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Sample records for auditory multi-class brain-computer

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

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    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. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 脑机接口中一种多类运动想象任务识别新方法%A novel recognition method of multi-class motor imagery tasks in brain computer interfaces

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    韩志军; 杨帮华; 何美燕; 刘丽

    2015-01-01

    Objective For multi-class motor imagery tasks in brain computer interface ( BCI ) , this paper presents a novel recognition method of electroencephalography ( EEG) by combining RLS-ICA-SampEn [ RLS ( recursive least-squares ) , ICA ( independent component analysis ) , SampEn ( sample entropy ) ] , multi-class CSP (common spatial patterns) and ISVM (incremental support vector machine ).Methods In the RLS-ICA-SampEn, Firstly, the ICA is used to decompose the contaminated EEG signals into independent components (IC).Then, the sample entropy is used to automatically identify the noise signal in the IC .Next, the RLS adaptive filters are applied to the identified noise in IC to remove noise further .Finally, the processed ICs are then projected back to reconstruct the noise-free EEG signals.The RLS-ICA-SampEn is used to preprocess EEG signals to get pure EEG signals , in which some noise signals can be removed .The multi-class CSP combines the CSP and the multi-band filtering technology , in which the CSP uses the ‘one versus one ’ strategy.The multi-class CSP is used to extract featuresfor pure EEG signals.The obtained features are input tothe ISVM for classification.The ‘one versus rest’ strategyis applied to classify three-class EEG signals.In order toverify the effectiveness of the proposed novel method , it iscompared with other two methods including multi CSP +ISVM(method 1), RLS-ICA +multi CSP +ISVM(method 2).Results The result shows that the recognitionaccuracy obtained by the proposed method is higher about 8% than other two methods.Conclusions Comparedwith method 1 and 2, the proposed method is better suited for the recognition of multi -class motor imagery tasksin BCI.%目的:针对脑机接口中三类运动想象任务,提出一种最小二乘法自适应滤波结合独立成分分析以及样本熵( RLS-ICA-SampEn )、多类共同空间模式( CSP )、增量式支持向量机( ISVM )相结合的脑电识别新方法,以解决

  12. 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

  13. Music and natural sounds in an auditory steady-state response based brain-computer interface to increase user acceptance.

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    Heo, Jeong; Baek, Hyun Jae; Hong, Seunghyeok; Chang, Min Hye; Lee, Jeong Su; Park, Kwang Suk

    2017-03-18

    Patients with total locked-in syndrome are conscious; however, they cannot express themselves because most of their voluntary muscles are paralyzed, and many of these patients have lost their eyesight. To improve the quality of life of these patients, there is an increasing need for communication-supporting technologies that leverage the remaining senses of the patient along with physiological signals. The auditory steady-state response (ASSR) is an electro-physiologic response to auditory stimulation that is amplitude-modulated by a specific frequency. By leveraging the phenomenon whereby ASSR is modulated by mind concentration, a brain-computer interface paradigm was proposed to classify the selective attention of the patient. In this paper, we propose an auditory stimulation method to minimize auditory stress by replacing the monotone carrier with familiar music and natural sounds for an ergonomic system. Piano and violin instrumentals were employed in the music sessions; the sounds of water streaming and cicadas singing were used in the natural sound sessions. Six healthy subjects participated in the experiment. Electroencephalograms were recorded using four electrodes (Cz, Oz, T7 and T8). Seven sessions were performed using different stimuli. The spectral power at 38 and 42Hz and their ratio for each electrode were extracted as features. Linear discriminant analysis was utilized to classify the selections for each subject. In offline analysis, the average classification accuracies with a modulation index of 1.0 were 89.67% and 87.67% using music and natural sounds, respectively. In online experiments, the average classification accuracies were 88.3% and 80.0% using music and natural sounds, respectively. Using the proposed method, we obtained significantly higher user-acceptance scores, while maintaining a high average classification accuracy.

  14. Towards user-friendly spelling with an auditory brain-computer interface: the CharStreamer paradigm.

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    Johannes Höhne

    Full Text Available Realizing the decoding of brain signals into control commands, brain-computer interfaces (BCI aim to establish an alternative communication pathway for locked-in patients. In contrast to most visual BCI approaches which use event-related potentials (ERP of the electroencephalogram, auditory BCI systems are challenged with ERP responses, which are less class-discriminant between attended and unattended stimuli. Furthermore, these auditory approaches have more complex interfaces which imposes a substantial workload on their users. Aiming for a maximally user-friendly spelling interface, this study introduces a novel auditory paradigm: "CharStreamer". The speller can be used with an instruction as simple as "please attend to what you want to spell". The stimuli of CharStreamer comprise 30 spoken sounds of letters and actions. As each of them is represented by the sound of itself and not by an artificial substitute, it can be selected in a one-step procedure. The mental mapping effort (sound stimuli to actions is thus minimized. Usability is further accounted for by an alphabetical stimulus presentation: contrary to random presentation orders, the user can foresee the presentation time of the target letter sound. Healthy, normal hearing users (n = 10 of the CharStreamer paradigm displayed ERP responses that systematically differed between target and non-target sounds. Class-discriminant features, however, varied individually from the typical N1-P2 complex and P3 ERP components found in control conditions with random sequences. To fully exploit the sequential presentation structure of CharStreamer, novel data analysis approaches and classification methods were introduced. The results of online spelling tests showed that a competitive spelling speed can be achieved with CharStreamer. With respect to user rating, it clearly outperforms a control setup with random presentation sequences.

