits/min maximum. Thus, this work supports the notion of cVEP-BCIs as a particular fast and robust approach suitable for real-world use. PMID:26469340

  2. Goal selection versus process control while learning to use a brain-computer interface

    Science.gov (United States)

    Royer, Audrey S.; Rose, Minn L.; He, Bin

    2011-06-01

    A brain-computer interface (BCI) can be used to accomplish a task without requiring motor output. Two major control strategies used by BCIs during task completion are process control and goal selection. In process control, the user exerts continuous control and independently executes the given task. In goal selection, the user communicates their goal to the BCI and then receives assistance executing the task. A previous study has shown that goal selection is more accurate and faster in use. An unanswered question is, which control strategy is easier to learn? This study directly compares goal selection and process control while learning to use a sensorimotor rhythm-based BCI. Twenty young healthy human subjects were randomly assigned either to a goal selection or a process control-based paradigm for eight sessions. At the end of the study, the best user from each paradigm completed two additional sessions using all paradigms randomly mixed. The results of this study were that goal selection required a shorter training period for increased speed, accuracy, and information transfer over process control. These results held for the best subjects as well as in the general subject population. The demonstrated characteristics of goal selection make it a promising option to increase the utility of BCIs intended for both disabled and able-bodied users.

  3. A telepresence mobile robot controlled with a noninvasive brain-computer interface.

    Science.gov (United States)

    Escolano, Carlos; Antelis, Javier Mauricio; Minguez, Javier

    2012-06-01

    This paper reports an electroencephalogram-based brain-actuated telepresence system to provide a user with presence in remote environments through a mobile robot, with access to the Internet. This system relies on a P300-based brain-computer interface (BCI) and a mobile robot with autonomous navigation and camera orientation capabilities. The shared-control strategy is built by the BCI decoding of task-related orders (selection of visible target destinations or exploration areas), which can be autonomously executed by the robot. The system was evaluated using five healthy participants in two consecutive steps: 1) screening and training of participants and 2) preestablished navigation and visual exploration telepresence tasks. On the basis of the results, the following evaluation studies are reported: 1) technical evaluation of the device and its main functionalities and 2) the users' behavior study. The overall result was that all participants were able to complete the designed tasks, reporting no failures, which shows the robustness of the system and its feasibility to solve tasks in real settings where joint navigation and visual exploration were needed. Furthermore, the participants showed great adaptation to the telepresence system. PMID:22180512

  4. A cognitive brain-computer interface for patients with amyotrophic lateral sclerosis.

    Science.gov (United States)

    Hohmann, M R; Fomina, T; Jayaram, V; Widmann, N; Förster, C; Just, J; Synofzik, M; Schölkopf, B; Schöls, L; Grosse-Wentrup, M

    2016-01-01

    Brain-computer interfaces (BCIs) are often based on the control of sensorimotor processes, yet sensorimotor processes are impaired in patients suffering from amyotrophic lateral sclerosis (ALS). We devised a new paradigm that targets higher-level cognitive processes to transmit information from the user to the BCI. We instructed five ALS patients and twelve healthy subjects to either activate self-referential memories or to focus on a process without mnemonic content while recording a high-density electroencephalogram (EEG). Both tasks are designed to modulate activity in the default mode network (DMN) without involving sensorimotor pathways. We find that the two tasks can be distinguished after only one experimental session from the average of the combined bandpower modulations in the theta- (4-7Hz) and alpha-range (8-13Hz), with an average accuracy of 62.5% and 60.8% for healthy subjects and ALS patients, respectively. The spatial weights of the decoding algorithm show a preference for the parietal area, consistent with modulation of neural activity in primary nodes of the DMN. PMID:27590971

  5. Classifying real and imaginary finger press tasks on a P300-based brain-computer interface.

    Science.gov (United States)

    Zhang, Jicai; Chen, Weidong; Gu, Yanlei; Wu, Bian; Qi, Yu; Zheng, Xiaoxiang

    2011-01-01

    Brain computer interfaces based on P300 and sensory-motor rhythms are widely studied and recent advances show some interest in the combination of the two. In this paper, typical P300 paradigm is modified by adding animation guide of the finger press as a stimulus and by using different response strategies (silent counting and actual/imaginary left or right index finger press following the animation). Both P300 potentials and sensory-motor rhythms are directly exploited and discussed. Classification results showed that even under very demanding conditions, which was, 200 ms inter-stimulus interval of the P300 stimuli and actual/imaginary finger press once per 1.6s, the paradigm can evoke both P300 potentials and sensory-motor rhythms simultaneously. Actual finger press increased single trial P300 selection accuracy of different subjects by 5-29.5% compared with silent counting; imaginary finger press did not increase the P300 selection accuracy apparently for most subjects except the two who were very poor at counting task. This showed by using different interface design and adopting certain mental response strategies, the 'BCI illiteracy' may be cured. Also imaginary task had good performance of left versus right classification (with the best subject reached 81.1% of accuracy), which is an additional information that can be used to improve system performance. PMID:22255792

  6. Asynchronous P300 classification in a reactive brain-computer interface during an outlier detection task

    Science.gov (United States)

    Krumpe, Tanja; Walter, Carina; Rosenstiel, Wolfgang; Spüler, Martin

    2016-08-01

    Objective. In this study, the feasibility of detecting a P300 via an asynchronous classification mode in a reactive EEG-based brain-computer interface (BCI) was evaluated. The P300 is one of the most popular BCI control signals and therefore used in many applications, mostly for active communication purposes (e.g. P300 speller). As the majority of all systems work with a stimulus-locked mode of classification (synchronous), the field of applications is limited. A new approach needs to be applied in a setting in which a stimulus-locked classification cannot be used due to the fact that the presented stimuli cannot be controlled or predicted by the system. Approach. A continuous observation task requiring the detection of outliers was implemented to test such an approach. The study was divided into an offline and an online part. Main results. Both parts of the study revealed that an asynchronous detection of the P300 can successfully be used to detect single events with high specificity. It also revealed that no significant difference in performance was found between the synchronous and the asynchronous approach. Significance. The results encourage the use of an asynchronous classification approach in suitable applications without a potential loss in performance.

  7. Dose-response relationships using brain-computer interface technology impact stroke rehabilitation.

    Science.gov (United States)

    Young, Brittany M; Nigogosyan, Zack; Walton, Léo M; Remsik, Alexander; Song, Jie; Nair, Veena A; Tyler, Mitchell E; Edwards, Dorothy F; Caldera, Kristin; Sattin, Justin A; Williams, Justin C; Prabhakaran, Vivek

    2015-01-01

    Brain-computer interfaces (BCIs) are an emerging novel technology for stroke rehabilitation. Little is known about how dose-response relationships for BCI therapies affect brain and behavior changes. We report preliminary results on stroke patients (n = 16, 11 M) with persistent upper extremity motor impairment who received therapy using a BCI system with functional electrical stimulation of the hand and tongue stimulation. We collected MRI scans and behavioral data using the Action Research Arm Test (ARAT), 9-Hole Peg Test (9-HPT), and Stroke Impact Scale (SIS) before, during, and after the therapy period. Using anatomical and functional MRI, we computed Laterality Index (LI) for brain activity in the motor network during impaired hand finger tapping. Changes from baseline LI and behavioral scores were assessed for relationships with dose, intensity, and frequency of BCI therapy. We found that gains in SIS Strength were directly responsive to BCI therapy: therapy dose and intensity correlated positively with increased SIS Strength (p ≤ 0.05), although no direct relationships were identified with ARAT or 9-HPT scores. We found behavioral measures that were not directly sensitive to differences in BCI therapy administration but were associated with concurrent brain changes correlated with BCI therapy administration parameters: therapy dose and intensity showed significant (p ≤ 0.05) or trending (0.05 stroke rehabilitation, therapy frequency may be less important than dose and intensity.

  8. Design of a Mobile Brain Computer Interface-Based Smart Multimedia Controller

    Directory of Open Access Journals (Sweden)

    Kevin C. Tseng

    2015-03-01

    Full Text Available Music is a way of expressing our feelings and emotions. Suitable music can positively affect people. However, current multimedia control methods, such as manual selection or automatic random mechanisms, which are now applied broadly in MP3 and CD players, cannot adaptively select suitable music according to the user’s physiological state. In this study, a brain computer interface-based smart multimedia controller was proposed to select music in different situations according to the user’s physiological state. Here, a commercial mobile tablet was used as the multimedia platform, and a wireless multi-channel electroencephalograph (EEG acquisition module was designed for real-time EEG monitoring. A smart multimedia control program built in the multimedia platform was developed to analyze the user’s EEG feature and select music according his/her state. The relationship between the user’s state and music sorted by listener’s preference was also examined in this study. The experimental results show that real-time music biofeedback according a user’s EEG feature may positively improve the user’s attention state.

  9. Non-target adjacent stimuli classification improves performance of classical ERP-based brain computer interface

    Science.gov (United States)

    Ceballos, G. A.; Hernández, L. F.

    2015-04-01

    Objective. The classical ERP-based speller, or P300 Speller, is one of the most commonly used paradigms in the field of Brain Computer Interfaces (BCI). Several alterations to the visual stimuli presentation system have been developed to avoid unfavorable effects elicited by adjacent stimuli. However, there has been little, if any, regard to useful information contained in responses to adjacent stimuli about spatial location of target symbols. This paper aims to demonstrate that combining the classification of non-target adjacent stimuli with standard classification (target versus non-target) significantly improves classical ERP-based speller efficiency. Approach. Four SWLDA classifiers were trained and combined with the standard classifier: the lower row, upper row, right column and left column classifiers. This new feature extraction procedure and the classification method were carried out on three open databases: the UAM P300 database (Universidad Autonoma Metropolitana, Mexico), BCI competition II (dataset IIb) and BCI competition III (dataset II). Main results. The inclusion of the classification of non-target adjacent stimuli improves target classification in the classical row/column paradigm. A gain in mean single trial classification of 9.6% and an overall improvement of 25% in simulated spelling speed was achieved. Significance. We have provided further evidence that the ERPs produced by adjacent stimuli present discriminable features, which could provide additional information about the spatial location of intended symbols. This work promotes the searching of information on the peripheral stimulation responses to improve the performance of emerging visual ERP-based spellers.

  10. Adaptive hybrid brain-computer interaction: ask a trainer for assistance!

    Science.gov (United States)

    Müller-Putz, Gernot R; Steyrl, David; Faller, Josef

    2014-01-01

    In applying mental imagery brain-computer interfaces (BCIs) to end users, training is a key part for novice users to get control. In general learning situations, it is an established concept that a trainer assists a trainee to improve his/her aptitude in certain skills. In this work, we want to evaluate whether we can apply this concept in the context of event-related desynchronization (ERD) based, adaptive, hybrid BCIs. Hence, in a first session we merged the features of a high aptitude BCI user, a trainer, and a novice user, the trainee, in a closed-loop BCI feedback task and automatically adapted the classifier over time. In a second session the trainees operated the system unassisted. Twelve healthy participants ran through this protocol. Along with the trainer, the trainees achieved a very high overall peak accuracy of 95.3 %. In the second session, where users operated the BCI unassisted, they still achieved a high overall peak accuracy of 83.6%. Ten of twelve first time BCI users successfully achieved significantly better than chance accuracy. Concluding, we can say that this trainer-trainee approach is very promising. Future research should investigate, whether this approach is superior to conventional training approaches. This trainer-trainee concept could have potential for future application of BCIs to end users. PMID:25570252

  11. Using EEG/MEG Data of Cognitive Processes in Brain-Computer Interfaces

    International Nuclear Information System (INIS)

    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

  12. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation.

    Science.gov (United States)

    Bauer, Robert; Gharabaghi, Alireza

    2015-01-01

    Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting. PMID:25729347

  13. An independent brain-computer interface using covert non-spatial visual selective attention

    Science.gov (United States)

    Zhang, Dan; Maye, Alexander; Gao, Xiaorong; Hong, Bo; Engel, Andreas K.; Gao, Shangkai

    2010-02-01

    In this paper, a novel independent brain-computer interface (BCI) system based on covert non-spatial visual selective attention of two superimposed illusory surfaces is described. Perception of two superimposed surfaces was induced by two sets of dots with different colors rotating in opposite directions. The surfaces flickered at different frequencies and elicited distinguishable steady-state visual evoked potentials (SSVEPs) over parietal and occipital areas of the brain. By selectively attending to one of the two surfaces, the SSVEP amplitude at the corresponding frequency was enhanced. An online BCI system utilizing the attentional modulation of SSVEP was implemented and a 3-day online training program with healthy subjects was carried out. The study was conducted with Chinese subjects at Tsinghua University, and German subjects at University Medical Center Hamburg-Eppendorf (UKE) using identical stimulation software and equivalent technical setup. A general improvement of control accuracy with training was observed in 8 out of 18 subjects. An averaged online classification accuracy of 72.6 ± 16.1% was achieved on the last training day. The system renders SSVEP-based BCI paradigms possible for paralyzed patients with substantial head or ocular motor impairments by employing covert attention shifts instead of changing gaze direction.

  14. A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface.

    Science.gov (United States)

    Zhou, Bangyan; Wu, Xiaopei; Lv, Zhao; Zhang, Lei; Guo, Xiaojin

    2016-01-01

    Independent component analysis (ICA) as a promising spatial filtering method can separate motor-related independent components (MRICs) from the multichannel electroencephalogram (EEG) signals. However, the unpredictable burst interferences may significantly degrade the performance of ICA-based brain-computer interface (BCI) system. In this study, we proposed a new algorithm frame to address this issue by combining the single-trial-based ICA filter with zero-training classifier. We developed a two-round data selection method to identify automatically the badly corrupted EEG trials in the training set. The "high quality" training trials were utilized to optimize the ICA filter. In addition, we proposed an accuracy-matrix method to locate the artifact data segments within a single trial and investigated which types of artifacts can influence the performance of the ICA-based MIBCIs. Twenty-six EEG datasets of three-class motor imagery were used to validate the proposed methods, and the classification accuracies were compared with that obtained by frequently used common spatial pattern (CSP) spatial filtering algorithm. The experimental results demonstrated that the proposed optimizing strategy could effectively improve the stability, practicality and classification performance of ICA-based MIBCI. The study revealed that rational use of ICA method may be crucial in building a practical ICA-based MIBCI system. PMID:27631789

  15. Enhancing performances of SSVEP-based brain-computer interfaces via exploiting inter-subject information

    Science.gov (United States)

    Yuan, Peng; Chen, Xiaogang; Wang, Yijun; Gao, Xiaorong; Gao, Shangkai

    2015-08-01

    Objective. A new training-free framework was proposed for target detection in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) using joint frequency-phase coding. Approach. The key idea is to transfer SSVEP templates from the existing subjects to a new subject to enhance the detection of SSVEPs. Under this framework, transfer template-based canonical correlation analysis (tt-CCA) methods were developed for single-channel and multi-channel conditions respectively. In addition, an online transfer template-based CCA (ott-CCA) method was proposed to update EEG templates by online adaptation. Main results. The efficiency of the proposed framework was proved with a simulated BCI experiment. Compared with the standard CCA method, tt-CCA obtained an 18.78% increase of accuracy with a data length of 1.5 s. A simulated test of ott-CCA further received an accuracy increase of 2.99%. Significance. The proposed simple yet efficient framework significantly facilitates the use of SSVEP BCIs using joint frequency-phase coding. This study also sheds light on the benefits from exploring and exploiting inter-subject information to the electroencephalogram (EEG)-based BCIs.

  16. A Gaze Independent Brain-Computer Interface Based on Visual Stimulation through Closed Eyelids

    Science.gov (United States)

    Hwang, Han-Jeong; Ferreria, Valeria Y.; Ulrich, Daniel; Kilic, Tayfun; Chatziliadis, Xenofon; Blankertz, Benjamin; Treder, Matthias

    2015-10-01

    A classical brain-computer interface (BCI) based on visual event-related potentials (ERPs) is of limited application value for paralyzed patients with severe oculomotor impairments. In this study, we introduce a novel gaze independent BCI paradigm that can be potentially used for such end-users because visual stimuli are administered on closed eyelids. The paradigm involved verbally presented questions with 3 possible answers. Online BCI experiments were conducted with twelve healthy subjects, where they selected one option by attending to one of three different visual stimuli. It was confirmed that typical cognitive ERPs can be evidently modulated by the attention of a target stimulus in eyes-closed and gaze independent condition, and further classified with high accuracy during online operation (74.58% ± 17.85 s.d.; chance level 33.33%), demonstrating the effectiveness of the proposed novel visual ERP paradigm. Also, stimulus-specific eye movements observed during stimulation were verified as reflex responses to light stimuli, and they did not contribute to classification. To the best of our knowledge, this study is the first to show the possibility of using a gaze independent visual ERP paradigm in an eyes-closed condition, thereby providing another communication option for severely locked-in patients suffering from complex ocular dysfunctions.

  17. Evaluation of Methods for Estimating Fractal Dimension in Motor Imagery-Based Brain Computer Interface

    Directory of Open Access Journals (Sweden)

    Chu Kiong Loo

    2011-01-01

    Full Text Available A brain computer interface BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction, and classification operations. Feature extraction is crucial, as it has a substantial effect on the classification accuracy and speed. While fractal dimension has been successfully used in various domains to characterize data exhibiting fractal properties, its usage in motor imagery-based BCI has been more recent. In this study, commonly used fractal dimension estimation methods to characterize time series Katz's method, Higuchi's method, rescaled range method, and Renyi's entropy were evaluated for feature extraction in motor imagery-based BCI by conducting offline analyses of a two class motor imagery dataset. Different classifiers fuzzy k-nearest neighbours FKNN, support vector machine, and linear discriminant analysis were tested in combination with these methods to determine the methodology with the best performance. This methodology was then modified by implementing the time-dependent fractal dimension TDFD, differential fractal dimension, and differential signals methods to determine if the results could be further improved. Katz's method with FKNN resulted in the highest classification accuracy of 85%, and further improvements by 3% were achieved by implementing the TDFD method.

  18. Playing checkers with your mind: an interactive multiplayer hardware game platform for brain-computer interfaces.

    Science.gov (United States)

    Akhtar, Aadeel; Norton, James J S; Kasraie, Mahsa; Bretl, Timothy

    2014-01-01

    In this paper we describe a multiplayer brain-computer interface (BCI) based on the classic game of checkers using steady-state visually evoked potentials (SSVEPs). Previous research in BCI gaming focuses mainly on the production of software-based games using a computer screen--few hardware-based BCI games using a physical board have been developed. Hardware-based games can present a unique set of challenges when compared to software-based games. Depending on where the user is sitting, some stimuli might be farther away from the player, at a steeper viewing angle, conflated with competing stimuli, or occluded by physical barriers. In our game, we light squares on a checkerboard with flickering LEDs to elicit SSVEP responses in the subjects. When a subject attends to a particular square, the resulting SSVEPs are classified and a robot arm moves the selected piece. In a set of pilot experiments we investigated the ability of two subjects to use the SSVEP-based hardware game platform, and assessed how interstimulus distance, interstimulus angle, distance between target stimulus and subject, number of competing stimuli, and visual occlusions of the stimuli influence classification accuracy.

  19. 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. PMID:26795195

  20. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation

    Directory of Open Access Journals (Sweden)

    Robert eBauer

    2015-02-01

    Full Text Available Restorative brain-computer interfaces (BCI are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation.In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting.

  1. Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation.

    Science.gov (United States)

    Liu, Ju-Chi; Chou, Hung-Chyun; Chen, Chien-Hsiu; Lin, Yi-Tseng; Kuo, Chung-Hsien

    2016-01-01

    A high efficient time-shift correlation algorithm was proposed to deal with the peak time uncertainty of P300 evoked potential for a P300-based brain-computer interface (BCI). The time-shift correlation series data were collected as the input nodes of an artificial neural network (ANN), and the classification of four LED visual stimuli was selected as the output node. Two operating modes, including fast-recognition mode (FM) and accuracy-recognition mode (AM), were realized. The proposed BCI system was implemented on an embedded system for commanding an adult-size humanoid robot to evaluate the performance from investigating the ground truth trajectories of the humanoid robot. When the humanoid robot walked in a spacious area, the FM was used to control the robot with a higher information transfer rate (ITR). When the robot walked in a crowded area, the AM was used for high accuracy of recognition to reduce the risk of collision. The experimental results showed that, in 100 trials, the accuracy rate of FM was 87.8% and the average ITR was 52.73 bits/min. In addition, the accuracy rate was improved to 92% for the AM, and the average ITR decreased to 31.27 bits/min. due to strict recognition constraints.

  2. Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation

    Directory of Open Access Journals (Sweden)

    Ju-Chi Liu

    2016-01-01

    Full Text Available A high efficient time-shift correlation algorithm was proposed to deal with the peak time uncertainty of P300 evoked potential for a P300-based brain-computer interface (BCI. The time-shift correlation series data were collected as the input nodes of an artificial neural network (ANN, and the classification of four LED visual stimuli was selected as the output node. Two operating modes, including fast-recognition mode (FM and accuracy-recognition mode (AM, were realized. The proposed BCI system was implemented on an embedded system for commanding an adult-size humanoid robot to evaluate the performance from investigating the ground truth trajectories of the humanoid robot. When the humanoid robot walked in a spacious area, the FM was used to control the robot with a higher information transfer rate (ITR. When the robot walked in a crowded area, the AM was used for high accuracy of recognition to reduce the risk of collision. The experimental results showed that, in 100 trials, the accuracy rate of FM was 87.8% and the average ITR was 52.73 bits/min. In addition, the accuracy rate was improved to 92% for the AM, and the average ITR decreased to 31.27 bits/min. due to strict recognition constraints.

  3. Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks

    Science.gov (United States)

    Akkaya, Nurullah; Aytac, Ersin; Günsel, Irfan; Çağman, Ahmet

    2016-01-01

    The design of brain-computer interface for the wheelchair for physically disabled people is presented. The design of the proposed system is based on receiving, processing, and classification of the electroencephalographic (EEG) signals and then performing the control of the wheelchair. The number of experimental measurements of brain activity has been done using human control commands of the wheelchair. Based on the mental activity of the user and the control commands of the wheelchair, the design of classification system based on fuzzy neural networks (FNN) is considered. The design of FNN based algorithm is used for brain-actuated control. The training data is used to design the system and then test data is applied to measure the performance of the control system. The control of the wheelchair is performed under real conditions using direction and speed control commands of the wheelchair. The approach used in the paper allows reducing the probability of misclassification and improving the control accuracy of the wheelchair. PMID:27777953

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

    Science.gov (United States)

    Ray, Andreas M.; Sitaram, Ranganatha; Rana, Mohit; Pasqualotto, Emanuele; Buyukturkoglu, Korhan; Guan, Cuntai; Ang, Kai-Keng; Tejos, Cristián; Zamorano, Francisco; Aboitiz, Francisco; Birbaumer, Niels; Ruiz, Sergio

    2015-01-01

    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. PMID:26539089

  5. Enhanced Z-LDA for Small Sample Size Training in Brain-Computer Interface Systems

    Directory of Open Access Journals (Sweden)

    Dongrui Gao

    2015-01-01

    Full Text Available Background. Usually the training set of online brain-computer interface (BCI experiment is small. For the small training set, it lacks enough information to deeply train the classifier, resulting in the poor classification performance during online testing. Methods. In this paper, on the basis of Z-LDA, we further calculate the classification probability of Z-LDA and then use it to select the reliable samples from the testing set to enlarge the training set, aiming to mine the additional information from testing set to adjust the biased classification boundary obtained from the small training set. The proposed approach is an extension of previous Z-LDA and is named enhanced Z-LDA (EZ-LDA. Results. We evaluated the classification performance of LDA, Z-LDA, and EZ-LDA on simulation and real BCI datasets with different sizes of training samples, and classification results showed EZ-LDA achieved the best classification performance. Conclusions. EZ-LDA is promising to deal with the small sample size training problem usually existing in online BCI system.

  6. Subject combination and electrode selection in cooperative brain-computer interface based on event related potentials.

    Science.gov (United States)

    Cecotti, Hubert; Rivet, Bertrand

    2014-01-01

    New paradigms are required in Brain-Computer Interface (BCI) systems for the needs and expectations of healthy people. To solve this issue, we explore the emerging field of cooperative BCIs, which involves several users in a single BCI system. Contrary to classical BCIs that are dependent on the unique subject's will, cooperative BCIs are used for problem solving tasks where several people shall be engaged by sharing a common goal. Similarly as combining trials over time improves performance, combining trials across subjects can significantly improve performance compared with when only a single user is involved. Yet, cooperative BCIs may only be used in particular settings, and new paradigms must be proposed to efficiently use this approach. The possible benefits of using several subjects are addressed, and compared with current single-subject BCI paradigms. To show the advantages of a cooperative BCI, we evaluate the performance of combining decisions across subjects with data from an event-related potentials (ERP) based experiment where each subject observed the same sequence of visual stimuli. Furthermore, we show that it is possible to achieve a mean AUC superior to 0.95 with 10 subjects and 3 electrodes on each subject, or with 4 subjects and 6 electrodes on each subject. Several emerging challenges and possible applications are proposed to highlight how cooperative BCIs could be efficiently used with current technologies and leverage BCI applications. PMID:24961765

  7. Subject Combination and Electrode Selection in Cooperative Brain-Computer Interface Based on Event Related Potentials

    Directory of Open Access Journals (Sweden)

    Hubert Cecotti

    2014-04-01

    Full Text Available New paradigms are required in Brain-Computer Interface (BCI systems for the needs and expectations of healthy people. To solve this issue, we explore the emerging field of cooperative BCIs, which involves several users in a single BCI system. Contrary to classical BCIs that are dependent on the unique subject’s will, cooperative BCIs are used for problem solving tasks where several people shall be engaged by sharing a common goal. Similarly as combining trials over time improves performance, combining trials across subjects can significantly improve performance compared with when only a single user is involved. Yet, cooperative BCIs may only be used in particular settings, and new paradigms must be proposed to efficiently use this approach. The possible benefits of using several subjects are addressed, and compared with current single-subject BCI paradigms. To show the advantages of a cooperative BCI, we evaluate the performance of combining decisions across subjects with data from an event-related potentials (ERP based experiment where each subject observed the same sequence of visual stimuli. Furthermore, we show that it is possible to achieve a mean AUC superior to 0.95 with 10 subjects and 3 electrodes on each subject, or with 4 subjects and 6 electrodes on each subject. Several emerging challenges and possible applications are proposed to highlight how cooperative BCIs could be efficiently used with current technologies and leverage BCI applications.

  8. Comparison of dry and gel based electrodes for p300 brain-computer interfaces.

    Science.gov (United States)

    Guger, Christoph; Krausz, Gunther; Allison, Brendan Z; Edlinger, Guenter

    2012-01-01

    Most brain-computer interfaces (BCIs) rely on one of three types of signals in the electroencephalogram (EEG): P300s, steady-state visually evoked potentials, and event-related desynchronization. EEG is typically recorded non-invasively with electrodes mounted on the human scalp using conductive electrode gel for optimal impedance and data quality. The use of electrode gel entails serious problems that are especially pronounced in real-world settings when experts are not available. Some recent work has introduced dry electrode systems that do not require gel, but often introduce new problems such as comfort and signal quality. The principal goal of this study was to assess a new dry electrode BCI system in a very common task: spelling with a P300 BCI. A total of 23 subjects used a P300 BCI to spell the word "LUCAS" while receiving real-time, closed-loop feedback. The dry system yielded classification accuracies that were similar to those obtained with gel systems. All subjects completed a questionnaire after data recording, and all subjects stated that the dry system was not uncomfortable. This is the first field validation of a dry electrode P300 BCI system, and paves the way for new research and development with EEG recording systems that are much more practical and convenient in field settings than conventional systems.

  9. Comparison of dry and gel based electrodes for P300 brain-computer interfaces

    Directory of Open Access Journals (Sweden)

    Christoph eGuger

    2012-05-01

    Full Text Available Most brain-computer interfaces (BCI rely on one of three types of signals in the electroencephalogram (EEG: P300s, steady-state visually evoked potentials (SSVEP, and event-related desynchronization (ERD. EEG is typically recorded non-invasively with electrodes mounted on the human scalp using conductive electrode gel for optimal impedance and data quality. The use of electrode gel entails serious problems that are especially pronounced in real-world settings when experts are not available. Some recent work has introduced dry electrode systems that do not require gel, but often introduce new problems such as comfort and signal quality. The principal goal of this study was to assess a new dry electrode BCI system in a very common task: spelling with a P300 BCI. A total of 23 subjects used a P300 BCI to spell the word LUCAS while receiving realtime, closed-loop feedback. The dry system yielded classification accuracies that were similar to those obtained with gel systems. All subjects completed a questionnaire after data recording, and all subjects stated that the dry system was not uncomfortable. This is the first field validation of a dry electrode P300 BCI system, and paves the way for new research and development with EEG recording systems that are much more practical and convenient in field settings than conventional systems.

  10. Leveraging anatomical information to improve transfer learning in brain-computer interfaces

    Science.gov (United States)

    Wronkiewicz, Mark; Larson, Eric; Lee, Adrian K. C.

    2015-08-01

    Objective. Brain-computer interfaces (BCIs) represent a technology with the potential to rehabilitate a range of traumatic and degenerative nervous system conditions but require a time-consuming training process to calibrate. An area of BCI research known as transfer learning is aimed at accelerating training by recycling previously recorded training data across sessions or subjects. Training data, however, is typically transferred from one electrode configuration to another without taking individual head anatomy or electrode positioning into account, which may underutilize the recycled data. Approach. We explore transfer learning with the use of source imaging, which estimates neural activity in the cortex. Transferring estimates of cortical activity, in contrast to scalp recordings, provides a way to compensate for variability in electrode positioning and head morphologies across subjects and sessions. Main results. Based on simulated and measured electroencephalography activity, we trained a classifier using data transferred exclusively from other subjects and achieved accuracies that were comparable to or surpassed a benchmark classifier (representative of a real-world BCI). Our results indicate that classification improvements depend on the number of trials transferred and the cortical region of interest. Significance. These findings suggest that cortical source-based transfer learning is a principled method to transfer data that improves BCI classification performance and provides a path to reduce BCI calibration time.

  11. An adaptive filter bank for motor imagery based Brain Computer Interface.

    Science.gov (United States)

    Thomas, Kavitha P; Guan, Cuntai; Tong, Lau Chiew; Prasad, Vinod A

    2008-01-01

    Brain Computer Interface (BCI) provides an alternative communication and control method for people with severe motor disabilities. Motor imagery patterns are widely used in Electroencephalogram (EEG) based BCIs. These motor imagery activities are associated with variation in alpha and beta band power of EEG signals called Event Related Desynchronization/synchronization (ERD/ERS). The dominant frequency bands are subject-specific and therefore performance of motor imagery based BCIs are sensitive to both temporal filtering and spatial filtering. As the optimum filter is strongly subject-dependent, we propose a method that selects the subject-specific discriminative frequency components using time-frequency plots of Fisher ratio of two-class motor imagery patterns. We also propose a low complexity adaptive Finite Impulse Response (FIR) filter bank system based on coefficient decimation technique which can realize the subject-specific bandpass filters adaptively depending on the information of Fisher ratio map. Features are extracted only from the selected frequency components. The proposed adaptive filter bank based system offers average classification accuracy of about 90%, which is slightly better than the existing fixed filter bank system. PMID:19162856

  12. Auditory and Visual Sensations

    CERN Document Server

    Ando, Yoichi

    2010-01-01

    Professor Yoichi Ando, acoustic architectural designer of the Kirishima International Concert Hall in Japan, presents a comprehensive rational-scientific approach to designing performance spaces. His theory is based on systematic psychoacoustical observations of spatial hearing and listener preferences, whose neuronal correlates are observed in the neurophysiology of the human brain. A correlation-based model of neuronal signal processing in the central auditory system is proposed in which temporal sensations (pitch, timbre, loudness, duration) are represented by an internal autocorrelation representation, and spatial sensations (sound location, size, diffuseness related to envelopment) are represented by an internal interaural crosscorrelation function. Together these two internal central auditory representations account for the basic auditory qualities that are relevant for listening to music and speech in indoor performance spaces. Observed psychological and neurophysiological commonalities between auditor...

  13. 뇌-컴퓨터 인터페이스 (Brain-Computer Interfaces) 기술에 대한 국내·외 연구개발 동향 조사 (Research and Development in Brain-Computer Interfacing Technology: A Comprehensive Technical Review). Final Report.

    OpenAIRE

    Nam, Chang Soo; Kim, Sung-Phil; Krusienkki, Dean; Nijholt, Anton

    2015-01-01

    This report commisioned by the Korean American Scientists and Engineers Association (KSEA) and written with the support of the Korea Federation of Science and Technology Societies (KOFST) surveys research and development trends in the area of brain-computer interface (Brain-Computer Interfaces, BCI) technology. The survey was done by taking expert interviews and through conducting a literature review in the period from September until December 2015. Brain-computer interface is a generic name ...

  14. EEG Responses to Auditory Stimuli for Automatic Affect Recognition

    Science.gov (United States)

    Hettich, Dirk T.; Bolinger, Elaina; Matuz, Tamara; Birbaumer, Niels; Rosenstiel, Wolfgang; Spüler, Martin

    2016-01-01

    Brain state classification for communication and control has been well established in the area of brain-computer interfaces over the last decades. Recently, the passive and automatic extraction of additional information regarding the psychological state of users from neurophysiological signals has gained increased attention in the interdisciplinary field of affective computing. We investigated how well specific emotional reactions, induced by auditory stimuli, can be detected in EEG recordings. We introduce an auditory emotion induction paradigm based on the International Affective Digitized Sounds 2nd Edition (IADS-2) database also suitable for disabled individuals. Stimuli are grouped in three valence categories: unpleasant, neutral, and pleasant. Significant differences in time domain domain event-related potentials are found in the electroencephalogram (EEG) between unpleasant and neutral, as well as pleasant and neutral conditions over midline electrodes. Time domain data were classified in three binary classification problems using a linear support vector machine (SVM) classifier. We discuss three classification performance measures in the context of affective computing and outline some strategies for conducting and reporting affect classification studies. PMID:27375410

  15. Multiple frequencies sequential coding for SSVEP-based brain-computer interface.

    Directory of Open Access Journals (Sweden)

    Yangsong Zhang

    Full Text Available BACKGROUND: Steady-state visual evoked potential (SSVEP-based brain-computer interface (BCI has become one of the most promising modalities for a practical noninvasive BCI system. Owing to both the limitation of refresh rate of liquid crystal display (LCD or cathode ray tube (CRT monitor, and the specific physiological response property that only a very small number of stimuli at certain frequencies could evoke strong SSVEPs, the available frequencies for SSVEP stimuli are limited. Therefore, it may not be enough to code multiple targets with the traditional frequencies coding protocols, which poses a big challenge for the design of a practical SSVEP-based BCI. This study aimed to provide an innovative coding method to tackle this problem. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we present a novel protocol termed multiple frequencies sequential coding (MFSC for SSVEP-based BCI. In MFSC, multiple frequencies are sequentially used in each cycle to code the targets. To fulfill the sequential coding, each cycle is divided into several coding epochs, and during each epoch, certain frequency is used. Obviously, different frequencies or the same frequency can be presented in the coding epochs, and the different epoch sequence corresponds to the different targets. To show the feasibility of MFSC, we used two frequencies to realize four targets and carried on an offline experiment. The current study shows that: 1 MFSC is feasible and efficient; 2 the performance of SSVEP-based BCI based on MFSC can be comparable to some existed systems. CONCLUSIONS/SIGNIFICANCE: The proposed protocol could potentially implement much more targets with the limited available frequencies compared with the traditional frequencies coding protocol. The efficiency of the new protocol was confirmed by real data experiment. We propose that the SSVEP-based BCI under MFSC might be a promising choice in the future.

  16. Using brain-computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience

    Directory of Open Access Journals (Sweden)

    Ole eJensen

    2011-05-01

    Full Text Available Large efforts are currently being made to develop and improve online analysis of brain activity which can be used e.g. for brain-computer interfacing (BCI. A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for aiding the disabled and for augmenting human performance. While technical developments obviously are important, we will here argue that new insight gained from cognitive neuroscience can be used to identify signatures of neural activation which reliably can be modulated by the subject at will. This review will focus mainly on oscillatory activity in the alpha band which is strongly modulated by changes in covert attention. Besides developing BCIs for their traditional purpose, they might also be used as a research tool for cognitive neuroscience. There is currently a strong interest in how brain state fluctuations impact cognition. These state fluctuations are partly reflected by ongoing oscillatory activity. The functional role of the brain state can be investigated by introducing stimuli in real time to subjects depending on the actual state of the brain. This principle of brain-state dependent stimulation may also be used as a practical tool for augmenting human behavior. In conclusion, new approaches based on online analysis of ongoing brain activity are currently in rapid development. These approaches are amongst others informed by new insight gained from EEG/MEG studies in cognitive neuroscience and hold the promise of providing new ways for investigating the brain at work.

  17. Brain computer interfaces for neurorehabilitation – its current status as a rehabilitation strategy post-stroke.

    Science.gov (United States)

    van Dokkum, L E H; Ward, T; Laffont, I

    2015-02-01

    The idea of using brain computer interfaces (BCI) for rehabilitation emerged relatively recently. Basically, BCI for neurorehabilitation involves the recording and decoding of local brain signals generated by the patient, as he/her tries to perform a particular task (even if imperfect), or during a mental imagery task. The main objective is to promote the recruitment of selected brain areas involved and to facilitate neural plasticity. The recorded signal can be used in several ways: (i) to objectify and strengthen motor imagery-based training, by providing the patient feedback on the imagined motor task, for example, in a virtual environment; (ii) to generate a desired motor task via functional electrical stimulation or rehabilitative robotic orthoses attached to the patient's limb – encouraging and optimizing task execution as well as "closing" the disrupted sensorimotor loop by giving the patient the appropriate sensory feedback; (iii) to understand cerebral reorganizations after lesion, in order to influence or even quantify plasticity-induced changes in brain networks. For example, applying cerebral stimulation to re-equilibrate inter-hemispheric imbalance as shown by functional recording of brain activity during movement may help recovery. Its potential usefulness for a patient population has been demonstrated on various levels and its diverseness in interface applications makes it adaptable to a large population. The position and status of these very new rehabilitation systems should now be considered with respect to our current and more or less validated traditional methods, as well as in the light of the wide range of possible brain damage. The heterogeneity in post-damage expression inevitably complicates the decoding of brain signals and thus their use in pathological conditions, asking for controlled clinical trials.

  18. Steady state visually evoked potentials based Brain computer interface test outside the lab

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    Eduardo Francisco Caicedo Bravo

    2016-06-01

    Full Text Available Context: Steady State Visually Evoked Potentials (SSVEP are brain signals which are one of the most promising signals for Brain Computer Interfaces (BCIs implementation, however, SSVEP based BCI generally are proven in a controlled environment and there are a few tests in demanding conditions.Method: We present a SSVEP based BCI system that was used outside the lab in a noisy environment with distractions, and with the presence of public. For the tests, we showed a maze in a laptop where the user could move an avatar looking for a target that is represented by a house.  In order to move the avatar, the volunteer must stare at one of the four visual stimuli; the four visual stimuli represent the four directions: right, up, left, and down. The system is proven without any calibration procedure.Results: 32 volunteers utilized the system and 20 achieved the target with an accuracy above 60%, including 9 with an accuracy of 100%, 7 achieved the target with an accuracy below 60% and 5 left without achieving the goal. For the volunteers who reached accuracy above 60%, the results of the performance achieved an average of 6,4s for command detections, precision of 79% and information transfer rate (ITR of 8,78 bits/s.Conclusions: We showed a SSVEP based BCI system with low cost, it was proved in a public event, it did not have calibration procedures, it was easy to install, and it was used for people in a wide age range. The results show that it is possible to bring this kind of systems to environments outside the laboratory.

  19. Brain-computer interfacing using modulations of alpha activity induced by covert shifts of attention

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    Schmidt Nico M

    2011-05-01

    Full Text Available Abstract Background Visual brain-computer interfaces (BCIs often yield high performance only when targets are fixated with the eyes. Furthermore, many paradigms use intense visual stimulation, which can be irritating especially in long BCI sessions. However, BCIs can more directly directly tap the neural processes underlying visual attention. Covert shifts of visual attention induce changes in oscillatory alpha activity in posterior cortex, even in the absence of visual stimulation. The aim was to investigate whether different pairs of directions of attention shifts can be reliably differentiated based on the electroencephalogram. To this end, healthy participants (N = 8 had to strictly fixate a central dot and covertly shift visual attention to one out of six cued directions. Results Covert attention shifts induced a prolonged alpha synchronization over posterior electrode sites (PO and O electrodes. Spectral changes had specific topographies so that different pairs of directions could be differentiated. There was substantial variation across participants with respect to the direction pairs that could be reliably classified. Mean accuracy for the best-classifiable pair amounted to 74.6%. Furthermore, an alpha power index obtained during a relaxation measurement showed to be predictive of peak BCI performance (r = .66. Conclusions Results confirm posterior alpha power modulations as a viable input modality for gaze-independent EEG-based BCIs. The pair of directions yielding optimal performance varies across participants. Consequently, participants with low control for standard directions such as left-right might resort to other pairs of directions including top and bottom. Additionally, a simple alpha index was shown to predict prospective BCI performance.

  20. Broad-Band Visually Evoked Potentials: Re(convolution in Brain-Computer Interfacing.

    Directory of Open Access Journals (Sweden)

    Jordy Thielen

    Full Text Available Brain-Computer Interfaces (BCIs allow users to control devices and communicate by using brain activity only. BCIs based on broad-band visual stimulation can outperform BCIs using other stimulation paradigms. Visual stimulation with pseudo-random bit-sequences evokes specific Broad-Band Visually Evoked Potentials (BBVEPs that can be reliably used in BCI for high-speed communication in speller applications. In this study, we report a novel paradigm for a BBVEP-based BCI that utilizes a generative framework to predict responses to broad-band stimulation sequences. In this study we designed a BBVEP-based BCI using modulated Gold codes to mark cells in a visual speller BCI. We defined a linear generative model that decomposes full responses into overlapping single-flash responses. These single-flash responses are used to predict responses to novel stimulation sequences, which in turn serve as templates for classification. The linear generative model explains on average 50% and up to 66% of the variance of responses to both seen and unseen sequences. In an online experiment, 12 participants tested a 6 × 6 matrix speller BCI. On average, an online accuracy of 86% was reached with trial lengths of 3.21 seconds. This corresponds to an Information Transfer Rate of 48 bits per minute (approximately 9 symbols per minute. This study indicates the potential to model and predict responses to broad-band stimulation. These predicted responses are proven to be well-suited as templates for a BBVEP-based BCI, thereby enabling communication and control by brain activity only.

  1. Decoding Sensorimotor Rhythms during Robotic-Assisted Treadmill Walking for Brain Computer Interface (BCI Applications.

    Directory of Open Access Journals (Sweden)

    Eliana García-Cossio

    Full Text Available Locomotor malfunction represents a major problem in some neurological disorders like stroke and spinal cord injury. Robot-assisted walking devices have been used during rehabilitation of patients with these ailments for regaining and improving walking ability. Previous studies showed the advantage of brain-computer interface (BCI based robot-assisted training combined with physical therapy in the rehabilitation of the upper limb after stroke. Therefore, stroke patients with walking disorders might also benefit from using BCI robot-assisted training protocols. In order to develop such BCI, it is necessary to evaluate the feasibility to decode walking intention from cortical patterns during robot-assisted gait training. Spectral patterns in the electroencephalogram (EEG related to robot-assisted active and passive walking were investigated in 10 healthy volunteers (mean age 32.3±10.8, six female and in three acute stroke patients (all male, mean age 46.7±16.9, Berg Balance Scale 20±12.8. A logistic regression classifier was used to distinguish walking from baseline in these spectral EEG patterns. Mean classification accuracies of 94.0±5.4% and 93.1±7.9%, respectively, were reached when active and passive walking were compared against baseline. The classification performance between passive and active walking was 83.4±7.4%. A classification accuracy of 89.9±5.7% was achieved in the stroke patients when comparing walking and baseline. Furthermore, in the healthy volunteers modulation of low gamma activity in central midline areas was found to be associated with the gait cycle phases, but not in the stroke patients. Our results demonstrate the feasibility of BCI-based robotic-assisted training devices for gait rehabilitation.

  2. An Algorithm for Idle-State Detection in Motor-Imagery-Based Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Yijun Wang

    2007-07-01

    Full Text Available For a robust brain-computer interface (BCI system based on motor imagery (MI, it should be able to tell when the subject is not concentrating on MI tasks (the “idle state” so that real MI tasks could be extracted accurately. Moreover, because of the diversity of idle state, detecting idle state without training samples is as important as classifying MI tasks. In this paper, we propose an algorithm for solving this problem. A three-class classifier was constructed by combining two two-class classifiers, one specified for idle-state detection and the other for these two MI tasks. Common spatial subspace decomposition (CSSD was used to extract the features of event-related desynchronization (ERD in two motor imagery tasks. Then Fisher discriminant analysis (FDA was employed in the design of two two-class classifiers for completion of detecting each task, respectively. The algorithm successfully provided a way to solve the problem of “idle-state detection without training samples.” The algorithm was applied to the dataset IVc from BCI competition III. A final result with mean square error of 0.30 was obtained on the testing set. This is the winning algorithm in BCI competition III. In addition, the algorithm was also validated by applying to the EEG data of an MI experiment including “idle” task.

  3. Describing different brain computer interface systems through a unique model: a UML implementation.

    Science.gov (United States)

    Quitadamo, Lucia Rita; Marciani, Maria Grazia; Cardarilli, Gian Carlo; Bianchi, Luigi

    2008-01-01

    All the protocols currently implemented in brain computer interface (BCI) experiments are characterized by different structural and temporal entities. Moreover, due to the lack of a unique descriptive model for BCI systems, there is not a standard way to define the structure and the timing of a BCI experimental session among different research groups and there is also great discordance on the meaning of the most common terms dealing with BCI, such as trial, run and session. The aim of this paper is to provide a unified modeling language (UML) implementation of BCI systems through a unique dynamic model which is able to describe the main protocols defined in the literature (P300, mu-rhythms, SCP, SSVEP, fMRI) and demonstrates to be reasonable and adjustable according to different requirements. This model includes a set of definitions of the typical entities encountered in a BCI, diagrams which explain the structural correlations among them and a detailed description of the timing of a trial. This last represents an innovation with respect to the models already proposed in the literature. The UML documentation and the possibility of adapting this model to the different BCI systems built to date, make it a basis for the implementation of new systems and a mean for the unification and dissemination of resources. The model with all the diagrams and definitions reported in the paper are the core of the body language framework, a free set of routines and tools for the implementation, optimization and delivery of cross-platform BCI systems.

  4. 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 ambulation after only 10 minutes training. The ability to achieve such BCI control with minimal training indicates that the implementation of future BCI-lower extremity prosthesis systems may be feasible.

  5. Advancing the detection of steady-state visual evoked potentials in brain-computer interfaces

    Science.gov (United States)

    Abu-Alqumsan, Mohammad; Peer, Angelika

    2016-06-01

    Objective. Spatial filtering has proved to be a powerful pre-processing step in detection of steady-state visual evoked potentials and boosted typical detection rates both in offline analysis and online SSVEP-based brain-computer interface applications. State-of-the-art detection methods and the spatial filters used thereby share many common foundations as they all build upon the second order statistics of the acquired Electroencephalographic (EEG) data, that is, its spatial autocovariance and cross-covariance with what is assumed to be a pure SSVEP response. The present study aims at highlighting the similarities and differences between these methods. Approach. We consider the canonical correlation analysis (CCA) method as a basis for the theoretical and empirical (with real EEG data) analysis of the state-of-the-art detection methods and the spatial filters used thereby. We build upon the findings of this analysis and prior research and propose a new detection method (CVARS) that combines the power of the canonical variates and that of the autoregressive spectral analysis in estimating the signal and noise power levels. Main results. We found that the multivariate synchronization index method and the maximum contrast combination method are variations of the CCA method. All three methods were found to provide relatively unreliable detections in low signal-to-noise ratio (SNR) regimes. CVARS and the minimum energy combination methods were found to provide better estimates for different SNR levels. Significance. Our theoretical and empirical results demonstrate that the proposed CVARS method outperforms other state-of-the-art detection methods when used in an unsupervised fashion. Furthermore, when used in a supervised fashion, a linear classifier learned from a short training session is able to estimate the hidden user intention, including the idle state (when the user is not attending to any stimulus), rapidly, accurately and reliably.

  6. Brain computer tomography in critically ill patients - a prospective cohort study

    International Nuclear Information System (INIS)

    Brain computer tomography (brain CT) is an important imaging tool in patients with intracranial disorders. In ICU patients, a brain CT implies an intrahospital transport which has inherent risks. The proceeds and consequences of a brain CT in a critically ill patient should outweigh these risks. The aim of this study was to critically evaluate the diagnostic and therapeutic yield of brain CT in ICU patients. In a prospective observational study data were collected during one year on the reasons to request a brain CT, expected abnormalities, abnormalities found by the radiologist and consequences for treatment. An “expected abnormality” was any finding that had been predicted by the physician requesting the brain CT. A brain CT was “diagnostically positive”, if the abnormality found was new or if an already known abnormality was increased. It was “diagnostically negative” if an already known abnormality was unchanged or if an expected abnormality was not found. The treatment consequences of the brain CT, were registered as “treatment as planned”, “treatment changed, not as planned”, “treatment unchanged”. Data of 225 brain CT in 175 patients were analyzed. In 115 (51%) brain CT the abnormalities found were new or increased known abnormalities. 115 (51%) brain CT were found to be diagnostically positive. In the medical group 29 (39%) of brain CT were positive, in the surgical group 86 (57%), p 0.01. After a positive brain CT, in which the expected abnormalities were found, treatment was changed as planned in 33%, and in 19% treatment was changed otherwise than planned. The results of this study show that the diagnostic and therapeutic yield of brain CT in critically ill patients is moderate. The development of guidelines regarding the decision rules for performing a brain CT in ICU patients is needed

  7. Towards a symbiotic brain-computer interface: exploring the application-decoder interaction

    Science.gov (United States)

    Verhoeven, T.; Buteneers Wiersema, P., Jr.; Dambre, J.; Kindermans, PJ

    2015-12-01

    Objective. State of the art brain-computer interface (BCI) research focuses on improving individual components such as the application or the decoder that converts the user’s brain activity to control signals. In this study, we investigate the interaction between these components in the P300 speller, a BCI for communication. We introduce a synergistic approach in which the stimulus presentation sequence is modified to enhance the machine learning decoding. In this way we aim for an improved overall BCI performance. Approach. First, a new stimulus presentation paradigm is introduced which provides us flexibility in tuning the sequence of visual stimuli presented to the user. Next, an experimental setup in which this paradigm is compared to other paradigms uncovers the underlying mechanism of the interdependence between the application and the performance of the decoder. Main results. Extensive analysis of the experimental results reveals the changing requirements of the decoder concerning the data recorded during the spelling session. When few data is recorded, the balance in the number of target and non-target stimuli shown to the user is more important than the signal-to-noise rate (SNR) of the recorded response signals. Only when more data has been collected, the SNR becomes the dominant factor. Significance. For BCIs in general, knowing the dominant factor that affects the decoder performance and being able to respond to it is of utmost importance to improve system performance. For the P300 speller, the proposed tunable paradigm offers the possibility to tune the application to the decoder’s needs at any time and, as such, fully exploit this application-decoder interaction.

  8. A dry EEG-system for scientific research and brain-computer interfaces

    Directory of Open Access Journals (Sweden)

    Thorsten Oliver Zander

    2011-05-01

    Full Text Available Although it ranks among the oldest tools in neuroscientific research, electroencephalography (EEG still forms the method of choice in a wide variety of clinical and research applications. In the context of Brain-Computer Interfacing (BCI, EEG recently has become a tool to enhance Human-Machine Interaction (HMI. EEG could be employed in a wider range of environments, especially for the use of BCI systems in a clinical context or at the homes of patients. However, the application of EEG in these contexts is impeded by the cumbersome preparation of the electrodes with conductive gel that is necessary to lower the impedance between electrodes and scalp. Dry electrodes could provide a solution to this barrier and allow for EEG applications outside the laboratory. In addition, dry electrodes may reduce the time needed for neurological exams in clinical practice. This study evaluates a prototype of a three-channel dry electrode EEG system, comparing it to state-of-the-art conventional EEG electrodes. Two experimental paradigms were used: first, Event-Related Potentials (ERP were investigated with a variant of the oddball paradigm. Second, features of the frequency domain were compared by a paradigm inducing occipital alpha. Furthermore, both paradigms were used to evaluate BCI classification accuracies of both EEG systems. Amplitude and temporal structure of ERPs as well as features in the frequency domain did not differ significantly between the EEG systems. BCI classification accuracies were equally high in both systems when the frequency domain was considered. With respect to the oddball classification accuracy, there were slight differences between the wet and dry electrode systems. We conclude that the tested dry electrodes were capable to detect EEG signals with good quality and that these signals can be used for research or BCI applications. Easy to handle electrodes may help to foster the use of EEG among a wider range of potential users.

  9. A combination strategy based brain-computer interface for two-dimensional movement control

    Science.gov (United States)

    Xia, Bin; Maysam, Oladazimi; Veser, Sandra; Cao, Lei; Li, Jie; Jia, Jie; Xie, Hong; Birbaumer, Niels

    2015-08-01

    Objective. Two-dimensional (2D) movement control is an important issue in brain-computer interfaces (BCIs) research because being able to move, for example, a cursor with the brain will enable patients with motor disabilities to control their environment. However, it is still a challenge to continuously control 2D movement with a non-invasive BCI system. In this paper, we developed a 2D cursor control with motor imagery BCI tasks allowing users to move a cursor to any position by using a combination strategy. With this strategy, a user can combine multiple motor imagery tasks, alternatively or simultaneously, to control 2D movements. Approach. After a training session, six participants took part in the first control strategy experiment (the center-out experiment) to verify the effectiveness of the cursor control. Three of the six participants performed an additional experiment, in which they were required to move the cursor to hit five targets in a given sequence. Main results. The average hit rate was more than 95.6% and the trajectories were close to the shortest path. The average hit rate was more than 95.6% and the trajectories were close to the shortest path in the center-out experiment. In the additional experiment, three participants achieved a 100% hit rate with a short trajectory. Significance. The results demonstrated that users were able to effectively control the 2D movement using the proposed strategy. The present system may be used as a tool to interact with the external world.

  10. Decoding Sensorimotor Rhythms during Robotic-Assisted Treadmill Walking for Brain Computer Interface (BCI) Applications.

    Science.gov (United States)

    García-Cossio, Eliana; Severens, Marianne; Nienhuis, Bart; Duysens, Jacques; Desain, Peter; Keijsers, Nöel; Farquhar, Jason

    2015-01-01

    Locomotor malfunction represents a major problem in some neurological disorders like stroke and spinal cord injury. Robot-assisted walking devices have been used during rehabilitation of patients with these ailments for regaining and improving walking ability. Previous studies showed the advantage of brain-computer interface (BCI) based robot-assisted training combined with physical therapy in the rehabilitation of the upper limb after stroke. Therefore, stroke patients with walking disorders might also benefit from using BCI robot-assisted training protocols. In order to develop such BCI, it is necessary to evaluate the feasibility to decode walking intention from cortical patterns during robot-assisted gait training. Spectral patterns in the electroencephalogram (EEG) related to robot-assisted active and passive walking were investigated in 10 healthy volunteers (mean age 32.3±10.8, six female) and in three acute stroke patients (all male, mean age 46.7±16.9, Berg Balance Scale 20±12.8). A logistic regression classifier was used to distinguish walking from baseline in these spectral EEG patterns. Mean classification accuracies of 94.0±5.4% and 93.1±7.9%, respectively, were reached when active and passive walking were compared against baseline. The classification performance between passive and active walking was 83.4±7.4%. A classification accuracy of 89.9±5.7% was achieved in the stroke patients when comparing walking and baseline. Furthermore, in the healthy volunteers modulation of low gamma activity in central midline areas was found to be associated with the gait cycle phases, but not in the stroke patients. Our results demonstrate the feasibility of BCI-based robotic-assisted training devices for gait rehabilitation.

  11. Novel semi-dry electrodes for brain-computer interface applications

    Science.gov (United States)

    Wang, Fei; Li, Guangli; Chen, Jingjing; Duan, Yanwen; Zhang, Dan

    2016-08-01

    Objectives. Modern applications of brain-computer interfaces (BCIs) based on electroencephalography rely heavily on the so-called wet electrodes (e.g. Ag/AgCl electrodes) which require gel application and skin preparation to operate properly. Recently, alternative ‘dry’ electrodes have been developed to increase ease of use, but they often suffer from higher electrode-skin impedance and signal instability. In the current paper, we have proposed a novel porous ceramic-based ‘semi-dry’ electrode. The key feature of the semi-dry electrodes is that their tips can slowly and continuously release a tiny amount of electrolyte liquid to the scalp, which provides an ionic conducting path for detecting neural signals. Approach. The performance of the proposed electrode was evaluated by simultaneous recording of the wet and semi-dry electrodes pairs in five classical BCI paradigms: eyes open/closed, the motor imagery BCI, the P300 speller, the N200 speller and the steady-state visually evoked potential-based BCI. Main results. The grand-averaged temporal cross-correlation was 0.95 ± 0.07 across the subjects and the nine recording positions, and these cross-correlations were stable throughout the whole experimental protocol. In the spectral domain, the semi-dry/wet coherence was greater than 0.80 at all frequencies and greater than 0.90 at frequencies above 10 Hz, with the exception of a dip around 50 Hz (i.e. the powerline noise). More importantly, the BCI classification accuracies were also comparable between the two types of electrodes. Significance. Overall, these results indicate that the proposed semi-dry electrode can effectively capture the electrophysiological responses and is a feasible alternative to the conventional dry electrode in BCI applications.

  12. A Dry EEG-System for Scientific Research and Brain-Computer Interfaces.

    Science.gov (United States)

    Zander, Thorsten Oliver; Lehne, Moritz; Ihme, Klas; Jatzev, Sabine; Correia, Joao; Kothe, Christian; Picht, Bernd; Nijboer, Femke

    2011-01-01

    Although it ranks among the oldest tools in neuroscientific research, electroencephalography (EEG) still forms the method of choice in a wide variety of clinical and research applications. In the context of brain-computer interfacing (BCI), EEG recently has become a tool to enhance human-machine interaction. EEG could be employed in a wider range of environments, especially for the use of BCI systems in a clinical context or at the homes of patients. However, the application of EEG in these contexts is impeded by the cumbersome preparation of the electrodes with conductive gel that is necessary to lower the impedance between electrodes and scalp. Dry electrodes could provide a solution to this barrier and allow for EEG applications outside the laboratory. In addition, dry electrodes may reduce the time needed for neurological exams in clinical practice. This study evaluates a prototype of a three-channel dry electrode EEG system, comparing it to state-of-the-art conventional EEG electrodes. Two experimental paradigms were used: first, event-related potentials (ERP) were investigated with a variant of the oddball paradigm. Second, features of the frequency domain were compared by a paradigm inducing occipital alpha. Furthermore, both paradigms were used to evaluate BCI classification accuracies of both EEG systems. Amplitude and temporal structure of ERPs as well as features in the frequency domain did not differ significantly between the EEG systems. BCI classification accuracies were equally high in both systems when the frequency domain was considered. With respect to the oddball classification accuracy, there were slight differences between the wet and dry electrode systems. We conclude that the tested dry electrodes were capable to detect EEG signals with good quality and that these signals can be used for research or BCI applications. Easy to handle electrodes may help to foster the use of EEG among a wider range of potential users.

  13. Collaborative filtering for brain-computer interaction using transfer learning and active class selection.

    Directory of Open Access Journals (Sweden)

    Dongrui Wu

    Full Text Available Brain-computer interaction (BCI and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL, active class selection (ACS, and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both k nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing.

  14. A brain-computer interface for potential non-verbal facial communication based on EEG signals related to specific emotions

    OpenAIRE

    Kashihara, Koji

    2014-01-01

    Unlike assistive technology for verbal communication, the brain-machine or brain-computer interface (BMI/BCI) has not been established as a non-verbal communication tool for amyotrophic lateral sclerosis (ALS) patients. Face-to-face communication enables access to rich emotional information, but individuals suffering from neurological disorders, such as ALS and autism, may not express their emotions or communicate their negative feelings. Although emotions may be inferred by looking at facial...

  15. A hypothesis of brain-to-brain coupling in interactive new media art and games using brain-computer interfaces

    OpenAIRE

    Zioga, Polina; Chapman, Paul; Ma, Minhua; Pollick, Frank

    2015-01-01

    Interactive new media art and games belong to distinctive fields, but nevertheless share common grounds, tools, methodologies, challenges, and goals, such as the use of applications and devices for engaging multiple participants and players, and more recently electroencephalography (EEG)-based brain-computer interfaces (BCIs). At the same time, an increasing number of new neuroscientific studies explore the phenomenon of brain-to-brain coupling, the dynamics and processes of the interaction a...

  16. Student teaching and research laboratory focusing on brain-computer interface paradigms--A creative environment for computer science students.

    Science.gov (United States)

    Rutkowski, Tomasz M

    2015-08-01

    This paper presents an applied concept of a brain-computer interface (BCI) student research laboratory (BCI-LAB) at the Life Science Center of TARA, University of Tsukuba, Japan. Several successful case studies of the student projects are reviewed together with the BCI Research Award 2014 winner case. The BCI-LAB design and project-based teaching philosophy is also explained. Future teaching and research directions summarize the review.

  17. Event-related brain potentials in emotion perception research, individual cognitive assessment and brain-computer interfaces

    OpenAIRE

    Bostanov, Vladimir

    2003-01-01

    All of the experimental and theoretical work presented in this dissertation has been inspired by the general idea of applying event-related brain potential (ERP) measurement and assessment for practical purposes: cognitive diagnostics and Brain-Computer Interfaces (BCI) for paralyzed people. In Chapter 1, two new ERP paradigms are introduced, which were developed for the diagnostics of a particular cognitive function, the recognition of affective prosody. The affective state of a speaker ...

  18. Real-time functional magnetic imaging-brain-computer interface and virtual reality promising tools for the treatment of pedophilia.

    Science.gov (United States)

    Renaud, Patrice; Joyal, Christian; Stoleru, Serge; Goyette, Mathieu; Weiskopf, Nikolaus; Birbaumer, Niels

    2011-01-01

    This chapter proposes a prospective view on using a real-time functional magnetic imaging (rt-fMRI) brain-computer interface (BCI) application as a new treatment for pedophilia. Neurofeedback mediated by interactive virtual stimuli is presented as the key process in this new BCI application. Results on the diagnostic discriminant power of virtual characters depicting sexual stimuli relevant to pedophilia are given. Finally, practical and ethical implications are briefly addressed.

  19. Minimizing inter-subject variability in fNIRS based Brain Computer Interfaces via multiple-kernel support vector learning

    OpenAIRE

    Abibullaev, Berdakh; An, Jinung; Lee, Seung-Hyun; Jin, Sang-Hyeon; Moon, Jeon-Il

    2012-01-01

    Brain signal variability in the measurements obtained from different subjects during different sessions significantly deteriorates the accuracy of most brain-computer interface (BCI) systems. Moreover these variabilities, also known as inter-subject or inter-session variabilities, require lengthy calibration sessions before the BCI system can be used. Furthermore, the calibration session has to be repeated for each subject independently and before use of the BCI due to the inter-session varia...

  20. Optimizing the Detection of Wakeful and Sleep-Like States for Future Electrocorticographic Brain Computer Interface Applications

    OpenAIRE

    Mrinal Pahwa; Matthew Kusner; Hacker, Carl D.; Bundy, David T.; Weinberger, Kilian Q.; Leuthardt, Eric C.

    2015-01-01

    Previous studies suggest stable and robust control of a brain-computer interface (BCI) can be achieved using electrocorticography (ECoG). Translation of this technology from the laboratory to the real world requires additional methods that allow users operate their ECoG-based BCI autonomously. In such an environment, users must be able to perform all tasks currently performed by the experimenter, including manually switching the BCI system on/off. Although a simple task, it can be challenging...

  1. Comparison of an open-hardware electroencephalography amplifier with medical grade device in brain-computer interface applications

    OpenAIRE

    Frey, Jérémy

    2016-01-01

    International audience Brain-computer interfaces (BCI) are promising communication devices between humans and machines. BCI based on non-invasive neuroimaging techniques such as electroencephalography (EEG) have many applications , however the dissemination of the technology is limited, in part because of the price of the hardware. In this paper we compare side by side two EEG amplifiers, the consumer grade OpenBCI and the medical grade g.tec g.USBamp. For this purpose, we employed an orig...

  2. The neglected neglect: auditory neglect.

    Science.gov (United States)

    Gokhale, Sankalp; Lahoti, Sourabh; Caplan, Louis R

    2013-08-01

    Whereas visual and somatosensory forms of neglect are commonly recognized by clinicians, auditory neglect is often not assessed and therefore neglected. The auditory cortical processing system can be functionally classified into 2 distinct pathways. These 2 distinct functional pathways deal with recognition of sound ("what" pathway) and the directional attributes of the sound ("where" pathway). Lesions of higher auditory pathways produce distinct clinical features. Clinical bedside evaluation of auditory neglect is often difficult because of coexisting neurological deficits and the binaural nature of auditory inputs. In addition, auditory neglect and auditory extinction may show varying degrees of overlap, which makes the assessment even harder. Shielding one ear from the other as well as separating the ear from space is therefore critical for accurate assessment of auditory neglect. This can be achieved by use of specialized auditory tests (dichotic tasks and sound localization tests) for accurate interpretation of deficits. Herein, we have reviewed auditory neglect with an emphasis on the functional anatomy, clinical evaluation, and basic principles of specialized auditory tests.

  3. Modulation of Posterior Alpha Activity by Spatial Attention Allows for Controlling A Continuous Brain-Computer Interface.

    Science.gov (United States)

    Horschig, Jörn M; Oosterheert, Wouter; Oostenveld, Robert; Jensen, Ole

    2015-11-01

    Here we report that the modulation of alpha activity by covert attention can be used as a control signal in an online brain-computer interface, that it is reliable, and that it is robust. Subjects were instructed to orient covert visual attention to the left or right hemifield. We decoded the direction of attention from the magnetoencephalogram by a template matching classifier and provided the classification outcome to the subject in real-time using a novel graphical user interface. Training data for the templates were obtained from a Posner-cueing task conducted just before the BCI task. Eleven subjects participated in four sessions each. Eight of the subjects achieved classification rates significantly above chance level. Subjects were able to significantly increase their performance from the first to the second session. Individual patterns of posterior alpha power remained stable throughout the four sessions and did not change with increased performance. We conclude that posterior alpha power can successfully be used as a control signal in brain-computer interfaces. We also discuss several ideas for further improving the setup and propose future research based on solid hypotheses about behavioral consequences of modulating neuronal oscillations by brain computer interfacing. PMID:25388661

  4. 뇌-컴퓨터 인터페이스 (Brain-Computer Interfaces) 기술에 대한 국내·외 연구개발 동향 조사 (Research and Development in Brain-Computer Interfacing Technology: A Comprehensive Technical Review). Final Report.

    NARCIS (Netherlands)

    Nam, Chang Soo; Kim, Sung-Phil; Krusienkki, Dean; Nijholt, Anton

    2015-01-01

    This report commisioned by the Korean American Scientists and Engineers Association (KSEA) and written with the support of the Korea Federation of Science and Technology Societies (KOFST) surveys research and development trends in the area of brain-computer interface (Brain-Computer Interfaces, BCI)

  5. Auditory pathways: anatomy and physiology.

    Science.gov (United States)

    Pickles, James O

    2015-01-01

    This chapter outlines the anatomy and physiology of the auditory pathways. After a brief analysis of the external, middle ears, and cochlea, the responses of auditory nerve fibers are described. The central nervous system is analyzed in more detail. A scheme is provided to help understand the complex and multiple auditory pathways running through the brainstem. The multiple pathways are based on the need to preserve accurate timing while extracting complex spectral patterns in the auditory input. The auditory nerve fibers branch to give two pathways, a ventral sound-localizing stream, and a dorsal mainly pattern recognition stream, which innervate the different divisions of the cochlear nucleus. The outputs of the two streams, with their two types of analysis, are progressively combined in the inferior colliculus and onwards, to produce the representation of what can be called the "auditory objects" in the external world. The progressive extraction of critical features in the auditory stimulus in the different levels of the central auditory system, from cochlear nucleus to auditory cortex, is described. In addition, the auditory centrifugal system, running from cortex in multiple stages to the organ of Corti of the cochlea, is described.

  6. 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

  7. Rapid P300 brain-computer interface communication with a head-mounted display

    Directory of Open Access Journals (Sweden)

    Ivo eKäthner

    2015-06-01

    Full Text Available Visual ERP (P300 based brain-computer interfaces (BCIs allow for fast and reliable spelling and are intended as a muscle-independent communication channel for people with severe paralysis. However, they require the presentation of visual stimuli in the field of view of the user. A head mounted display could allow convenient presentation of visual stimuli in situations, where mounting a conventional monitor might be difficult or not feasible (e.g. at a patient’s bedside. To explore if similar accuracies can be achieved with a virtual reality (VR headset compared to a conventional flat screen monitor, we conducted an experiment with 18 healthy participants. We also evaluated it with a person in the locked-in state (LIS to verify that usage of the headset is possible for a severely paralyzed person. Healthy participants performed online spelling with three different display methods. In one condition a 5x5 letter matrix was presented on a conventional 22 inch TFT monitor. Two configurations of the VR headset were tested. In the first (glasses A, the same 5x5 matrix filled the field of view of the user. In the second (glasses B, single letters of the matrix filled the field of view of the user. The participant in the LIS tested the VR headset on 3 different occasions (glasses A condition only. For healthy participants, average online spelling accuracies were 94% (15.5 bits/min using three flash sequences for spelling with the monitor and glasses A and 96% (16.2 bits/min with glasses B. In one session, the participant in the LIS reached an online spelling accuracy of 100% (10 bits/min using the glasses A condition. We also demonstrated that spelling with one flash sequence is possible with the VR headset for healthy users (mean: 32.1 bits/min, maximum reached by one user: 71.89 bits/min at 100% accuracy. We conclude that the VR headset allows for rapid P300 BCI communication in healthy users and may be a suitable display option for severely

  8. Adaptive estimation of hand movement trajectory in an EEG based brain-computer interface system

    Science.gov (United States)

    Robinson, Neethu; Guan, Cuntai; Vinod, A. P.

    2015-12-01

    Objective. The various parameters that define a hand movement such as its trajectory, speed, etc, are encoded in distinct brain activities. Decoding this information from neurophysiological recordings is a less explored area of brain-computer interface (BCI) research. Applying non-invasive recordings such as electroencephalography (EEG) for decoding makes the problem more challenging, as the encoding is assumed to be deep within the brain and not easily accessible by scalp recordings. Approach. EEG based BCI systems can be developed to identify the neural features underlying movement parameters that can be further utilized to provide a detailed and well defined control command set to a BCI output device. A real-time continuous control is better suited for practical BCI systems, and can be achieved by continuous adaptive reconstruction of movement trajectory than discrete brain activity classifications. In this work, we adaptively reconstruct/estimate the parameters of two-dimensional hand movement trajectory, namely movement speed and position, from multi-channel EEG recordings. The data for analysis is collected by performing an experiment that involved center-out right-hand movement tasks in four different directions at two different speeds in random order. We estimate movement trajectory using a Kalman filter that models the relation between brain activity and recorded parameters based on a set of defined predictors. We propose a method to define these predictor variables that includes spatial, spectral and temporally localized neural information and to select optimally informative variables. Main results. The proposed method yielded correlation of (0.60 ± 0.07) between recorded and estimated data. Further, incorporating the proposed predictor subset selection, the correlation achieved is (0.57 ± 0.07, p {\\lt }0.004) with significant gain in stability of the system, as well as dramatic reduction in number of predictors (76%) for the savings of computational

  9. Automatic artefact removal in a self-paced hybrid brain- computer interface system

    Directory of Open Access Journals (Sweden)

    Yong Xinyi

    2012-07-01

    Full Text Available Abstract Background A novel artefact removal algorithm is proposed for a self-paced hybrid brain-computer interface (BCI system. This hybrid system combines a self-paced BCI with an eye-tracker to operate a virtual keyboard. To select a letter, the user must gaze at the target for at least a specific period of time (dwell time and then activate the BCI by performing a mental task. Unfortunately, electroencephalogram (EEG signals are often contaminated with artefacts. Artefacts change the quality of EEG signals and subsequently degrade the BCI’s performance. Methods To remove artefacts in EEG signals, the proposed algorithm uses the stationary wavelet transform combined with a new adaptive thresholding mechanism. To evaluate the performance of the proposed algorithm and other artefact handling/removal methods, semi-simulated EEG signals (i.e., real EEG signals mixed with simulated artefacts and real EEG signals obtained from seven participants are used. For real EEG signals, the hybrid BCI system’s performance is evaluated in an online-like manner, i.e., using the continuous data from the last session as in a real-time environment. Results With semi-simulated EEG signals, we show that the proposed algorithm achieves lower signal distortion in both time and frequency domains. With real EEG signals, we demonstrate that for dwell time of 0.0s, the number of false-positives/minute is 2 and the true positive rate (TPR achieved by the proposed algorithm is 44.7%, which is more than 15.0% higher compared to other state-of-the-art artefact handling methods. As dwell time increases to 1.0s, the TPR increases to 73.1%. Conclusions The proposed artefact removal algorithm greatly improves the BCI’s performance. It also has the following advantages: a it does not require additional electrooculogram/electromyogram channels, long data segments or a large number of EEG channels, b it allows real-time processing, and c it reduces signal distortion.

  10. An optimized ERP brain-computer interface based on facial expression changes

    Science.gov (United States)

    Jin, Jing; Daly, Ian; Zhang, Yu; Wang, Xingyu; Cichocki, Andrzej

    2014-06-01

    Objective. Interferences from spatially adjacent non-target stimuli are known to evoke event-related potentials (ERPs) during non-target flashes and, therefore, lead to false positives. This phenomenon was commonly seen in visual attention-based brain-computer interfaces (BCIs) using conspicuous stimuli and is known to adversely affect the performance of BCI systems. Although users try to focus on the target stimulus, they cannot help but be affected by conspicuous changes of the stimuli (such as flashes or presenting images) which were adjacent to the target stimulus. Furthermore, subjects have reported that conspicuous stimuli made them tired and annoyed. In view of this, the aim of this study was to reduce adjacent interference, annoyance and fatigue using a new stimulus presentation pattern based upon facial expression changes. Our goal was not to design a new pattern which could evoke larger ERPs than the face pattern, but to design a new pattern which could reduce adjacent interference, annoyance and fatigue, and evoke ERPs as good as those observed during the face pattern. Approach. Positive facial expressions could be changed to negative facial expressions by minor changes to the original facial image. Although the changes are minor, the contrast is big enough to evoke strong ERPs. In this paper, a facial expression change pattern between positive and negative facial expressions was used to attempt to minimize interference effects. This was compared against two different conditions, a shuffled pattern containing the same shapes and colours as the facial expression change pattern, but without the semantic content associated with a change in expression, and a face versus no face pattern. Comparisons were made in terms of classification accuracy and information transfer rate as well as user supplied subjective measures. Main results. The results showed that interferences from adjacent stimuli, annoyance and the fatigue experienced by the subjects could be

  11. 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

  12. 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

  13. Visual–auditory spatial processing in auditory cortical neurons

    OpenAIRE

    Bizley, Jennifer K.; King, Andrew J

    2008-01-01

    Neurons responsive to visual stimulation have now been described in the auditory cortex of various species, but their functions are largely unknown. Here we investigate the auditory and visual spatial sensitivity of neurons recorded in 5 different primary and non-primary auditory cortical areas of the ferret. We quantified the spatial tuning of neurons by measuring the responses to stimuli presented across a range of azimuthal positions and calculating the mutual information (MI) between the ...

  14. Resizing Auditory Communities

    DEFF Research Database (Denmark)

    Kreutzfeldt, Jacob

    2012-01-01

    Heard through the ears of the Canadian composer and music teacher R. Murray Schafer the ideal auditory community had the shape of a village. Schafer’s work with the World Soundscape Project in the 70s represent an attempt to interpret contemporary environments through musical and auditory...... of sound as an active component in shaping urban environments. As urban conditions spreads globally, new scales, shapes and forms of communities appear and call for new distinctions and models in the study and representation of sonic environments. Particularly so, since urban environments are increasingly...... presents some terminologies for mapping urban environments through its sonic configuration. Such probing into the practices of acoustic territorialisation may direct attention to some of the conflicting and disharmonious interests defining public inclusive domains. The paper investigates the concept...

  15. A Review of the Brain - computer Interface Technology%脑-机接口技术研究概况

    Institute of Scientific and Technical Information of China (English)

    赵慧; 李远清

    2006-01-01

    脑-机接口(BCI-Brain-Computer Interface)是一种全新的通讯和控制技术.首先介绍BCI的定义和工作原理;从输入信号的类型选择、预处理、特征的提取、分类方法等方面论述了BCI系统设计中的关键技术;最后对BCI的应用及在未来的发展作了介绍.

  16. 脑机接口技术研究概述%A Review of Brain-Computer Interface Technology

    Institute of Scientific and Technical Information of China (English)

    朱文明; 高诺

    2008-01-01

    脑机接口(Brain-Computer Interface, BCI)是在人脑和外界之间建立不依赖于常规大脑信息输出通路(外周神经和肌肉组织)的一种通讯系统.本文概述了基于脑电信号(EEG)的BCI技术的基本原理、研究方法、类型、研究现状,并分析了目前存在的问题与应用前景.

  17. 脑机接口技术研究%The study of brain-computer interface technology

    Institute of Scientific and Technical Information of China (English)

    杨瑞霞

    2009-01-01

    脑机接口(Brain-Computer Interface,BCI)是在人脑和外界之间建立不依赖于常规大脑信息输出通路(外周神经和肌肉组织)的一种通讯系统.该文概述了基于脑电信号(EEG)的BCI技术的基本原理、研究方法、类型、研究现状,并分析了目前存在的问题与应用前景.

  18. Frontiers: tools for brain-computer interaction: a general concept for a hybrid BCI (hBCI)

    OpenAIRE

    Mueller-Putz, G.R.; Breitwieser, C.; Cincotti, F; Leeb, R.; Schreuder, M; Leotta, F.; Tavella, M.; L. Bianchi; Kreilinger, A.; RAMSAY, A.; Rohm, M.; Sagebaum, M.; Tonin, L.; Neuper, C.; Millán, J. d. R.

    2011-01-01

    The aim of this work is to present the development of a hybrid Brain-Computer Interface (hBCI) which combines existing input devices with a BCI. Thereby, the BCI should be available if the user wishes to extend the types of inputs available to an assistive technology system, but the user can also choose not to use the BCI at all; the BCI is active in the background. The hBCI might decide on the one hand which input channel(s) offer the most reliable signal(s) and switch between input channels...

  19. Auditory and non-auditory effects of noise on health

    NARCIS (Netherlands)

    Basner, M.; Babisch, W.; Davis, A.; Brink, M.; Clark, C.; Janssen, S.A.; Stansfeld, S.

    2013-01-01

    Noise is pervasive in everyday life and can cause both auditory and non-auditory health eff ects. Noise-induced hearing loss remains highly prevalent in occupational settings, and is increasingly caused by social noise exposure (eg, through personal music players). Our understanding of molecular mec

  20. Partial Epilepsy with Auditory Features

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2004-07-01

    Full Text Available The clinical characteristics of 53 sporadic (S cases of idiopathic partial epilepsy with auditory features (IPEAF were analyzed and compared to previously reported familial (F cases of autosomal dominant partial epilepsy with auditory features (ADPEAF in a study at the University of Bologna, Italy.

  1. The Perception of Auditory Motion.

    Science.gov (United States)

    Carlile, Simon; Leung, Johahn

    2016-01-01

    The growing availability of efficient and relatively inexpensive virtual auditory display technology has provided new research platforms to explore the perception of auditory motion. At the same time, deployment of these technologies in command and control as well as in entertainment roles is generating an increasing need to better understand the complex processes underlying auditory motion perception. This is a particularly challenging processing feat because it involves the rapid deconvolution of the relative change in the locations of sound sources produced by rotational and translations of the head in space (self-motion) to enable the perception of actual source motion. The fact that we perceive our auditory world to be stable despite almost continual movement of the head demonstrates the efficiency and effectiveness of this process. This review examines the acoustical basis of auditory motion perception and a wide range of psychophysical, electrophysiological, and cortical imaging studies that have probed the limits and possible mechanisms underlying this perception. PMID:27094029

  2. Peripheral Auditory Mechanisms

    CERN Document Server

    Hall, J; Hubbard, A; Neely, S; Tubis, A

    1986-01-01

    How weIl can we model experimental observations of the peripheral auditory system'? What theoretical predictions can we make that might be tested'? It was with these questions in mind that we organized the 1985 Mechanics of Hearing Workshop, to bring together auditory researchers to compare models with experimental observations. Tbe workshop forum was inspired by the very successful 1983 Mechanics of Hearing Workshop in Delft [1]. Boston University was chosen as the site of our meeting because of the Boston area's role as a center for hearing research in this country. We made a special effort at this meeting to attract students from around the world, because without students this field will not progress. Financial support for the workshop was provided in part by grant BNS- 8412878 from the National Science Foundation. Modeling is a traditional strategy in science and plays an important role in the scientific method. Models are the bridge between theory and experiment. Tbey test the assumptions made in experim...

  3. Rapid multi-residue and multi-class qualitative screening for veterinary drugs in foods of animal origin by UHPLC-MS/MS.

    Science.gov (United States)

    Robert, C; Gillard, N; Brasseur, P-Y; Pierret, G; Ralet, N; Dubois, M; Delahaut, Ph

    2013-01-01

    Multi-class UHPLC-MS/MS was developed for the analysis of more than 160 regulated or banned compounds of various classes: anthelmintics including benzimidazoles, avermectins and others; antibiotics including amphenicols, beta-lactams, macrolides, pyrimidines, quinolones, sulphonamides and tetracyclines; beta-agonists; corticosteroids; ionophores; nitroimidazoles; non-steroidal anti-inflammatory agents; steroids; and tranquillisers. Samples were extracted with acetonitrile, without any additional purification step, and analysed by using UHPLC-MS/MS. Validation was done in accordance with the guidelines laid down by European Commission Decision 2002/657/EC for qualitative screening methods. This simple method proved applicable to routine screening for residues in egg, honey, milk and muscle samples at half the maximum concentration permitted by the European Union for each drug. In most cases, the target value was set at 5 µg kg(-1) for unauthorised compounds.

  4. Multi-class methodology to determine pesticides and mycotoxins in green tea and royal jelly supplements by liquid chromatography coupled to Orbitrap high resolution mass spectrometry.

    Science.gov (United States)

    Martínez-Domínguez, Gerardo; Romero-González, Roberto; Garrido Frenich, Antonia

    2016-04-15

    A multi-class methodology was developed to determine pesticides and mycotoxins in food supplements. The extraction was performed using acetonitrile acidified with formic acid (1%, v/v). Different clean-up sorbents were tested, and the best results were obtained using C18 and zirconium oxide for green tea and royal jelly, respectively. The compounds were determined using ultra high performance liquid chromatography (UHPLC) coupled to Exactive-Orbitrap high resolution mass spectrometry (HRMS). The recovery rates obtained were between 70% and 120% for most of the compounds studied with a relative standard deviation aflatoxin B1 (5.4 μg/kg) was also found in one of the green tea samples. PMID:26617033

  5. A novel 9-class auditory ERP paradigm driving a predictive text entry system

    Directory of Open Access Journals (Sweden)

    Johannes eHöhne

    2011-08-01

    Full Text Available Brain-Computer Interfaces (BCIs based on Event Related Potentials (ERPs strive for offering communication pathways which are independent of muscle activity. While most visual ERP-based BCI paradigms require good control of the user's gaze direction, auditory BCI paradigms overcome this restriction. The present work proposes a novel approach using Auditory Evoked Potentials (AEP for the example of a multiclass text spelling application. To control the ERP speller, BCI users focus their attention to two-dimensional auditory stimuli that vary in both, pitch (high/medium/low and direction (left/middle/right and that are presented via headphones. The resulting nine different control signals are exploited to drive a predictive text entry system. It enables the user to spell a letter by a single 9-class decision plus two additional decisions to confirm a spelled word.This paradigm - called PASS2D - was investigated in an online study with twelve healthy participants. Users spelled with more than 0.8 characters per minute on average (3.4 bits per minute which makes PASS2D a competitive method. It could enrich the toolbox of existing ERP paradigms for BCI end users like late-stage ALS patients.

  6. An exploration of spatial auditory BCI paradigms with different sounds: music notes versus beeps.

    Science.gov (United States)

    Huang, Minqiang; Daly, Ian; Jin, Jing; Zhang, Yu; Wang, Xingyu; Cichocki, Andrzej

    2016-06-01

    Visual brain-computer interfaces (BCIs) are not suitable for people who cannot reliably maintain their eye gaze. Considering that this group usually maintains audition, an auditory based BCI may be a good choice for them. In this paper, we explore two auditory patterns: (1) a pattern utilizing symmetrical spatial cues with multiple frequency beeps [called the high low medium (HLM) pattern], and (2) a pattern utilizing non-symmetrical spatial cues with six tones derived from the diatonic scale [called the diatonic scale (DS) pattern]. These two patterns are compared to each other in terms of accuracy to determine which auditory pattern is better. The HLM pattern uses three different frequency beeps and has a symmetrical spatial distribution. The DS pattern uses six spoken stimuli, which are six notes solmizated as "do", "re", "mi", "fa", "sol" and "la", and derived from the diatonic scale. These six sounds are distributed to six, spatially distributed, speakers. Thus, we compare a BCI paradigm using beeps with another BCI paradigm using tones on the diatonic scale, when the stimuli are spatially distributed. Although no significant differences are found between the ERPs, the HLM pattern performs better than the DS pattern: the online accuracy achieved with the HLM pattern is significantly higher than that achieved with the DS pattern (p = 0.0028). PMID:27275376

  7. Auditory short-term memory in the primate auditory cortex.

    Science.gov (United States)

    Scott, Brian H; Mishkin, Mortimer

    2016-06-01

    Sounds are fleeting, and assembling the sequence of inputs at the ear into a coherent percept requires auditory memory across various time scales. Auditory short-term memory comprises at least two components: an active ׳working memory' bolstered by rehearsal, and a sensory trace that may be passively retained. Working memory relies on representations recalled from long-term memory, and their rehearsal may require phonological mechanisms unique to humans. The sensory component, passive short-term memory (pSTM), is tractable to study in nonhuman primates, whose brain architecture and behavioral repertoire are comparable to our own. This review discusses recent advances in the behavioral and neurophysiological study of auditory memory with a focus on single-unit recordings from macaque monkeys performing delayed-match-to-sample (DMS) tasks. Monkeys appear to employ pSTM to solve these tasks, as evidenced by the impact of interfering stimuli on memory performance. In several regards, pSTM in monkeys resembles pitch memory in humans, and may engage similar neural mechanisms. Neural correlates of DMS performance have been observed throughout the auditory and prefrontal cortex, defining a network of areas supporting auditory STM with parallels to that supporting visual STM. These correlates include persistent neural firing, or a suppression of firing, during the delay period of the memory task, as well as suppression or (less commonly) enhancement of sensory responses when a sound is repeated as a ׳match' stimulus. Auditory STM is supported by a distributed temporo-frontal network in which sensitivity to stimulus history is an intrinsic feature of auditory processing. This article is part of a Special Issue entitled SI: Auditory working memory. PMID:26541581

  8. Auditory short-term memory in the primate auditory cortex.

    Science.gov (United States)

    Scott, Brian H; Mishkin, Mortimer

    2016-06-01

    Sounds are fleeting, and assembling the sequence of inputs at the ear into a coherent percept requires auditory memory across various time scales. Auditory short-term memory comprises at least two components: an active ׳working memory' bolstered by rehearsal, and a sensory trace that may be passively retained. Working memory relies on representations recalled from long-term memory, and their rehearsal may require phonological mechanisms unique to humans. The sensory component, passive short-term memory (pSTM), is tractable to study in nonhuman primates, whose brain architecture and behavioral repertoire are comparable to our own. This review discusses recent advances in the behavioral and neurophysiological study of auditory memory with a focus on single-unit recordings from macaque monkeys performing delayed-match-to-sample (DMS) tasks. Monkeys appear to employ pSTM to solve these tasks, as evidenced by the impact of interfering stimuli on memory performance. In several regards, pSTM in monkeys resembles pitch memory in humans, and may engage similar neural mechanisms. Neural correlates of DMS performance have been observed throughout the auditory and prefrontal cortex, defining a network of areas supporting auditory STM with parallels to that supporting visual STM. These correlates include persistent neural firing, or a suppression of firing, during the delay period of the memory task, as well as suppression or (less commonly) enhancement of sensory responses when a sound is repeated as a ׳match' stimulus. Auditory STM is supported by a distributed temporo-frontal network in which sensitivity to stimulus history is an intrinsic feature of auditory processing. This article is part of a Special Issue entitled SI: Auditory working memory.

  9. Tactile feedback improves auditory spatial localization

    OpenAIRE

    Gori, Monica; Vercillo, Tiziana; Sandini, Giulio; Burr, David

    2014-01-01

    Our recent studies suggest that congenitally blind adults have severely impaired thresholds in an auditory spatial bisection task, pointing to the importance of vision in constructing complex auditory spatial maps (Gori et al., 2014). To explore strategies that may improve the auditory spatial sense in visually impaired people, we investigated the impact of tactile feedback on spatial auditory localization in 48 blindfolded sighted subjects. We measured auditory spatial bisection thresholds b...

  10. Tactile feedback improves auditory spatial localization

    OpenAIRE

    Monica eGori; Tiziana eVercillo; Giulio eSandini; David eBurr

    2014-01-01

    Our recent studies suggest that congenitally blind adults have severely impaired thresholds in an auditory spatial-bisection task, pointing to the importance of vision in constructing complex auditory spatial maps (Gori et al., 2014). To explore strategies that may improve the auditory spatial sense in visually impaired people, we investigated the impact of tactile feedback on spatial auditory localization in 48 blindfolded sighted subjects. We measured auditory spatial bisection thresholds b...

  11. Auditory Neuropathy - A Case of Auditory Neuropathy after Hyperbilirubinemia

    Directory of Open Access Journals (Sweden)

    Maliheh Mazaher Yazdi

    2007-12-01

    Full Text Available Background and Aim: Auditory neuropathy is an hearing disorder in which peripheral hearing is normal, but the eighth nerve and brainstem are abnormal. By clinical definition, patient with this disorder have normal OAE, but exhibit an absent or severely abnormal ABR. Auditory neuropathy was first reported in the late 1970s as different methods could identify discrepancy between absent ABR and present hearing threshold. Speech understanding difficulties are worse than can be predicted from other tests of hearing function. Auditory neuropathy may also affect vestibular function. Case Report: This article presents electrophysiological and behavioral data from a case of auditory neuropathy in a child with normal hearing after bilirubinemia in a 5 years follow-up. Audiological findings demonstrate remarkable changes after multidisciplinary rehabilitation. Conclusion: auditory neuropathy may involve damage to the inner hair cells-specialized sensory cells in the inner ear that transmit information about sound through the nervous system to the brain. Other causes may include faulty connections between the inner hair cells and the nerve leading from the inner ear to the brain or damage to the nerve itself. People with auditory neuropathy have OAEs response but absent ABR and hearing loss threshold that can be permanent, get worse or get better.

  12. Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification

    Science.gov (United States)

    Treder, M. S.; Purwins, H.; Miklody, D.; Sturm, I.; Blankertz, B.

    2014-04-01

    Objective. Polyphonic music (music consisting of several instruments playing in parallel) is an intuitive way of embedding multiple information streams. The different instruments in a musical piece form concurrent information streams that seamlessly integrate into a coherent and hedonistically appealing entity. Here, we explore polyphonic music as a novel stimulation approach for use in a brain-computer interface. Approach. In a multi-streamed oddball experiment, we had participants shift selective attention to one out of three different instruments in music audio clips. Each instrument formed an oddball stream with its own specific standard stimuli (a repetitive musical pattern) and oddballs (deviating musical pattern). Main results. Contrasting attended versus unattended instruments, ERP analysis shows subject- and instrument-specific responses including P300 and early auditory components. The attended instrument can be classified offline with a mean accuracy of 91% across 11 participants. Significance. This is a proof of concept that attention paid to a particular instrument in polyphonic music can be inferred from ongoing EEG, a finding that is potentially relevant for both brain-computer interface and music research.

  13. Electroencephalogram-based brain-computer interface system%基于脑电的脑-机接口系统

    Institute of Scientific and Technical Information of China (English)

    任亚莉

    2011-01-01

    BACKGROUND: Brain-computer interfaces (BCI) provide a direct communication and control channel for sending messages and instructions from brain to external computers or other electronic devices. Using the non-muscular channel, subjects with severe neuromuscular dysfunction can directly express their thought and manipulate the external devices without using human language and actions. This greatly enhances the ability of these subjects to manage external event and improves their quality of life.OBJECTIVE: To summarize latest research advances and problems in the BCI and discuss the research direction of BCI.METHODS: The literatures on BCI were searched on the PubMed database published from January 1990 to December 2009 with the key words "brain-computer interface, rehabilitation" in English. In addition, the related articles were also searched on CNKI-KNS published between January 1990 and December 2009 with the key words "brain-computer interface, signal processing and electroencephalography" in Chinese.RESULTS AND CONCLUSION: Researches of BCI system is still at a developing stage. There are some disadvantages, such as low rate of communications instability, especially for algorithm improvement and selection of signal processing.%背景:脑-机接口是在人脑与计算机或其它电子设备之间建立的直接交流和控制通道,通过这种通道,人就可以直接通过脑来表达想法或操纵设备,而不需要语言或动作,这可以有效增强身体严重残疾的患者与外界交流或控制外部环境的能力,以提高患者的生活质量.目的:总结近年来国内外有关脑-机接口系统的研究进展及存在的问题,探讨该领域进一步发展的方向.方法:应用计算机检索PubMed数据库中1990-01/2009-12脑-机接口方面的文献,检索词"brain-computer interface,Rehabilitatian",并限定语言为English;同时检索CNKI-KNS 1990-01/2009-12脑-机接口方面的文献,检索词为"脑-机接口,信号处理,脑电

  14. A Two-Stage State Recognition Method for Asynchronous SSVEP-Based Brain-Computer Interface System

    Institute of Scientific and Technical Information of China (English)

    ZHANG Zimu; DENG Zhidong

    2013-01-01

    A two-stage state recognition method is proposed for asynchronous SSVEP (steady-state visual evoked potential) based brain-computer interface (SBCI) system.The two-stage method is composed of the idle state (IS) detection and control state (CS) discrimination modules.Based on blind source separation and continuous wavelet transform techniques,the proposed method integrates functions of multi-electrode spatial filtering and feature extraction.In IS detection module,a method using the ensemble IS feature is proposed.In CS discrimination module,the ensemble CS feature is designed as feature vector for control intent classification.Further,performance comparisons are investigated among our IS detection module and other existing ones.Also the experimental results validate the satisfactory performance of our CS discrimination module.

  15. Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment

    Directory of Open Access Journals (Sweden)

    Burke R

    2005-01-01

    Full Text Available This paper presents the application of an effective EEG-based brain-computer interface design for binary control in a visually elaborate immersive 3D game. The BCI uses the steady-state visual evoked potential (SSVEP generated in response to phase-reversing checkerboard patterns. Two power-spectrum estimation methods were employed for feature extraction in a series of offline classification tests. Both methods were also implemented during real-time game play. The performance of the BCI was found to be robust to distracting visual stimulation in the game and relatively consistent across six subjects, with 41 of 48 games successfully completed. For the best performing feature extraction method, the average real-time control accuracy across subjects was 89%. The feasibility of obtaining reliable control in such a visually rich environment using SSVEPs is thus demonstrated and the impact of this result is discussed.

  16. Real-time fMRI brain computer interfaces: self-regulation of single brain regions to networks.

    Science.gov (United States)

    Ruiz, Sergio; Buyukturkoglu, Korhan; Rana, Mohit; Birbaumer, Niels; Sitaram, Ranganatha

    2014-01-01

    With the advent of brain computer interfaces based on real-time fMRI (rtfMRI-BCI), the possibility of performing neurofeedback based on brain hemodynamics has become a reality. In the early stage of the development of this field, studies have focused on the volitional control of activity in circumscribed brain regions. However, based on the understanding that the brain functions by coordinated activity of spatially distributed regions, there have recently been further developments to incorporate real-time feedback of functional connectivity and spatio-temporal patterns of brain activity. The present article reviews the principles of rtfMRI neurofeedback, its applications, benefits and limitations. A special emphasis is given to the discussion of novel developments that have enabled the use of this methodology to achieve self-regulation of the functional connectivity between different brain areas and of distributed brain networks, anticipating new and exciting applications for cognitive neuroscience and for the potential alleviation of neuropsychiatric disorders.

  17. Brain-computer interface driven functional electrical stimulation system for overground walking in spinal cord injury participant.

    Science.gov (United States)

    King, Christine E; Wang, Po T; McCrimmon, Colin M; Chou, Cathy C Y; Do, An H; Nenadic, Zoran

    2014-01-01

    The current treatment for ambulation after spinal cord injury (SCI) is to substitute the lost behavior with a wheelchair; however, this can result in many co-morbidities. Thus, novel solutions for the restoration of walking, such as brain-computer interfaces (BCI) and functional electrical stimulation (FES) devices, have been sought. This study reports on the first electroencephalogram (EEG) based BCI-FES system for overground walking, and its performance assessment in an individual with paraplegia due to SCI. The results revealed that the participant was able to purposefully operate the system continuously in real time. If tested in a larger population of SCI individuals, this system may pave the way for the restoration of overground walking after SCI.

  18. An open-source and cross-platform framework for Brain Computer Interface-guided robotic arm control.

    Science.gov (United States)

    Kubben, Pieter L; Pouratian, Nader

    2012-01-01

    Brain Computer Interfaces (BCIs) have focused on several areas, of which motor substitution has received particular interest. Whereas open-source BCI software is available to facilitate cost-effective collaboration between research groups, it mainly focuses on communication and computer control. We developed an open-source and cross-platform framework, which works with cost-effective equipment that allows researchers to enter the field of BCI-based motor substitution without major investments upfront. It is based on the C++ programming language and the Qt framework, and offers a separate class for custom MATLAB/Simulink scripts. It has been tested using a 14-channel wireless electroencephalography (EEG) device and a low-cost robotic arm that offers 5° of freedom. The software contains four modules to control the robotic arm, one of which receives input from the EEG device. Strengths, current limitations, and future developments will be discussed. PMID:23372966

  19. Using brain-computer interfaces to overcome the extinction of goal-directed thinking in minimally conscious state patients.

    Science.gov (United States)

    Liberati, Giulia; Birbaumer, Niels

    2012-08-01

    Minimally conscious state (MCS) is a condition of severely altered consciousness, in which patients appear to be wakeful and exhibit fluctuating but reproducible signs of awareness. MCS patients do not respond and are therefore dependent on others. In agreement with the embodied cognition assumption that motor actions influence our cognition, the absence of movement and the decrease in consequences for any type of covert or overt response may cause an extinction of goal-directed thinking. Brain-computer interfaces, which allow a direct output without muscular involvement, may be used to promote goal-directed thinking by allowing the performance of spatial and motor imagery tasks and could facilitate the interaction of MCS patients with their environment, possibly regaining some degree of communication and autonomy.

  20. Testing the Self-Similarity Exponent to Feature Extraction in Motor Imagery Based Brain Computer Interface Systems

    Science.gov (United States)

    Rodríguez-Bermúdez, Germán; Sánchez-Granero, Miguel Ángel; García-Laencina, Pedro J.; Fernández-Martínez, Manuel; Serna, José; Roca-Dorda, Joaquín

    2015-12-01

    A Brain Computer Interface (BCI) system is a tool not requiring any muscle action to transmit information. Acquisition, preprocessing, feature extraction (FE), and classification of electroencephalograph (EEG) signals constitute the main steps of a motor imagery BCI. Among them, FE becomes crucial for BCI, since the underlying EEG knowledge must be properly extracted into a feature vector. Linear approaches have been widely applied to FE in BCI, whereas nonlinear tools are not so common in literature. Thus, the main goal of this paper is to check whether some Hurst exponent and fractal dimension based estimators become valid indicators to FE in motor imagery BCI. The final results obtained were not optimal as expected, which may be due to the fact that the nature of the analyzed EEG signals in these motor imagery tasks were not self-similar enough.

  1. Single-trial detection of visual evoked potentials by common spatial patterns and wavelet filtering for brain-computer interface.

    Science.gov (United States)

    Tu, Yiheng; Huang, Gan; Hung, Yeung Sam; Hu, Li; Hu, Yong; Zhang, Zhiguo

    2013-01-01

    Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems as input signals conveying a subject's intention. A fast and reliable single-trial ERP detection method can be used to develop a BCI system with both high speed and high accuracy. However, most of single-trial ERP detection methods are developed for offline EEG analysis and thus have a high computational complexity and need manual operations. Therefore, they are not applicable to practical BCI systems, which require a low-complexity and automatic ERP detection method. This work presents a joint spatial-time-frequency filter that combines common spatial patterns (CSP) and wavelet filtering (WF) for improving the signal-to-noise (SNR) of visual evoked potentials (VEP), which can lead to a single-trial ERP-based BCI.

  2. An Auditory BCI System for Assisting CRS-R Behavioral Assessment in Patients with Disorders of Consciousness

    Science.gov (United States)

    Xiao, Jun; Xie, Qiuyou; He, Yanbin; Yu, Tianyou; Lu, Shenglin; Huang, Ningmeng; Yu, Ronghao; Li, Yuanqing

    2016-09-01

    The Coma Recovery Scale-Revised (CRS-R) is a consistent and sensitive behavioral assessment standard for disorders of consciousness (DOC) patients. However, the CRS-R has limitations due to its dependence on behavioral markers, which has led to a high rate of misdiagnosis. Brain-computer interfaces (BCIs), which directly detect brain activities without any behavioral expression, can be used to evaluate a patient’s state. In this study, we explored the application of BCIs in assisting CRS-R assessments of DOC patients. Specifically, an auditory passive EEG-based BCI system with an oddball paradigm was proposed to facilitate the evaluation of one item of the auditory function scale in the CRS-R – the auditory startle. The results obtained from five healthy subjects validated the efficacy of the BCI system. Nineteen DOC patients participated in the CRS-R and BCI assessments, of which three patients exhibited no responses in the CRS-R assessment but were responsive to auditory startle in the BCI assessment. These results revealed that a proportion of DOC patients who have no behavioral responses in the CRS-R assessment can generate neural responses, which can be detected by our BCI system. Therefore, the proposed BCI may provide more sensitive results than the CRS-R and thus assist CRS-R behavioral assessments.

  3. Perception and cognition of cues used in synchronous brain-computer interfaces modify electroencephalographic patterns of control tasks

    Directory of Open Access Journals (Sweden)

    Luz Maria eAlonso Valerdi

    2015-11-01

    Full Text Available A motor imagery (MI based brain computer interface (BCI is a system that enables humans to interact with their environment by translating their brain signals into control commands for a target device. In particular, synchronous BCI systems make use of cues to trigger the motor activity of interest. So far, it has been shown that Electroencephalographic (EEG patterns before and after cue onset can reveal the user cognitive state and enhance the discrimination of MI related control tasks. However, there has been no detailed investigation of the nature of those EEG patterns. We, therefore, propose to study the cue effects on MI related control tasks by selecting EEG patterns that best discriminate such control tasks, and analysing where those patterns are coming from. The study was carried out under two methods: standard and all-embracing. The standard method was based on sources (recording sites, frequency bands and time windows, where the modulation of EEG signals due to motor activity is typically detected. The all-embracing method included a wider variety of sources, where not only motor activity is reflected. The findings of this study showed that the classification accuracy of MI related control tasks did not depend on the type of cue in use. However, EEG patterns which best differentiated those control tasks emerged from sources well defined by the perception and cognition of the cue in use. An implication of this study is the possibility of obtaining different control commands that could be detected with the same accuracy. Since different cues trigger control tasks that yield similar classification accuracies, and those control tasks produce EEG patterns differentiated by the cue nature, this leads to accelerate the brain-computer communication by having a wider variety of detectable control commands. This is an important issue for Neuroergonimcs research because neural activity could not only be used to monitor the human mental state as is

  4. 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.

  5. Auditory Processing Disorder in Children

    Science.gov (United States)

    ... free publications Find organizations Related Topics Auditory Neuropathy Autism Spectrum Disorder: Communication Problems in Children Dysphagia Quick ... NIH… Turning Discovery Into Health ® National Institute on Deafness and Other Communication Disorders 31 Center Drive, MSC ...

  6. Auditory Processing Disorder (For Parents)

    Science.gov (United States)

    ... and school. A positive, realistic attitude and healthy self-esteem in a child with APD can work wonders. And kids with APD can go on to ... Parents MORE ON THIS TOPIC Auditory Processing Disorder Special ...

  7. Psychology of auditory perception.

    Science.gov (United States)

    Lotto, Andrew; Holt, Lori

    2011-09-01

    Audition is often treated as a 'secondary' sensory system behind vision in the study of cognitive science. In this review, we focus on three seemingly simple perceptual tasks to demonstrate the complexity of perceptual-cognitive processing involved in everyday audition. After providing a short overview of the characteristics of sound and their neural encoding, we present a description of the perceptual task of segregating multiple sound events that are mixed together in the signal reaching the ears. Then, we discuss the ability to localize the sound source in the environment. Finally, we provide some data and theory on how listeners categorize complex sounds, such as speech. In particular, we present research on how listeners weigh multiple acoustic cues in making a categorization decision. One conclusion of this review is that it is time for auditory cognitive science to be developed to match what has been done in vision in order for us to better understand how humans communicate with speech and music. WIREs Cogni Sci 2011 2 479-489 DOI: 10.1002/wcs.123 For further resources related to this article, please visit the WIREs website. PMID:26302301

  8. On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification.

    Science.gov (United States)

    Yang, Huijuan; Sakhavi, Siavash; Ang, Kai Keng; Guan, Cuntai

    2015-01-01

    Learning the deep structures and unknown correlations is important for the detection of motor imagery of EEG signals (MI-EEG). This study investigates the use of convolutional neural networks (CNNs) for the classification of multi-class MI-EEG signals. Augmented common spatial pattern (ACSP) features are generated based on pair-wise projection matrices, which covers various frequency ranges. We propose a frequency complementary feature map selection (FCMS) scheme by constraining the dependency among frequency bands. Experiments are conducted on BCI competition IV dataset IIa with 9 subjects. Averaged cross-validation accuracy of 68.45% and 69.27% is achieved for FCMS and all feature maps, respectively, which is significantly higher (4.53% and 5.34%) than random map selection and higher (1.44% and 2.26%) than filter-bank CSP (FBCSP). The results demonstrate that the CNNs are capable of learning discriminant, deep structure features for EEG classification without relying on the handcrafted features. PMID:26736829

  9. Classification of fault location and the degree of performance degradation of a rolling bearing based on an improved hyper-sphere-structured multi-class support vector machine

    Science.gov (United States)

    Wang, Yujing; Kang, Shouqiang; Jiang, Yicheng; Yang, Guangxue; Song, Lixin; Mikulovich, V. I.

    2012-05-01

    Effective classification of a rolling bearing fault location and especially its degree of performance degradation provides an important basis for appropriate fault judgment and processing. Two methods are introduced to extract features of the rolling bearing vibration signal—one combining empirical mode decomposition (EMD) with the autoregressive model, whose model parameters and variances of the remnant can be obtained using the Yule-Walker or Ulrych-Clayton method, and the other combining EMD with singular value decomposition. Feature vector matrices obtained are then regarded as the input of the improved hyper-sphere-structured multi-class support vector machine (HSSMC-SVM) for classification. Thereby, multi-status intelligent diagnosis of normal rolling bearings and faulty rolling bearings at different locations and the degrees of performance degradation of the faulty rolling bearings can be achieved simultaneously. Experimental results show that EMD combined with singular value decomposition and the improved HSSMC-SVM intelligent method requires less time and has a higher recognition rate.

  10. Multi-class methodology to determine pesticides and mycotoxins in green tea and royal jelly supplements by liquid chromatography coupled to Orbitrap high resolution mass spectrometry.

    Science.gov (United States)

    Martínez-Domínguez, Gerardo; Romero-González, Roberto; Garrido Frenich, Antonia

    2016-04-15

    A multi-class methodology was developed to determine pesticides and mycotoxins in food supplements. The extraction was performed using acetonitrile acidified with formic acid (1%, v/v). Different clean-up sorbents were tested, and the best results were obtained using C18 and zirconium oxide for green tea and royal jelly, respectively. The compounds were determined using ultra high performance liquid chromatography (UHPLC) coupled to Exactive-Orbitrap high resolution mass spectrometry (HRMS). The recovery rates obtained were between 70% and 120% for most of the compounds studied with a relative standard deviation <25%, at three different concentration levels. The calculated limits of quantification (LOQ) were <10 μg/kg. The method was applied to green tea (10) and royal jelly (8) samples. Nine (eight of green tea and one of royal jelly) samples were found to be positive for pesticides at concentrations ranging from 10.6 (cinosulfuron) to 47.9 μg/kg (paclobutrazol). The aflatoxin B1 (5.4 μg/kg) was also found in one of the green tea samples.

  11. Conditional associative learning examined in a paralyzed patient with amyotrophic lateral sclerosis using brain-computer interface technology

    Directory of Open Access Journals (Sweden)

    Birbaumer N

    2008-11-01

    Full Text Available Abstract Background Brain-computer interface methodology based on self-regulation of slow-cortical potentials (SCPs of the EEG (electroencephalogram was used to assess conditional associative learning in one severely paralyzed, late-stage ALS patient. After having been taught arbitrary stimulus relations, he was evaluated for formation of equivalence classes among the trained stimuli. Methods A monitor presented visual information in two targets. The method of teaching was matching to sample. Three types of stimuli were presented: signs (A, colored disks (B, and geometrical shapes (C. The sample was one type, and the choice was between two stimuli from another type. The patient used his SCP to steer a cursor to one of the targets. A smiley was presented as a reward when he hit the correct target. The patient was taught A-B and B-C (sample – comparison matching with three stimuli of each type. Tests for stimulus equivalence involved the untaught B-A, C-B, A-C, and C-A relations. An additional test was discrimination between all three stimuli of one equivalence class presented together versus three unrelated stimuli. The patient also had sessions with identity matching using the same stimuli. Results The patient showed high accuracy, close to 100%, on identity matching and could therefore discriminate the stimuli and control the cursor correctly. Acquisition of A-B matching took 11 sessions (of 70 trials each and had to be broken into simpler units before he could learn it. Acquisition of B-C matching took two sessions. The patient passed all equivalence class tests at 90% or higher. Conclusion The patient may have had a deficit in acquisition of the first conditional association of signs and colored disks. In contrast, the patient showed clear evidence that A-B and B-C training had resulted in formation of equivalence classes. The brain-computer interface technology combined with the matching to sample method is a useful way to assess various

  12. Neural Correlates of an Auditory Afterimage in Primary Auditory Cortex

    OpenAIRE

    Noreña, A. J.; Eggermont, J. J.

    2003-01-01

    The Zwicker tone (ZT) is defined as an auditory negative afterimage, perceived after the presentation of an appropriate inducer. Typically, a notched noise (NN) with a notch width of 1/2 octave induces a ZT with a pitch falling in the frequency range of the notch. The aim of the present study was to find potential neural correlates of the ZT in the primary auditory cortex of ketamine-anesthetized cats. Responses of multiunits were recorded simultaneously with two 8-electrode arrays during 1 s...

  13. Auditory Hallucinations in Acute Stroke

    Directory of Open Access Journals (Sweden)

    Yair Lampl

    2005-01-01

    Full Text Available Auditory hallucinations are uncommon phenomena which can be directly caused by acute stroke, mostly described after lesions of the brain stem, very rarely reported after cortical strokes. The purpose of this study is to determine the frequency of this phenomenon. In a cross sectional study, 641 stroke patients were followed in the period between 1996–2000. Each patient underwent comprehensive investigation and follow-up. Four patients were found to have post cortical stroke auditory hallucinations. All of them occurred after an ischemic lesion of the right temporal lobe. After no more than four months, all patients were symptom-free and without therapy. The fact the auditory hallucinations may be of cortical origin must be taken into consideration in the treatment of stroke patients. The phenomenon may be completely reversible after a couple of months.

  14. Adaptation in the auditory system: an overview

    OpenAIRE

    David ePérez-González; Malmierca, Manuel S.

    2014-01-01

    The early stages of the auditory system need to preserve the timing information of sounds in order to extract the basic features of acoustic stimuli. At the same time, different processes of neuronal adaptation occur at several levels to further process the auditory information. For instance, auditory nerve fiber responses already experience adaptation of their firing rates, a type of response that can be found in many other auditory nuclei and may be useful for emphasizing the onset of the s...

  15. Brain computer interface technology research review%脑机接口技术研究综述

    Institute of Scientific and Technical Information of China (English)

    李勃

    2013-01-01

    脑机接口技术(brain computer interface,BCI)不依赖于常规大脑信息输出通路,该技术建立了一种直接的信息交流和控制通道,为人脑和外界之间提供了一种全新的交互方式.简要介绍了BCI技术的定义和基本组成及发展现状,并对皮层慢电位、视觉诱发电位、眼动产生的α波、P300电位和基于运动想象的μ节律及β波5种脑机接口技术的研究方向作了简要阐述,最后指出目前BCI研究面临的挑战及未来的应用前景.

  16. A Modular Framework for EEG Web Based Binary Brain Computer Interfaces to Recover Communication Abilities in Impaired People.

    Science.gov (United States)

    Placidi, Giuseppe; Petracca, Andrea; Spezialetti, Matteo; Iacoviello, Daniela

    2016-01-01

    A Brain Computer Interface (BCI) allows communication for impaired people unable to express their intention with common channels. Electroencephalography (EEG) represents an effective tool to allow the implementation of a BCI. The present paper describes a modular framework for the implementation of the graphic interface for binary BCIs based on the selection of symbols in a table. The proposed system is also designed to reduce the time required for writing text. This is made by including a motivational tool, necessary to improve the quality of the collected signals, and by containing a predictive module based on the frequency of occurrence of letters in a language, and of words in a dictionary. The proposed framework is described in a top-down approach through its modules: signal acquisition, analysis, classification, communication, visualization, and predictive engine. The framework, being modular, can be easily modified to personalize the graphic interface to the needs of the subject who has to use the BCI and it can be integrated with different classification strategies, communication paradigms, and dictionaries/languages. The implementation of a scenario and some experimental results on healthy subjects are also reported and discussed: the modules of the proposed scenario can be used as a starting point for further developments, and application on severely disabled people under the guide of specialized personnel.

  17. Reliability-based automatic repeat request for short code modulation visual evoked potentials in brain computer interfaces.

    Science.gov (United States)

    Sato, Jun-Ichi; Washizawa, Yoshikazu

    2015-08-01

    We propose two methods to improve code modulation visual evoked potential brain computer interfaces (cVEP BCIs). Most of BCIs average brain signals from several trials in order to improve the classification performance. The number of averaging defines the trade-off between input speed and accuracy, and the optimal averaging number depends on individual, signal acquisition system, and so forth. Firstly, we propose a novel dynamic method to estimate the averaging number for cVEP BCIs. The proposed method is based on the automatic repeat request (ARQ) that is used in communication systems. The existing cVEP BCIs employ rather longer code, such as 63-bit M-sequence. The code length also defines the trade-off between input speed and accuracy. Since the reliability of the proposed BCI can be controlled by the proposed ARQ method, we introduce shorter codes, 32-bit M-sequence and the Kasami-sequence. Thanks to combine the dynamic averaging number estimation method and the shorter codes, the proposed system exhibited higher information transfer rate compared to existing cVEP BCIs.

  18. Towards an optimization of stimulus parameters for brain-computer interfaces based on steady state visual evoked potentials.

    Directory of Open Access Journals (Sweden)

    Anna Duszyk

    Full Text Available Efforts to construct an effective brain-computer interface (BCI system based on Steady State Visual Evoked Potentials (SSVEP commonly focus on sophisticated mathematical methods for data analysis. The role of different stimulus features in evoking strong SSVEP is less often considered and the knowledge on the optimal stimulus properties is still fragmentary. The goal of this study was to provide insight into the influence of stimulus characteristics on the magnitude of SSVEP response. Five stimuli parameters were tested: size, distance, colour, shape, and presence of a fixation point in the middle of each flickering field. The stimuli were presented on four squares on LCD screen, with each square highlighted by LEDs flickering with different frequencies. Brighter colours and larger dimensions of flickering fields resulted in a significantly stronger SSVEP response. The distance between stimulation fields and the presence or absence of the fixation point had no significant effect on the response. Contrary to a popular belief, these results suggest that absence of the fixation point does not reduce the magnitude of SSVEP response. However, some parameters of the stimuli such as colour and the size of the flickering field play an important role in evoking SSVEP response, which indicates that stimuli rendering is an important factor in building effective SSVEP based BCI systems.

  19. Current challenges facing the translation of brain computer interfaces from preclinical trials to use in human patients

    Directory of Open Access Journals (Sweden)

    Maxwell D. Murphy

    2016-01-01

    Full Text Available Current research in brain computer interface (BCI technology is advancing beyond preclinical studies, with trials beginning in human patients. To date, these trials have been carried out with several different types of recording interfaces. The success of these devices has varied widely, but different factors such as the level of invasiveness, timescale of recorded information, and ability to maintain stable functionality of the device over a long period of time all must be considered in addition to accuracy in decoding intent when assessing the most practical type of device moving forward. Here, we discuss various approaches to BCIs, distinguishing between devices focusing on control of operations extrinsic to the subject (e.g., prosthetic limbs, computer cursors and those focusing on control of operations intrinsic to the brain (e.g. using stimulation or external feedback, including closed-loop or adaptive devices. In this discussion, we consider the current challenges facing the translation of various types of BCI technology to eventual human application.

  20. Current Challenges Facing the Translation of Brain Computer Interfaces from Preclinical Trials to Use in Human Patients.

    Science.gov (United States)

    Murphy, Maxwell D; Guggenmos, David J; Bundy, David T; Nudo, Randolph J

    2015-01-01

    Current research in brain computer interface (BCI) technology is advancing beyond preclinical studies, with trials beginning in human patients. To date, these trials have been carried out with several different types of recording interfaces. The success of these devices has varied widely, but different factors such as the level of invasiveness, timescale of recorded information, and ability to maintain stable functionality of the device over a long period of time all must be considered in addition to accuracy in decoding intent when assessing the most practical type of device moving forward. Here, we discuss various approaches to BCIs, distinguishing between devices focusing on control of operations extrinsic to the subject (e.g., prosthetic limbs, computer cursors) and those focusing on control of operations intrinsic to the brain (e.g., using stimulation or external feedback), including closed-loop or adaptive devices. In this discussion, we consider the current challenges facing the translation of various types of BCI technology to eventual human application.

  1. On the need to better specify the concept of control in brain-computer-interfaces/neurofeedback research

    Directory of Open Access Journals (Sweden)

    Guilherme eWood

    2014-09-01

    Full Text Available Aiming at a better specification of the concept of control in brain-computer-interfaces (BCI and neurofeedback research, we propose to distinguish self-control of brain activity from the broader concept of BCI control, since the first describes a neurocognitive phenomenon and is only one of the many components of BCI control. Based on this distinction, we developed a framework based on dual-processes theory that describes the cognitive determinants of self-control of brain activity as the interplay of automatic vs. controlled information processing. Further, we distinguish between cognitive processes that are necessary and sufficient to achieve a given level of self-control of brain activity and those which are not. We discuss that those cognitive processes which are not necessary for the learning process can hamper self-control because they cannot be completely turned-off at any time. This framework aims at a comprehensive description of the cognitive determinants of the acquisition of self-control of brain activity underlying those classes of BCI which require the user to achieve regulation of brain activity as well as neurofeedback learning.

  2. Estimating cognitive load during self-regulation of brain activity and neurofeedback with therapeutic brain-computer interfaces

    Directory of Open Access Journals (Sweden)

    Robert eBauer

    2015-02-01

    Full Text Available Neurofeedback training with brain-computer interfaces is currently studied in a variety of neurological and neuropsychiatric conditions to reduce disorder-specific symptoms. For this purpose, a variety of classification algorithms have been explored to distinguish different brain states. These neural states, e.g. self-regulated brain activity versus rest, are separated by setting a threshold parameter. Measures such as the maximum classification accuracy have been introduced to evaluate the performance of these algorithms. Interestingly, the very same measures are often used to estimate the subject’s ability to perform brain self-regulation. This is surprising, as the goal of improving the tool that differentiates brain states is different from the aim of optimizing neurofeedback for the subject who performs brain self-regulation. For the latter, knowledge about mental resources and work load is essential to adapt the difficulty of the intervention.In this context, we apply an analytical method and provide empirical data to determine the zone of proximal development as a measure of a subject’s cognitive resources and the instructional efficacy of neurofeedback. This approach is based on a reconsideration of item-response theory and cognitive load theory for instructional design, and combines them with the classification accuracy curve as a measure of BCI performance.

  3. A semi-supervised support vector machine approach for parameter setting in motor imagery-based brain computer interfaces

    Science.gov (United States)

    Long, Jinyi; Yu, Zhuliang

    2010-01-01

    Parameter setting plays an important role for improving the performance of a brain computer interface (BCI). Currently, parameters (e.g. channels and frequency band) are often manually selected. It is time-consuming and not easy to obtain an optimal combination of parameters for a BCI. In this paper, motor imagery-based BCIs are considered, in which channels and frequency band are key parameters. First, a semi-supervised support vector machine algorithm is proposed for automatically selecting a set of channels with given frequency band. Next, this algorithm is extended for joint channel-frequency selection. In this approach, both training data with labels and test data without labels are used for training a classifier. Hence it can be used in small training data case. Finally, our algorithms are applied to a BCI competition data set. Our data analysis results show that these algorithms are effective for selection of frequency band and channels when the training data set is small. PMID:21886673

  4. Brain Painting: first evaluation of a new brain-computer interface application with ALS patients and healthy volunteers.

    Directory of Open Access Journals (Sweden)

    Jana I. Muenssinger

    2010-11-01

    Full Text Available Brain-computer interfaces (BCI enable paralyzed patients to communicate; however, up to date, no creative expression was possible. The current study investigated the accuracy and user friendliness of P300-Brain Painting, a new BCI-application developed to paint pictures using brain activity only. Two different versions of the P300-Brain Painting application were tested: A coloured matrix tested by a group of ALS-patients (n = 3 and healthy participants (n = 10, and a black & white matrix tested by healthy participants (n = 10. The three ALS-patients achieved high accuracies; two of them reaching above 89% accuracy. In healthy subjects, a comparison between the P300-Brain Painting application (coloured matrix and the P300-Spelling application revealed significantly lower accuracy and P300 amplitudes for the P300-Brain Painting application. This drop in accuracy and P300 amplitudes was not found when comparing the P300-Spelling application to an adapted, black & white matrix of the P300-Brain Painting application. By employing a black and white matrix, the accuracy of the P300-Brain Painting application was significantly enhanced and reached the accuracy of the P300-Spelling application. ALS patients greatly enjoyed P300-Brain Painting and were able to use the application with the same accuracy as healthy subjects. P300-Brain Painting enables paralyzed patients to express themselves creatively and to participate in the prolific society through exhibitions.

  5. Aggregation of sparse linear discriminant analyses for event-related potential classification in brain-computer interface.

    Science.gov (United States)

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

    2014-02-01

    Two main issues for event-related potential (ERP) classification in brain-computer interface (BCI) application are curse-of-dimensionality and bias-variance tradeoff, which may deteriorate classification performance, especially with insufficient training samples resulted from limited calibration time. This study introduces an aggregation of sparse linear discriminant analyses (ASLDA) to overcome these problems. In the ASLDA, multiple sparse discriminant vectors are learned from differently l1-regularized least-squares regressions by exploiting the equivalence between LDA and least-squares regression, and are subsequently aggregated to form an ensemble classifier, which could not only implement automatic feature selection for dimensionality reduction to alleviate curse-of-dimensionality, but also decrease the variance to improve generalization capacity for new test samples. Extensive investigation and comparison are carried out among the ASLDA, the ordinary LDA and other competing ERP classification algorithms, based on different three ERP datasets. Experimental results indicate that the ASLDA yields better overall performance for single-trial ERP classification when insufficient training samples are available. This suggests the proposed ASLDA is promising for ERP classification in small sample size scenario to improve the practicability of BCI. PMID:24344691

  6. Brain-Computer Interface Controlled Cyborg: Establishing a Functional Information Transfer Pathway from Human Brain to Cockroach Brain

    Science.gov (United States)

    2016-01-01

    An all-chain-wireless brain-to-brain system (BTBS), which enabled motion control of a cyborg cockroach via human brain, was developed in this work. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) was used in this system for recognizing human motion intention and an optimization algorithm was proposed in SSVEP to improve online performance of the BCI. The cyborg cockroach was developed by surgically integrating a portable microstimulator that could generate invasive electrical nerve stimulation. Through Bluetooth communication, specific electrical pulse trains could be triggered from the microstimulator by BCI commands and were sent through the antenna nerve to stimulate the brain of cockroach. Serial experiments were designed and conducted to test overall performance of the BTBS with six human subjects and three cockroaches. The experimental results showed that the online classification accuracy of three-mode BCI increased from 72.86% to 78.56% by 5.70% using the optimization algorithm and the mean response accuracy of the cyborgs using this system reached 89.5%. Moreover, the results also showed that the cyborg could be navigated by the human brain to complete walking along an S-shape track with the success rate of about 20%, suggesting the proposed BTBS established a feasible functional information transfer pathway from the human brain to the cockroach brain. PMID:26982717

  7. Toward a brain-computer interface for Alzheimer's disease patients by combining classical conditioning and brain state classification.

    Science.gov (United States)

    Liberati, Giulia; Dalboni da Rocha, Josué Luiz; van der Heiden, Linda; Raffone, Antonino; Birbaumer, Niels; Olivetti Belardinelli, Marta; Sitaram, Ranganatha

    2012-01-01

    Brain-computer interfaces (BCIs) provide alternative methods for communicating and acting on the world, since messages or commands are conveyed from the brain to an external device without using the normal output pathways of peripheral nerves and muscles. Alzheimer's disease (AD) patients in the most advanced stages, who have lost the ability to communicate verbally, could benefit from a BCI that may allow them to convey basic thoughts (e.g., "yes" and "no") and emotions. There is currently no report of such research, mostly because the cognitive deficits in AD patients pose serious limitations to the use of traditional BCIs, which are normally based on instrumental learning and require users to self-regulate their brain activation. Recent studies suggest that not only self-regulated brain signals, but also involuntary signals, for instance related to emotional states, may provide useful information about the user, opening up the path for so-called "affective BCIs". These interfaces do not necessarily require users to actively perform a cognitive task, and may therefore be used with patients who are cognitively challenged. In the present hypothesis paper, we propose a paradigm shift from instrumental learning to classical conditioning, with the aim of discriminating "yes" and "no" thoughts after associating them to positive and negative emotional stimuli respectively. This would represent a first step in the development of a BCI that could be used by AD patients, lending a new direction not only for communication, but also for rehabilitation and diagnosis.

  8. A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem

    Science.gov (United States)

    McClay, Wilbert A.; Yadav, Nancy; Ozbek, Yusuf; Haas, Andy; Attias, Hagaii T.; Nagarajan, Srikantan S.

    2015-01-01

    Ecumenically, the fastest growing segment of Big Data is human biology-related data and the annual data creation is on the order of zetabytes. The implications are global across industries, of which the treatment of brain related illnesses and trauma could see the most significant and immediate effects. The next generation of health care IT and sensory devices are acquiring and storing massive amounts of patient related data. An innovative Brain-Computer Interface (BCI) for interactive 3D visualization is presented utilizing the Hadoop Ecosystem for data analysis and storage. The BCI is an implementation of Bayesian factor analysis algorithms that can distinguish distinct thought actions using magneto encephalographic (MEG) brain signals. We have collected data on five subjects yielding 90% positive performance in MEG mid- and post-movement activity. We describe a driver that substitutes the actions of the BCI as mouse button presses for real-time use in visual simulations. This process has been added into a flight visualization demonstration. By thinking left or right, the user experiences the aircraft turning in the chosen direction. The driver components of the BCI can be compiled into any software and substitute a user’s intent for specific keyboard strikes or mouse button presses. The BCI’s data analytics of a subject’s MEG brainwaves and flight visualization performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse. PMID:26437432

  9. [A wireless smart home system based on brain-computer interface of steady state visual evoked potential].

    Science.gov (United States)

    Zhao, Li; Xing, Xiao; Guo, Xuhong; Liu, Zehua; He, Yang

    2014-10-01

    Brain-computer interface (BCI) system is a system that achieves communication and control among humans and computers and other electronic equipment with the electroencephalogram (EEG) signals. This paper describes the working theory of the wireless smart home system based on the BCI technology. We started to get the steady-state visual evoked potential (SSVEP) using the single chip microcomputer and the visual stimulation which composed by LED lamp to stimulate human eyes. Then, through building the power spectral transformation on the LabVIEW platform, we processed timely those EEG signals under different frequency stimulation so as to transfer them to different instructions. Those instructions could be received by the wireless transceiver equipment to control the household appliances and to achieve the intelligent control towards the specified devices. The experimental results showed that the correct rate for the 10 subjects reached 100%, and the control time of average single device was 4 seconds, thus this design could totally achieve the original purpose of smart home system.

  10. Abnormal neural connectivity in schizophrenia and fMRI-brain computer interface as a potential therapeutic approach

    Directory of Open Access Journals (Sweden)

    Sergio eRuiz

    2013-03-01

    Full Text Available Considering that single locations of structural and functional abnormalities are insufficient to explain the diverse psychopathology of schizophrenia, new models have postulated that the impairments associated with the disease arise from a failure to integrate the activity of local and distributed neural circuits: the abnormal neural connectivity hypothesis. In the last years, new evidence coming from neuroimaging have supported and expanded this theory. However, despite the increasing evidence that schizophrenia is a disorder of neural connectivity, so far there are no treatments that have shown to produce a significant change in brain connectivity, or that have been specifically designed to alleviate this problem. Brain-Computer Interfaces based on real-time functional Magnetic Resonance Imaging (fMRI-BCI are novel techniques that have allowed subjects to achieve self-regulation of circumscribed brain regions. In recent studies, experiments with this technology have resulted in new findings suggesting that this methodology could be used to train subjects to enhance brain connectivity, and therefore could potentially be used as a therapeutic tool in mental disorders including schizophrenia.The present article summarizes the findings coming from hemodynamics-based neuroimaging that support the abnormal connectivity hypothesis in schizophrenia, and discusses a new approach that could address this problem.

  11. A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem.

    Science.gov (United States)

    McClay, Wilbert A; Yadav, Nancy; Ozbek, Yusuf; Haas, Andy; Attias, Hagaii T; Nagarajan, Srikantan S

    2015-01-01

    Ecumenically, the fastest growing segment of Big Data is human biology-related data and the annual data creation is on the order of zetabytes. The implications are global across industries, of which the treatment of brain related illnesses and trauma could see the most significant and immediate effects. The next generation of health care IT and sensory devices are acquiring and storing massive amounts of patient related data. An innovative Brain-Computer Interface (BCI) for interactive 3D visualization is presented utilizing the Hadoop Ecosystem for data analysis and storage. The BCI is an implementation of Bayesian factor analysis algorithms that can distinguish distinct thought actions using magneto encephalographic (MEG) brain signals. We have collected data on five subjects yielding 90% positive performance in MEG mid- and post-movement activity. We describe a driver that substitutes the actions of the BCI as mouse button presses for real-time use in visual simulations. This process has been added into a flight visualization demonstration. By thinking left or right, the user experiences the aircraft turning in the chosen direction. The driver components of the BCI can be compiled into any software and substitute a user's intent for specific keyboard strikes or mouse button presses. The BCI's data analytics OPEN ACCESS Brain. Sci. 2015, 5 420 of a subject's MEG brainwaves and flight visualization performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse. PMID:26437432

  12. 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.

  13. 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.

  14. A co-adaptive sensory motor rhythms Brain-Computer Interface based on common spatial patterns and Random Forest.

    Science.gov (United States)

    Schwarz, Andreas; Scherer, Reinhold; Steyrl, David; Faller, Josef; Muller-Putz, Gernot R

    2015-08-01

    Sensorimotor rhythm (SMR) based Brain-Computer Interfaces (BCI) typically require lengthy user training. This can be exhausting and fatiguing for the user as data collection may be monotonous and typically without any feedback for user motivation. Hence new ways to reduce user training and improve performance are needed. We recently introduced a two class motor imagery BCI system which continuously adapted with increasing run-time to the brain patterns of the user. The system was designed to provide visual feedback to the user after just five minutes. The aim of the current work was to improve user-specific online adaptation, which was expected to lead to higher performances. To maximize SMR discrimination, the method of filter-bank common spatial patterns (fbCSP) and Random Forest (RF) classifier were combined. In a supporting online study, all volunteers performed significantly better than chance. Overall peak accuracy of 88.6 ± 6.1 (SD) % was reached, which significantly exceeded the performance of our previous system by 13%. Therefore, we consider this system the next step towards fully auto-calibrating motor imagery BCIs. PMID:26736445

  15. Brain-computer interface game applications for combined neurofeedback and biofeedback treatment for children on the autism spectrum

    Directory of Open Access Journals (Sweden)

    Elisabeth V C Friedrich

    2014-07-01

    Full Text Available Individuals with Autism Spectrum Disorder (ASD show deficits in social and communicative skills, including imitation, empathy, and shared attention, as well as restricted interests and repetitive patterns of behaviors. Evidence for and against the idea that dysfunctions in the mirror neuron system are involved in imitation and could be one underlying cause for ASD is discussed in this review. Neurofeedback interventions have reduced symptoms in children with ASD by self-regulation of brain rhythms. However, cortical deficiencies are not the only cause of these symptoms. Peripheral physiological activity, such as the heart rate, is closely linked to neurophysiological signals and associated with social engagement. Therefore, a combined approach targeting the interplay between brain, body and behavior could be more effective. Brain-computer interface applications for combined neurofeedback and biofeedback treatment for children with ASD are currently nonexistent. To facilitate their use, we have designed an innovative game that includes social interactions and provides neural- and body-based feedback that corresponds directly to the underlying significance of the trained signals as well as to the behavior that is reinforced.

  16. Paralyzed subject controls telepresence mobile robot using novel sEMG brain-computer interface: case study.

    Science.gov (United States)

    Lyons, Kenneth R; Joshi, Sanjay S

    2013-06-01

    Here we demonstrate the use of a new singlesignal surface electromyography (sEMG) brain-computer interface (BCI) to control a mobile robot in a remote location. Previous work on this BCI has shown that users are able to perform cursor-to-target tasks in two-dimensional space using only a single sEMG signal by continuously modulating the signal power in two frequency bands. Using the cursor-to-target paradigm, targets are shown on the screen of a tablet computer so that the user can select them, commanding the robot to move in different directions for a fixed distance/angle. A Wifi-enabled camera transmits video from the robot's perspective, giving the user feedback about robot motion. Current results show a case study with a C3-C4 spinal cord injury (SCI) subject using a single auricularis posterior muscle site to navigate a simple obstacle course. Performance metrics for operation of the BCI as well as completion of the telerobotic command task are developed. It is anticipated that this noninvasive and mobile system will open communication opportunities for the severely paralyzed, possibly using only a single sensor. PMID:24187246

  17. 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.

  18. Usability and Performance Measure of a Consumer-grade Brain Computer Interface System for Environmental Control by Neurological Patients

    Directory of Open Access Journals (Sweden)

    Farzin Deravi

    2015-07-01

    Full Text Available With the increasing incidence and prevalence of chronic brain injury patients and the current financial constraints in healthcare budgets, there is a need for a more intelligent way to realise the current practice of neuro-rehabilitation service provision. Brain-computer Interface (BCI systems have the potential to address this issue to a certain extent only if carefully designed research can demonstrate that these systems are accurate, safe, cost-effective, are able to increase patient/carer satisfaction and enhance their quality of life. Therefore, one of the objectives of the proposed study was to examine whether participants (patients with brain injury and a sample of reference population were able to use a low cost BCI system (Emotiv EPOC to interact with a computer and to communicate via spelling words. Patients participated in the study did not have prior experience in using BCI headsets so as to measure the user experience in the first-exposure to BCI training. To measure emotional arousal of participants we used an ElectroDermal Activity Sensor (Qsensor by Affectiva. For the signal processing and feature extraction of imagery controls the Cognitive Suite of Emotiv's Control Panel was used. Our study reports the key findings based on data obtained from a group of patients and a sample reference population and presents the implications for the design and development of a BCI system for communication and control. The study also evaluates the performance of the system when used practically in context of an acute clinical environment

  19. The Development of control system via Brain Computer Interface (BCI - Functional Electrical Stimulation (FES for paraplegic subject

    Directory of Open Access Journals (Sweden)

    K. A. A. Rahman

    2012-12-01

    Full Text Available Brain is known to be one of the powerful systems in human body because of its ability to give command and communicate throughout the body. The spinal cord is the pathway for impulses from the brain to the body as well as from the body to the brain. However, the bounty of this pathway could be lost due to spinal cord injury (SCI and that results in a loss of function especially mobility. A combination of Brain Computer Interface (BCI and Functional Electrical Stimulation (FES is among one of the technique to regain the mobility function of human body which will be the focused area of this research. In this study, Electroencephalography (EEG system will be used to capture the brain signal which will then drive the FES. A paraplegic subject will be involved in this study. The subject will be required to move the knee joint with involvement few muscle contraction. Overall, in this paper the combination of BCI-FES methods for development of rehabilitation system will be proposed. From this preliminary study, it can be summarized that the combination between BCI and FES potentially would provide a better rehabilitation system for SCI patient in comparison to the conventional FES system.

  20. Paralyzed subject controls telepresence mobile robot using novel sEMG brain-computer interface: case study.

    Science.gov (United States)

    Lyons, Kenneth R; Joshi, Sanjay S

    2013-06-01

    Here we demonstrate the use of a new singlesignal surface electromyography (sEMG) brain-computer interface (BCI) to control a mobile robot in a remote location. Previous work on this BCI has shown that users are able to perform cursor-to-target tasks in two-dimensional space using only a single sEMG signal by continuously modulating the signal power in two frequency bands. Using the cursor-to-target paradigm, targets are shown on the screen of a tablet computer so that the user can select them, commanding the robot to move in different directions for a fixed distance/angle. A Wifi-enabled camera transmits video from the robot's perspective, giving the user feedback about robot motion. Current results show a case study with a C3-C4 spinal cord injury (SCI) subject using a single auricularis posterior muscle site to navigate a simple obstacle course. Performance metrics for operation of the BCI as well as completion of the telerobotic command task are developed. It is anticipated that this noninvasive and mobile system will open communication opportunities for the severely paralyzed, possibly using only a single sensor.

  1. Analogue mouse pointer control via an online steady state visual evoked potential (SSVEP) brain-computer interface

    Science.gov (United States)

    Wilson, John J.; Palaniappan, Ramaswamy

    2011-04-01

    The steady state visual evoked protocol has recently become a popular paradigm in brain-computer interface (BCI) applications. Typically (regardless of function) these applications offer the user a binary selection of targets that perform correspondingly discrete actions. Such discrete control systems are appropriate for applications that are inherently isolated in nature, such as selecting numbers from a keypad to be dialled or letters from an alphabet to be spelled. However motivation exists for users to employ proportional control methods in intrinsically analogue tasks such as the movement of a mouse pointer. This paper introduces an online BCI in which control of a mouse pointer is directly proportional to a user's intent. Performance is measured over a series of pointer movement tasks and compared to the traditional discrete output approach. Analogue control allowed subjects to move the pointer faster to the cued target location compared to discrete output but suffers more undesired movements overall. Best performance is achieved when combining the threshold to movement of traditional discrete techniques with the range of movement offered by proportional control.

  2. 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

    2014-01-01

    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.

  3. Brain-Computer Interface Controlled Cyborg: Establishing a Functional Information Transfer Pathway from Human Brain to Cockroach Brain.

    Science.gov (United States)

    Li, Guangye; Zhang, Dingguo

    2016-01-01

    An all-chain-wireless brain-to-brain system (BTBS), which enabled motion control of a cyborg cockroach via human brain, was developed in this work. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) was used in this system for recognizing human motion intention and an optimization algorithm was proposed in SSVEP to improve online performance of the BCI. The cyborg cockroach was developed by surgically integrating a portable microstimulator that could generate invasive electrical nerve stimulation. Through Bluetooth communication, specific electrical pulse trains could be triggered from the microstimulator by BCI commands and were sent through the antenna nerve to stimulate the brain of cockroach. Serial experiments were designed and conducted to test overall performance of the BTBS with six human subjects and three cockroaches. The experimental results showed that the online classification accuracy of three-mode BCI increased from 72.86% to 78.56% by 5.70% using the optimization algorithm and the mean response accuracy of the cyborgs using this system reached 89.5%. Moreover, the results also showed that the cyborg could be navigated by the human brain to complete walking along an S-shape track with the success rate of about 20%, suggesting the proposed BTBS established a feasible functional information transfer pathway from the human brain to the cockroach brain. PMID:26982717

  4. Influence of P300 latency jitter on event related potential-based brain-computer interface performance

    Science.gov (United States)

    Aricò, P.; Aloise, F.; Schettini, F.; Salinari, S.; Mattia, D.; Cincotti, F.

    2014-06-01

    Objective. Several ERP-based brain-computer interfaces (BCIs) that can be controlled even without eye movements (covert attention) have been recently proposed. However, when compared to similar systems based on overt attention, they displayed significantly lower accuracy. In the current interpretation, this is ascribed to the absence of the contribution of short-latency visual evoked potentials (VEPs) in the tasks performed in the covert attention modality. This study aims to investigate if this decrement (i) is fully explained by the lack of VEP contribution to the classification accuracy; (ii) correlates with lower temporal stability of the single-trial P300 potentials elicited in the covert attention modality. Approach. We evaluated the latency jitter of P300 evoked potentials in three BCI interfaces exploiting either overt or covert attention modalities in 20 healthy subjects. The effect of attention modality on the P300 jitter, and the relative contribution of VEPs and P300 jitter to the classification accuracy have been analyzed. Main results. The P300 jitter is higher when the BCI is controlled in covert attention. Classification accuracy negatively correlates with jitter. Even disregarding short-latency VEPs, overt-attention BCI yields better accuracy than covert. When the latency jitter is compensated offline, the difference between accuracies is not significant. Significance. The lower temporal stability of the P300 evoked potential generated during the tasks performed in covert attention modality should be regarded as the main contributing explanation of lower accuracy of covert-attention ERP-based BCIs.

  5. Event-related potentials in a moving matrix modification of the P300 brain-computer interface paradigm.

    Science.gov (United States)

    Shishkin, Sergei L; Ganin, Ilya P; Kaplan, Alexander Ya

    2011-06-01

    In the standard design of the brain-computer interfaces (BCI) based on the P300 component of the event-related potentials (ERP), target and non-target stimuli are presented at fixed positions in a motionless matrix. Can we let this matrix be moving (e.g., if attached to a robot) without loosing the efficiency of BCI? We assessed changes of the positive peak at Pz in the time interval 300-500 ms after the stimulus onset (P300) and the negative peak at the occipital electrodes in the range 140-240 ms (N1), both important for the operation of the P300 BCI, during fixating a target cell of a moving matrix in healthy participants (n=12). N1 amplitude in the difference (target-non-target) waveforms decreased with the velocity, although remained high (M=-4.3, SD=2.1) even at highest velocity (20°/s). In general, the amplitudes and latencies of these ERP components were remarkably stable in studied types of matrix movement and all velocities of horizontal movement (5, 10 and 20°/s) comparing to matrix in fixed position. These data suggest that, for the users controlling their gaze, the P300 BCI design can be extended to modifications requiring stimuli matrix motion.

  6. A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem

    Directory of Open Access Journals (Sweden)

    Wilbert A. McClay

    2015-09-01

    Full Text Available Ecumenically, the fastest growing segment of Big Data is human biology-related data and the annual data creation is on the order of zetabytes. The implications are global across industries, of which the treatment of brain related illnesses and trauma could see the most significant and immediate effects. The next generation of health care IT and sensory devices are acquiring and storing massive amounts of patient related data. An innovative Brain-Computer Interface (BCI for interactive 3D visualization is presented utilizing the Hadoop Ecosystem for data analysis and storage. The BCI is an implementation of Bayesian factor analysis algorithms that can distinguish distinct thought actions using magneto encephalographic (MEG brain signals. We have collected data on five subjects yielding 90% positive performance in MEG mid- and post-movement activity. We describe a driver that substitutes the actions of the BCI as mouse button presses for real-time use in visual simulations. This process has been added into a flight visualization demonstration. By thinking left or right, the user experiences the aircraft turning in the chosen direction. The driver components of the BCI can be compiled into any software and substitute a user’s intent for specific keyboard strikes or mouse button presses. The BCI’s data analytics OPEN ACCESS Brain. Sci. 2015, 5 420 of a subject’s MEG brainwaves and flight visualization performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse.

  7. A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem.

    Science.gov (United States)

    McClay, Wilbert A; Yadav, Nancy; Ozbek, Yusuf; Haas, Andy; Attias, Hagaii T; Nagarajan, Srikantan S

    2015-09-30

    Ecumenically, the fastest growing segment of Big Data is human biology-related data and the annual data creation is on the order of zetabytes. The implications are global across industries, of which the treatment of brain related illnesses and trauma could see the most significant and immediate effects. The next generation of health care IT and sensory devices are acquiring and storing massive amounts of patient related data. An innovative Brain-Computer Interface (BCI) for interactive 3D visualization is presented utilizing the Hadoop Ecosystem for data analysis and storage. The BCI is an implementation of Bayesian factor analysis algorithms that can distinguish distinct thought actions using magneto encephalographic (MEG) brain signals. We have collected data on five subjects yielding 90% positive performance in MEG mid- and post-movement activity. We describe a driver that substitutes the actions of the BCI as mouse button presses for real-time use in visual simulations. This process has been added into a flight visualization demonstration. By thinking left or right, the user experiences the aircraft turning in the chosen direction. The driver components of the BCI can be compiled into any software and substitute a user's intent for specific keyboard strikes or mouse button presses. The BCI's data analytics OPEN ACCESS Brain. Sci. 2015, 5 420 of a subject's MEG brainwaves and flight visualization performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse.

  8. 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.

  9. Attention modulations of posterior alpha as a control signal for two-dimensional brain-computer interfaces.

    Science.gov (United States)

    van Gerven, Marcel; Jensen, Ole

    2009-04-30

    Research on brain-computer interfaces (BCIs) is gaining strong interest. This is motivated by BCIs being applicable for helping disabled, for gaming, and as a tool in cognitive neuroscience. Often, motor imagery is used to produce (binary) control signals. However, finding other types of control signals that allow the discrimination of multiple classes would help to increase the applicability of BCIs. We have investigated if modulation of posterior alpha activity by means of covert spatial attention in two dimensions can be reliably classified in single trials. Magnetoencephalography (MEG) data were collected for 15 subjects who were engaged in a task where they covertly had to visually attend left, right, up or down during a period of 2500 ms. We then classified the trials using support vector machines. The four orientations of covert attention could be reliably classified up to a maximum of 69% correctly classified trials (25% chance level) without the need for lengthy and burdensome subject training. Low classification performance in some subjects was explained by a low alpha signal. These findings support the case that modulation of alpha activity by means of covert spatial attention is promising as a control signal for a two-dimensional BCI. PMID:19428515

  10. 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.

  11. A user-friendly SSVEP-based brain-computer interface using a time-domain classifier

    Science.gov (United States)

    Luo, An; Sullivan, Thomas J.

    2010-04-01

    We introduce a user-friendly steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system. Single-channel EEG is recorded using a low-noise dry electrode. Compared to traditional gel-based multi-sensor EEG systems, a dry sensor proves to be more convenient, comfortable and cost effective. A hardware system was built that displays four LED light panels flashing at different frequencies and synchronizes with EEG acquisition. The visual stimuli have been carefully designed such that potential risk to photosensitive people is minimized. We describe a novel stimulus-locked inter-trace correlation (SLIC) method for SSVEP classification using EEG time-locked to stimulus onsets. We studied how the performance of the algorithm is affected by different selection of parameters. Using the SLIC method, the average light detection rate is 75.8% with very low error rates (an 8.4% false positive rate and a 1.3% misclassification rate). Compared to a traditional frequency-domain-based method, the SLIC method is more robust (resulting in less annoyance to the users) and is also suitable for irregular stimulus patterns.

  12. Towards Brain-Computer Interface Control of a 6-Degree-of-Freedom Robotic Arm Using Dry EEG Electrodes

    Directory of Open Access Journals (Sweden)

    Alexander Astaras

    2013-01-01

    Full Text Available Introduction. Development of a robotic arm that can be operated using an exoskeletal position sensing harness as well as a dry electrode brain-computer interface headset. Design priorities comprise an intuitive and immersive user interface, fast and smooth movement, portability, and cost minimization. Materials and Methods. A robotic arm prototype capable of moving along 6 degrees of freedom has been developed, along with an exoskeletal position sensing harness which was used to control it. Commercially available dry electrode BCI headsets were evaluated. A particular headset model has been selected and is currently being integrated into the hybrid system. Results and Discussion. The combined arm-harness system has been successfully tested and met its design targets for speed, smooth movement, and immersive control. Initial tests verify that an operator using the system can perform pick and place tasks following a rather short learning curve. Further evaluation experiments are planned for the integrated BCI-harness hybrid setup. Conclusions. It is possible to design a portable robotic arm interface comparable in size, dexterity, speed, and fluidity to the human arm at relatively low cost. The combined system achieved its design goals for intuitive and immersive robotic control and is currently being further developed into a hybrid BCI system for comparative experiments.

  13. Control of a Wheelchair in an Indoor Environment Based on a Brain-Computer Interface and Automated Navigation.

    Science.gov (United States)

    Zhang, Rui; Li, Yuanqing; Yan, Yongyong; Zhang, Hao; Wu, Shaoyu; Yu, Tianyou; Gu, Zhenghui

    2016-01-01

    The concept of controlling a wheelchair using brain signals is promising. However, the continuous control of a wheelchair based on unstable and noisy electroencephalogram signals is unreliable and generates a significant mental burden for the user. A feasible solution is to integrate a brain-computer interface (BCI) with automated navigation techniques. This paper presents a brain-controlled intelligent wheelchair with the capability of automatic navigation. Using an autonomous navigation system, candidate destinations and waypoints are automatically generated based on the existing environment. The user selects a destination using a motor imagery (MI)-based or P300-based BCI. According to the determined destination, the navigation system plans a short and safe path and navigates the wheelchair to the destination. During the movement of the wheelchair, the user can issue a stop command with the BCI. Using our system, the mental burden of the user can be substantially alleviated. Furthermore, our system can adapt to changes in the environment. Two experiments based on MI and P300 were conducted to demonstrate the effectiveness of our system.

  14. Suspect screening and target quantification of multi-class pharmaceuticals in surface water based on large-volume injection liquid chromatography and time-of-flight mass spectrometry.

    Science.gov (United States)

    Vergeynst, Leendert; Van Langenhove, Herman; Joos, Pieter; Demeestere, Kristof

    2014-04-01

    The ever-growing number of emerging micropollutants such as pharmaceuticals requests rapid and sensitive full-spectrum analytical techniques. Time-of-flight high-resolution mass spectrometry (TOF-HRMS) is a promising alternative for the state-of-the-art tandem mass spectrometry instruments because of its ability to simultaneously screen for a virtually unlimited number of suspect analytes and to perform target quantification. The challenge for such suspect screening is to develop a strategy, which minimizes the false-negative rate without restraining numerous false-positives. At the same time, omitting laborious sample enrichment through large-volume injection ultra-performance liquid chromatography (LVI-UPLC) avoids selective preconcentration. A suspect screening strategy was developed using LVI-UPLC-TOF-MS aiming the detection of 69 multi-class pharmaceuticals in surface water without the a priori availability of analytical standards. As a novel approach, the screening takes into account the signal-intensity-dependent accurate mass error of TOF-MS, hereby restraining 95 % of the measured suspect pharmaceuticals present in surface water. Application on five Belgian river water samples showed the potential of the suspect screening approach, as exemplified by a false-positive rate not higher than 15 % and given that 30 out of 37 restrained suspect compounds were confirmed by the retention time of analytical standards. Subsequently, this paper discusses the validation and applicability of the LVI-UPLC full-spectrum HRMS method for target quantification of the 69 pharmaceuticals in surface water. Analysis of five Belgian river water samples revealed the occurrence of 17 pharmaceuticals in a concentration range of 17 ng L(-1) up to 3.1 μg L(-1). PMID:24633561

  15. Development of a sensitive and reliable high performance liquid chromatography method with fluorescence detection for high-throughput analysis of multi-class mycotoxins in Coix seed.

    Science.gov (United States)

    Kong, Wei-Jun; Li, Jun-Yuan; Qiu, Feng; Wei, Jian-He; Xiao, Xiao-He; Zheng, Yuguo; Yang, Mei-Hua

    2013-10-17

    As an edible and medicinal plant, Coix seed is readily contaminated by more than one group of mycotoxins resulting in potential risk to human health. A reliable and sensitive method has been developed to determine seven mycotoxins (aflatoxins B1, B2, G1, G2, zearalenone, α-zearalenol, and β-zearalenol) simultaneously in 10 batches of Coix seed marketed in China. The method is based on a rapid ultrasound-assisted solid-liquid extraction (USLE) using methanol/water (80/20) followed by immunoaffinity column (IAC) clean-up, on-line photochemical derivatization (PCD), and high performance liquid chromatography coupled with fluorescence detection (HPLC-FLD). Careful optimization of extraction, clean-up, separation and detection conditions was accomplished to increase sample throughput and to attain rapid separation and sensitive detection. Method validation was performed by analyzing samples spiked at three different concentrations for the seven mycotoxins. Recoveries were from 73.5% to 107.3%, with relative standard deviations (RSDs) lower than 7.7%. The intra- and inter-day precisions, expressed as RSDs, were lower than 4% for all studied analytes. Limits of detection and quantification ranged from 0.01 to 50.2 μg kg(-1), and from 0.04 to 125.5 μg kg(-1), respectively, which were below the tolerance levels for mycotoxins set by the European Union. Samples that tested positive were further analyzed by HPLC tandem electrospray ionization mass spectrometry for confirmatory purposes. This is the first application of USLE-IAC-HPLC-PCD-FLD for detecting the occurrence of multi-class mycotoxins in Coix seed. PMID:24091376

  16. Analysis of multi-class preservatives in leave-on and rinse-off cosmetics by matrix solid-phase dispersion.

    Science.gov (United States)

    Sanchez-Prado, Lucia; Alvarez-Rivera, Gerardo; Lamas, J Pablo; Lores, Marta; Garcia-Jares, Carmen; Llompart, Maria

    2011-12-01

    Matrix solid-phase extraction has been successfully applied for the determination of multi-class preservatives in a wide variety of cosmetic samples including rinse-off and leave-on products. After extraction, derivatization with acetic anhydride, and gas chromatography-mass spectrometry analysis were performed. Optimization studies were done on real non-spiked and spiked leave-on and rinse-off cosmetic samples. The selection of the most suitable extraction conditions was made using statistical tools such as ANOVA, as well as factorial experimental designs. The final optimized conditions were common for both groups of cosmetics and included the dispersion of the sample with Florisil (1:4), and the elution of the MSPD column with 5 mL of hexane/acetone (1:1). After derivatization, the extract was analyzed without any further clean-up or concentration step. Accuracy, precision, linearity and detection limits were evaluated to assess the performance of the proposed method. The recovery studies on leave-on and rinse-off cosmetics gave satisfactory values (>78% for all analytes in all the samples) with an average relative standard deviation value of 4.2%. The quantification limits were well below those set by the international cosmetic regulations, making this multi-component analytical method suitable for routine control. The analysis of a broad range of cosmetics including body milk, moisturizing creams, anti-stretch marks creams, hand creams, deodorant, shampoos, liquid soaps, makeup, sun milk, hand soaps, among others, demonstrated the high use of most of the target preservatives, especially butylated hydroxytoluene, methylparaben, propylparaben, and butylparaben.

  17. Development of a sensitive and reliable high performance liquid chromatography method with fluorescence detection for high-throughput analysis of multi-class mycotoxins in Coix seed.

    Science.gov (United States)

    Kong, Wei-Jun; Li, Jun-Yuan; Qiu, Feng; Wei, Jian-He; Xiao, Xiao-He; Zheng, Yuguo; Yang, Mei-Hua

    2013-10-17

    As an edible and medicinal plant, Coix seed is readily contaminated by more than one group of mycotoxins resulting in potential risk to human health. A reliable and sensitive method has been developed to determine seven mycotoxins (aflatoxins B1, B2, G1, G2, zearalenone, α-zearalenol, and β-zearalenol) simultaneously in 10 batches of Coix seed marketed in China. The method is based on a rapid ultrasound-assisted solid-liquid extraction (USLE) using methanol/water (80/20) followed by immunoaffinity column (IAC) clean-up, on-line photochemical derivatization (PCD), and high performance liquid chromatography coupled with fluorescence detection (HPLC-FLD). Careful optimization of extraction, clean-up, separation and detection conditions was accomplished to increase sample throughput and to attain rapid separation and sensitive detection. Method validation was performed by analyzing samples spiked at three different concentrations for the seven mycotoxins. Recoveries were from 73.5% to 107.3%, with relative standard deviations (RSDs) lower than 7.7%. The intra- and inter-day precisions, expressed as RSDs, were lower than 4% for all studied analytes. Limits of detection and quantification ranged from 0.01 to 50.2 μg kg(-1), and from 0.04 to 125.5 μg kg(-1), respectively, which were below the tolerance levels for mycotoxins set by the European Union. Samples that tested positive were further analyzed by HPLC tandem electrospray ionization mass spectrometry for confirmatory purposes. This is the first application of USLE-IAC-HPLC-PCD-FLD for detecting the occurrence of multi-class mycotoxins in Coix seed.

  18. Application of Brain -computer Interface Technology in Medical Field%脑-计算机接口技术在医学领域的应用

    Institute of Scientific and Technical Information of China (English)

    赵俊龙; 贾花萍

    2015-01-01

    介绍脑-计算机接口系统的结构、工作原理,重点阐述该技术在医学领域中的应用,包括癫痫自动检测及分类、康复训练、麻醉深度检测等方面,指出脑-计算机接口技术面临的挑战。%The paper introduces the structure and working principle of brain -computer interface system, elaborates the application of the technology in medical field, including automatic detection and classification of epilepsy, rehabilitation training and anesthetic depth monitoring, pointing out the challenges brain -computer interface technology faces.

  19. Brain-computer interface

    DEFF Research Database (Denmark)

    2014-01-01

    A computer-implemented method of providing an interface between a user and a processing unit, the method comprising : presenting one or more stimuli to a user, each stimulus varying at a respective stimulation frequency, each stimulation frequency being associated with a respective user......-selectable input; receiving at least one signal indicative of brain activity of the user; and determining, from the received signal, which of the one or more stimuli the user attends to and selecting the user-selectable input associated with the stimulation frequency of the determined stimuli as being a user...

  20. Toward more intuitive brain-computer interfacing: classification of binary covert intentions using functional near-infrared spectroscopy.

    Science.gov (United States)

    Hwang, Han-Jeong; Choi, Han; Kim, Jeong-Youn; Chang, Won-Du; Kim, Do-Won; Kim, Kiwoong; Jo, Sungho; Im, Chang-Hwan

    2016-09-01

    In traditional brain-computer interface (BCI) studies, binary communication systems have generally been implemented using two mental tasks arbitrarily assigned to “yes” or “no” intentions (e.g., mental arithmetic calculation for “yes”). A recent pilot study performed with one paralyzed patient showed the possibility of a more intuitive paradigm for binary BCI communications, in which the patient’s internal yes/no intentions were directly decoded from functional near-infrared spectroscopy (fNIRS). We investigated whether such an “fNIRS-based direct intention decoding” paradigm can be reliably used for practical BCI communications. Eight healthy subjects participated in this study, and each participant was administered 70 disjunctive questions. Brain hemodynamic responses were recorded using a multichannel fNIRS device, while the participants were internally expressing “yes” or “no” intentions to each question. Different feature types, feature numbers, and time window sizes were tested to investigate optimal conditions for classifying the internal binary intentions. About 75% of the answers were correctly classified when the individual best feature set was employed (75.89% ± 1.39 and 74.08% ± 2.87 for oxygenated and deoxygenated hemoglobin responses, respectively), which was significantly higher than a random chance level (68.57% for p < 0.001). The kurtosis feature showed the highest mean classification accuracy among all feature types. The grand-averaged hemodynamic responses showed that wide brain regions are associated with the processing of binary implicit intentions. Our experimental results demonstrated that direct decoding of internal binary intention has the potential to be used for implementing more intuitive and user-friendly communication systems for patients with motor disabilities. PMID:27050535

  1. High-frequency combination coding-based steady-state visual evoked potential for brain computer interface

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Feng; Zhang, Xin; Xie, Jun; Li, Yeping; Han, Chengcheng; Lili, Li; Wang, Jing [School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049 (China); Xu, Guang-Hua [School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049 (China); State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710054 (China)

    2015-03-10

    This study presents a new steady-state visual evoked potential (SSVEP) paradigm for brain computer interface (BCI) systems. The goal of this study is to increase the number of targets using fewer stimulation high frequencies, with diminishing subject’s fatigue and reducing the risk of photosensitive epileptic seizures. The new paradigm is High-Frequency Combination Coding-Based High-Frequency Steady-State Visual Evoked Potential (HFCC-SSVEP).Firstly, we studied SSVEP high frequency(beyond 25 Hz)response of SSVEP, whose paradigm is presented on the LED. The SNR (Signal to Noise Ratio) of high frequency(beyond 40 Hz) response is very low, which is been unable to be distinguished through the traditional analysis method; Secondly we investigated the HFCC-SSVEP response (beyond 25 Hz) for 3 frequencies (25Hz, 33.33Hz, and 40Hz), HFCC-SSVEP produces n{sup n} with n high stimulation frequencies through Frequence Combination Code. Further, Animproved Hilbert-huang transform (IHHT)-based variable frequency EEG feature extraction method and a local spectrum extreme target identification algorithmare adopted to extract time-frequency feature of the proposed HFCC-SSVEP response.Linear predictions and fixed sifting (iterating) 10 time is used to overcome the shortage of end effect and stopping criterion,generalized zero-crossing (GZC) is used to compute the instantaneous frequency of the proposed SSVEP respondent signals, the improved HHT-based feature extraction method for the proposed SSVEP paradigm in this study increases recognition efficiency, so as to improve ITR and to increase the stability of the BCI system. what is more, SSVEPs evoked by high-frequency stimuli (beyond 25Hz) minimally diminish subject’s fatigue and prevent safety hazards linked to photo-induced epileptic seizures, So as to ensure the system efficiency and undamaging.This study tests three subjects in order to verify the feasibility of the proposed method.

  2. Single trial predictors for gating motor-imagery brain-computer interfaces based on sensorimotor rhythm and visual evoked potentials

    Directory of Open Access Journals (Sweden)

    Andrew eGeronimo

    2016-04-01

    Full Text Available For brain-computer interfaces (BCIs that utilize visual cues to direct the user, the neural signals extracted by the computer are representative of ongoing processes, visual evoked responses, and voluntary modulation. We proposed to use three brain signatures for predicting success on a single trial of a BCI task. The first two features, the amplitude and phase of the pre-trial mu amplitude, were chosen as a correlate for cortical excitability. The remaining feature, related to the visually evoked response to the cue, served as a possible measure of fixation and attention to the task. Of these three features, mu rhythm amplitude over the central electrodes at the time of cue presentation and to a lesser extent the single trial visual evoked response were correlated with the success on the subsequent imagery task. Despite the potential for gating trials using these features, an offline gating simulation was limited in its ability to produce an increase in device throughput. This discrepancy highlights a distinction between the identification of predictive features, and the use of this knowledge in an online BCI. Using such a system, we cannot assume that the user will respond similarly when faced with a scenario where feedback is altered by trials that are gated on a regular basis. The results of this study suggest the possibility of using individualized, pre-task neural signatures for personalized and asynchronous (self-paced BCI applications, although these effects need to be quantified in a real-time adaptive scenario in a future study.

  3. 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.

  4. Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design

    Directory of Open Access Journals (Sweden)

    Fabien eLotte

    2013-09-01

    Full Text Available While recent research on Brain-Computer Interfaces (BCI has highlighted their potential for many applications, they remain barely used outside laboratories. The main reason is their lack of robustness. Indeed, with current BCI, mental state recognition is usually slow and often incorrect. Spontaneous BCI (i.e., mental imagery-based BCI often rely on mutual learning efforts by the user and the machine, with BCI users learning to produce stable EEG patterns (spontaneous BCI control being widely acknowledged as a skill while the computer learns to automatically recognize these EEG patterns, using signal processing. Most research so far was focused on signal processing, mostly neglecting the human in the loop. However, how well the user masters the BCI skill is also a key element explaining BCI robustness. Indeed, if the user is not able to produce stable and distinct EEG patterns, then no signal processing algorithm would be able to recognize them. Unfortunately, despite the importance of BCI training protocols, they have been scarcely studied so far, and used mostly unchanged for years.In this paper, we advocate that current human training approaches for spontaneous BCI are most likely inappropriate. We notably study instructional design literature in order to identify the key requirements and guidelines for a successful training procedure that promotes a good and efficient skill learning. This literature study highlights that current spontaneous BCI user training procedures satisfy very few of these requirements and hence are likely to be suboptimal. We therefore identify the flaws in BCI training protocols according to instructional design principles, at several levels: in the instructions provided to the user, in the tasks he/she has to perform, and in the feedback provided. For each level, we propose new research directions that are theoretically expected to address some of these flaws and to help users learn the BCI skill more efficiently.

  5. Increasing session-to-session transfer in a brain-computer interface with on-site background noise acquisition

    Science.gov (United States)

    Cho, Hohyun; Ahn, Minkyu; Kim, Kiwoong; Jun, Sung Chan

    2015-12-01

    Objective. A brain-computer interface (BCI) usually requires a time-consuming training phase during which data are collected and used to generate a classifier. Because brain signals vary dynamically over time (and even over sessions), this training phase may be necessary each time the BCI system is used, which is impractical. However, the variability in background noise, which is less dependent on a control signal, may dominate the dynamics of brain signals. Therefore, we hypothesized that an understanding of variations in background noise may allow existing data to be reused by incorporating the noise characteristics into the feature extraction framework; in this way, new session data are not required each time and this increases the feasibility of the BCI systems. Approach. In this work, we collected background noise during a single, brief on-site acquisition session (approximately 3 min) immediately before a new session, and we found that variations in background noise were predictable to some extent. Then we implemented this simple session-to-session transfer strategy with a regularized spatiotemporal filter (RSTF), and we tested it with a total of 20 cross-session datasets collected over multiple days from 12 subjects. We also proposed and tested a bias correction (BC) in the RSTF. Main results. We found that our proposed session-to-session strategies yielded a slightly less or comparable performance to the conventional paradigm (each session training phase is needed with an on-site training dataset). Furthermore, using an RSTF only and an RSTF with a BC outperformed existing approaches in session-to-session transfers. Significance. We inferred from our results that, with an on-site background noise suppression feature extractor and pre-existing training data, further training time may be unnecessary.

  6. A novel Morse code-inspired method for multiclass motor imagery brain-computer interface (BCI) design.

    Science.gov (United States)

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

    2015-11-01

    Motor imagery (MI)-based brain-computer interfaces (BCIs) allow disabled individuals to control external devices voluntarily, helping us to restore lost motor functions. However, the number of control commands available in MI-based BCIs remains limited, limiting the usability of BCI systems in control applications involving multiple degrees of freedom (DOF), such as control of a robot arm. To address this problem, we developed a novel Morse code-inspired method for MI-based BCI design to increase the number of output commands. Using this method, brain activities are modulated by sequences of MI (sMI) tasks, which are constructed by alternately imagining movements of the left or right hand or no motion. The codes of the sMI task was detected from EEG signals and mapped to special commands. According to permutation theory, an sMI task with N-length allows 2 × (2(N)-1) possible commands with the left and right MI tasks under self-paced conditions. To verify its feasibility, the new method was used to construct a six-class BCI system to control the arm of a humanoid robot. Four subjects participated in our experiment and the averaged accuracy of the six-class sMI tasks was 89.4%. The Cohen's kappa coefficient and the throughput of our BCI paradigm are 0.88 ± 0.060 and 23.5bits per minute (bpm), respectively. Furthermore, all of the subjects could operate an actual three-joint robot arm to grasp an object in around 49.1s using our approach. These promising results suggest that the Morse code-inspired method could be used in the design of BCIs for multi-DOF control. PMID:26340647

  7. Motor imagery for severely motor-impaired patients: evidence for brain-computer interfacing as superior control solution.

    Directory of Open Access Journals (Sweden)

    Johannes Höhne

    Full Text Available Brain-Computer Interfaces (BCIs strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.

  8. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface

    Science.gov (United States)

    Chen, Xiaogang; Wang, Yijun; Gao, Shangkai; Jung, Tzyy-Ping; Gao, Xiaorong

    2015-08-01

    Objective. Recently, canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) due to its high efficiency, robustness, and simple implementation. However, a method with which to make use of harmonic SSVEP components to enhance the CCA-based frequency detection has not been well established. Approach. This study proposed a filter bank canonical correlation analysis (FBCCA) method to incorporate fundamental and harmonic frequency components to improve the detection of SSVEPs. A 40-target BCI speller based on frequency coding (frequency range: 8-15.8 Hz, frequency interval: 0.2 Hz) was used for performance evaluation. To optimize the filter bank design, three methods (M1: sub-bands with equally spaced bandwidths; M2: sub-bands corresponding to individual harmonic frequency bands; M3: sub-bands covering multiple harmonic frequency bands) were proposed for comparison. Classification accuracy and information transfer rate (ITR) of the three FBCCA methods and the standard CCA method were estimated using an offline dataset from 12 subjects. Furthermore, an online BCI speller adopting the optimal FBCCA method was tested with a group of 10 subjects. Main results. The FBCCA methods significantly outperformed the standard CCA method. The method M3 achieved the highest classification performance. At a spelling rate of ˜33.3 characters/min, the online BCI speller obtained an average ITR of 151.18 ± 20.34 bits min-1. Significance. By incorporating the fundamental and harmonic SSVEP components in target identification, the proposed FBCCA method significantly improves the performance of the SSVEP-based BCI, and thereby facilitates its practical applications such as high-speed spelling.

  9. P300-based brain-computer interface communication: evaluation and follow-up in amyotrophic lateral sclerosis

    Directory of Open Access Journals (Sweden)

    Stefano Silvoni

    2009-06-01

    Full Text Available Objectives: To describe results of training and one-year follow-up of brain-communication in a larger group of early and middle stage amyotrophic lateral sclerosis (ALS patients using a P300-based brain-computer interface (BCI, and to investigate the relationship between clinical status, age and BCI performance. Methods: A group of 21 ALS patients were tested with a BCI-system using two-dimensional cursor movements. A four choice visual paradigm was employed to training and test the brain-communication abilities. The task consisted of reaching with the cursor one out of four icons representing four basic needs. Five patients performed a follow-up test one year later. The clinical severity in all patients were assessed with a battery of clinical tests. A comparable control group of 9 healthy subjects was employed to investigate performance differences. Results: 19 patients and 9 healthy subjects were able to achieve good and excellent cursor movements’ control, acquiring at least communication abilities above chance level; during follow-up the patients maintained their BCI-skill. We found mild cognitive impairments in the ALS group which may be attributed to motor deficiencies, while no relevant correlation has been found between clinical data and BCI performance. A positive correlation between age and the BCI-skill in patients was found. Conclusion: Time since training acquisition and clinical status did not affect the patients brain-communication skill at early and middle stage of the disease. Significance: A brain communication tool can be used in most ALS patients at early and middle stage of the disease, before entering the locked-in stage.

  10. Massively Parallel Signal Processing using the Graphics Processing Unit for Real-Time Brain-Computer Interface Feature Extraction.

    Science.gov (United States)

    Wilson, J Adam; Williams, Justin C

    2009-01-01

    The clock speeds of modern computer processors have nearly plateaued in the past 5 years. Consequently, neural prosthetic systems that rely on processing large quantities of data in a short period of time face a bottleneck, in that it may not be possible to process all of the data recorded from an electrode array with high channel counts and bandwidth, such as electrocorticographic grids or other implantable systems. Therefore, in this study a method of using the processing capabilities of a graphics card [graphics processing unit (GPU)] was developed for real-time neural signal processing of a brain-computer interface (BCI). The NVIDIA CUDA system was used to offload processing to the GPU, which is capable of running many operations in parallel, potentially greatly increasing the speed of existing algorithms. The BCI system records many channels of data, which are processed and translated into a control signal, such as the movement of a computer cursor. This signal processing chain involves computing a matrix-matrix multiplication (i.e., a spatial filter), followed by calculating the power spectral density on every channel using an auto-regressive method, and finally classifying appropriate features for control. In this study, the first two computationally intensive steps were implemented on the GPU, and the speed was compared to both the current implementation and a central processing unit-based implementation that uses multi-threading. Significant performance gains were obtained with GPU processing: the current implementation processed 1000 channels of 250 ms in 933 ms, while the new GPU method took only 27 ms, an improvement of nearly 35 times.

  11. Massively parallel signal processing using the graphics processing unit for real-time brain-computer interface feature extraction

    Directory of Open Access Journals (Sweden)

    J. Adam Wilson

    2009-07-01

    Full Text Available The clock speeds of modern computer processors have nearly plateaued in the past five years. Consequently, neural prosthetic systems that rely on processing large quantities of data in a short period of time face a bottleneck, in that it may not be possible to process all of the data recorded from an electrode array with high channel counts and bandwidth, such as electrocorticographic grids or other implantable systems. Therefore, in this study a method of using the processing capabilities of a graphics card (GPU was developed for real-time neural signal processing of a brain-computer interface (BCI. The NVIDIA CUDA system was used to offload processing to the GPU, which is capable of running many operations in parallel, potentially greatly increasing the speed of existing algorithms. The BCI system records many channels of data, which are processed and translated into a control signal, such as the movement of a computer cursor. This signal processing chain involves computing a matrix-matrix multiplication (i.e., a spatial filter, followed by calculating the power spectral density on every channel using an auto-regressive method, and finally classifying appropriate features for control. In this study, the first two computationally-intensive steps were implemented on the GPU, and the speed was compared to both the current implementation and a CPU-based implementation that uses multi-threading. Significant performance gains were obtained with GPU processing: the current implementation processed 1000 channels in 933 ms, while the new GPU method took only 27 ms, an improvement of nearly 35 times.

  12. Context-aware brain-computer interfaces: exploring the information space of user, technical system and environment

    Science.gov (United States)

    Zander, T. O.; Jatzev, S.

    2012-02-01

    Brain-computer interface (BCI) systems are usually applied in highly controlled environments such as research laboratories or clinical setups. However, many BCI-based applications are implemented in more complex environments. For example, patients might want to use a BCI system at home, and users without disabilities could benefit from BCI systems in special working environments. In these contexts, it might be more difficult to reliably infer information about brain activity, because many intervening factors add up and disturb the BCI feature space. One solution for this problem would be adding context awareness to the system. We propose to augment the available information space with additional channels carrying information about the user state, the environment and the technical system. In particular, passive BCI systems seem to be capable of adding highly relevant context information—otherwise covert aspects of user state. In this paper, we present a theoretical framework based on general human-machine system research for adding context awareness to a BCI system. Building on that, we present results from a study on a passive BCI, which allows access to the covert aspect of user state related to the perceived loss of control. This study is a proof of concept and demonstrates that context awareness could beneficially be implemented in and combined with a BCI system or a general human-machine system. The EEG data from this experiment are available for public download at www.phypa.org. Parts of this work have already been presented in non-journal publications. This will be indicated specifically by appropriate references in the text.

  13. A novel Morse code-inspired method for multiclass motor imagery brain-computer interface (BCI) design.

    Science.gov (United States)

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

    2015-11-01

    Motor imagery (MI)-based brain-computer interfaces (BCIs) allow disabled individuals to control external devices voluntarily, helping us to restore lost motor functions. However, the number of control commands available in MI-based BCIs remains limited, limiting the usability of BCI systems in control applications involving multiple degrees of freedom (DOF), such as control of a robot arm. To address this problem, we developed a novel Morse code-inspired method for MI-based BCI design to increase the number of output commands. Using this method, brain activities are modulated by sequences of MI (sMI) tasks, which are constructed by alternately imagining movements of the left or right hand or no motion. The codes of the sMI task was detected from EEG signals and mapped to special commands. According to permutation theory, an sMI task with N-length allows 2 × (2(N)-1) possible commands with the left and right MI tasks under self-paced conditions. To verify its feasibility, the new method was used to construct a six-class BCI system to control the arm of a humanoid robot. Four subjects participated in our experiment and the averaged accuracy of the six-class sMI tasks was 89.4%. The Cohen's kappa coefficient and the throughput of our BCI paradigm are 0.88 ± 0.060 and 23.5bits per minute (bpm), respectively. Furthermore, all of the subjects could operate an actual three-joint robot arm to grasp an object in around 49.1s using our approach. These promising results suggest that the Morse code-inspired method could be used in the design of BCIs for multi-DOF control.

  14. A low-cost EEG system-based hybrid brain-computer interface for humanoid robot navigation and recognition.

    Science.gov (United States)

    Choi, Bongjae; Jo, Sungho

    2013-01-01

    This paper describes a hybrid brain-computer interface (BCI) technique that combines the P300 potential, the steady state visually evoked potential (SSVEP), and event related de-synchronization (ERD) to solve a complicated multi-task problem consisting of humanoid robot navigation and control along with object recognition using a low-cost BCI system. Our approach enables subjects to control the navigation and exploration of a humanoid robot and recognize a desired object among candidates. This study aims to demonstrate the possibility of a hybrid BCI based on a low-cost system for a realistic and complex task. It also shows that the use of a simple image processing technique, combined with BCI, can further aid in making these complex tasks simpler. An experimental scenario is proposed in which a subject remotely controls a humanoid robot in a properly sized maze. The subject sees what the surrogate robot sees through visual feedback and can navigate the surrogate robot. While navigating, the robot encounters objects located in the maze. It then recognizes if the encountered object is of interest to the subject. The subject communicates with the robot through SSVEP and ERD-based BCIs to navigate and explore with the robot, and P300-based BCI to allow the surrogate robot recognize their favorites. Using several evaluation metrics, the performances of five subjects navigating the robot were quite comparable to manual keyboard control. During object recognition mode, favorite objects were successfully selected from two to four choices. Subjects conducted humanoid navigation and recognition tasks as if they embodied the robot. Analysis of the data supports the potential usefulness of the proposed hybrid BCI system for extended applications. This work presents an important implication for the future work that a hybridization of simple BCI protocols provide extended controllability to carry out complicated tasks even with a low-cost system.

  15. On the use of brain-computer interfaces outside scientific laboratories toward an application in domotic environments.

    Science.gov (United States)

    Babiloni, F; Cincotti, F; Marciani, M; Salinari, S; Astolfi, L; Aloise, F; De Vico Fallani, F; Mattia, D

    2009-01-01

    Brain-computer interface (BCI) applications were initially designed to provide final users with special capabilities, like writing letters on a screen, to communicate with others without muscular effort. In these last few years, the BCI scientific community has been interested in bringing BCI applications outside the scientific laboratories, initially to provide useful applications in everyday life and in future in more complex environments, such as space. Recently, we implemented a control of a domestic environment realized with BCI applications. In the present chapter, we analyze the methodological approach employed to allow the interaction between subjects and domestic devices by use of noninvasive EEG recordings. In particular, we analyze whether the cortical activity estimated from noninvasive EEG recordings could be useful in detecting mental states related to imagined limb movements. We estimate cortical activity from high-resolution EEG recordings in a group of healthy subjects by using realistic head models. Such cortical activity was estimated in a region of interest associated with the subjects' Brodmann areas by use of depth-weighted minimum norm solutions. Results show that the use of the estimated cortical activity instead of unprocessed EEG improves the recognition of the mental states associated with limb-movement imagination in a group of healthy subjects. The BCI methodology here presented has been used in a group of disabled patients to give them suitable control of several electronic devices disposed in a three-room environment devoted to neurorehabilitation. Four of six patients were able to control several electronic devices in the domotic context with the BCI system, with a percentage of correct responses averaging over 63%.

  16. Advanced Technology in Brain-computer Interface%无创高通讯速率的实时脑-机接口系统

    Institute of Scientific and Technical Information of China (English)

    高上凯

    2007-01-01

    @@ 脑-机接口(brain computer interface,简称BCI)是通过实时记录人脑的脑电波,在一定程度上解读人的简单思维,并将其翻译成控制命令,来实现对计算机、家用电器、机器人等设备的控制(参见图1).

  17. 脑计算机接口技术与应用前景%Technology and Appl ication Prospect of Brain-Computer Interface

    Institute of Scientific and Technical Information of China (English)

    贾花萍; 赵俊龙

    2015-01-01

    Brain-Computer interface (BCI)technology is a communication system between human brain and computer or other electronic equipments,the system understands the people's thinking through the EEG signal record,then controls the computer,equipments,intelli-gent household or unmanned vehicles by thinking.The technology involves in neuroscience,psychology of cognitive science,rehabilitation engineering,biomedical engineering and computer science and so on.Currently,brain-computer interface system is becoming a hot re-search,the paper introduces the structure,working principle,problems and prospect of brain-computer interface system.%脑计算机接口(Brain-Computer Interface,BCI)技术是在人脑和计算机或其他电子设备之间建立通信系统,该系统通过记录人的脑电信号来了解人的思维,用思维来控制计算机,操纵设备、智能家居、无人驾驶交通工具等。该技术涉及神经科学、心理认知科学、康复工程、生物医学工程和计算机科学等多种学科。目前,脑计算机接口系统正在成为研究热点,本文介绍了脑计算机接口系统的结构、工作原理、存在问题及发展前景。

  18. Electrode replacement does not affect classification accuracy in dual-session use of a passive brain-computer interface for assessing cognitive workload

    OpenAIRE

    Estepp, Justin R.; Christensen, James C.

    2015-01-01

    The passive brain-computer interface (pBCI) framework has been shown to be a very promising construct for assessing cognitive and affective state in both individuals and teams. There is a growing body of work that focuses on solving the challenges of transitioning pBCI systems from the research laboratory environment to practical, everyday use. An interesting issue is what impact methodological variability may have on the ability to reliably identify (neuro)physiological patterns that are use...

  19. Brain-computer interfaces based on event-related potentials: toward fast, reliable and easy-to-use communication systems for people with neurodegenerative disease

    OpenAIRE

    Kaufmann, Tobias

    2013-01-01

    Objective: Brain Computer Interfaces (BCI) provide a muscle independent interaction channel making them particularly valuable for individuals with severe motor impairment. Thus, different BCI systems and applications have been proposed as assistive technology (AT) solutions for such patients. The most prominent system for communication utilizes event-related potentials (ERP) obtained from the electroencephalogram (EEG) to allow for communication on a character-by-character basis. Yet in their...

  20. Usefulness of preoperative coronary angiography and brain computed tomography in cases of coronary artery disease and cerebrovascular disease undergoing revascularization for arteriosclerosis obliterans

    Energy Technology Data Exchange (ETDEWEB)

    Sakurada, Tall; Shibata, Yoshiki [Southern Tohoku Fukushima Hospital (Japan)

    2003-05-01

    Coronary angiography and brain computed tomography were preoperatively performed to evaluate the clinical condition of coronary artery disease and cerebrovascular disease in 101 patients (mean age, 68.4 years) with revascularization for arteriosclerosis obliterans. Eighty patients had hypertension, 12 had diabetes, and 26 had hyperlipidemia. Seventy-one patients (70.3%) had coronary stenosis. Significant stenoses in major coronary artery branches were confirmed in 35 patients, including 13 patients with old myocardial infarction. Coronary artery bypass grafting and percutaneous coronary angioplasty were performed in 2 and 7 patients with critical stenosis, respectively. Of 57 patients, who underwent brain computed tomography, abnormalities were found in 52 patients (91.2%), including cortical infarction in 9, lacunar infarction in 35, and leukoaraiosis in 27 patients. During the follow-up period 13 patients died (including 3 cases of myocardial infarction and 3 cases of stroke). Actuarial survival rate at 5 years was 80.4%. The influence of ischemic heart disease and cerebrovascular disease on early and late mortality after surgical reconstruction for peripheral occlusive vascular disease is significant. Using visual diagnostic techniques, such as coronary angiography and brain computed tomography, long term survivor should be closely observed for multiple arteriosclerotic vascular diseases. (author)