  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. Brain computer

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    Sarah N. Abdulkader

    2015-07-01

    Full Text Available Brain computer interface technology represents a highly growing field of research with application systems. Its contributions in medical fields range from prevention to neuronal rehabilitation for serious injuries. Mind reading and remote communication have their unique fingerprint in numerous fields such as educational, self-regulation, production, marketing, security as well as games and entertainment. It creates a mutual understanding between users and the surrounding systems. This paper shows the application areas that could benefit from brain waves in facilitating or achieving their goals. We also discuss major usability and technical challenges that face brain signals utilization in various components of BCI system. Different solutions that aim to limit and decrease their effects have also been reviewed.

  18. 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

  19. A multi-class large margin classifier

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    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.

  20. 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.

  1. Robust brain-computer interfaces

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    Reuderink, Boris

    2011-01-01

    A brain-computer interface (BCI) enables direct communication from the brain to devices, bypassing the traditional pathway of peripheral nerves and muscles. Current BCIs aimed at patients require that the user invests weeks, or even months, to learn the skill to intentionally modify their brain sign

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. Hierarchical Maximum Margin Learning for Multi-Class Classification

    CERN Document Server

    Yang, Jian-Bo

    2012-01-01

    Due to myriads of classes, designing accurate and efficient classifiers becomes very challenging for multi-class classification. Recent research has shown that class structure learning can greatly facilitate multi-class learning. In this paper, we propose a novel method to learn the class structure for multi-class classification problems. The class structure is assumed to be a binary hierarchical tree. To learn such a tree, we propose a maximum separating margin method to determine the child nodes of any internal node. The proposed method ensures that two classgroups represented by any two sibling nodes are most separable. In the experiments, we evaluate the accuracy and efficiency of the proposed method over other multi-class classification methods on real world large-scale problems. The results show that the proposed method outperforms benchmark methods in terms of accuracy for most datasets and performs comparably with other class structure learning methods in terms of efficiency for all datasets.

  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. A model for a multi-class classification machine

    Science.gov (United States)

    Rau, Albrecht; Nadal, Jean-Pierre

    1992-06-01

    We consider the properties of multi-class neural networks, where each neuron can be in several different states. The motivations for considering such systems are manifold. In image processing for example, the different states correspond to the different grey tone levels. Another multi-class classification task implemented on a feed-forward network is the analysis of DNA sequences or the prediction of the secondary structure of proteins from the sequence of amino acids. To investigate the behaviour of such systems, one specific dynamical rule - the “winner-take-all” rule - is studied. Gauge invariances of the model are analysed. For a multi-class perceptron with N Q-state input neurons and Q‧-state output neuron, the maximal number of patterns that can be stored in the large N limit is found to be proportional to N(Q - 1) ƒ(Q‧), where ƒ( Q‧) is a slowly increasing and bounded function of order 1.

  9. 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.

  10. Brain-Computer Interfacing and Games

    NARCIS (Netherlands)

    Plass-Oude Bos, Danny; Reuderink, Boris; Laar, van de Bram; Gürkök, Hayrettin; Mühl, Christian; Poel, Mannes; Nijholt, Anton; Heylen, Dirk; Tan, D.; Nijholt, A.

    2010-01-01

    Recently research into Brain-Computer Interfacing (BCI) applications for healthy users, such as games, has been initiated. But why would a healthy person use a still-unproven technology such as BCI for game interaction? BCI provides a combination of information and features that no other input modal

  11. Affective Brain-Computer Interfaces: Preface

    NARCIS (Netherlands)

    Mühl, Christian; Heylen, Dirk; Nijholt, Anton; Mühl, Christian; Heylen, Dirk; Nijholt, Anton

    2009-01-01

    These are the proceedings of ABCI 2009, Affective Brain Computer Interfaces, a workshop that was organized in conjunction with ACII 2009, the International Conference on Affective Computation and Intelligent Interaction, held in Amsterdam, The Netherlands, September 2009. The workshop took place on

  12. Experiencing Brain-Computer Interface Control

    NARCIS (Netherlands)

    Laar, van de B.L.A.

    2016-01-01

    Brain-Computer Interfaces (BCIs) are systems that extract information from the user’s brain activity and employ it in some way in an interactive system. While historically BCIs were mainly catered towards paralyzed or otherwise physically handicapped users, the last couple of years applications with

  13. A Multi-Class, Interdisciplinary Project Using Elementary Statistics

    Science.gov (United States)

    Reese, Margaret

    2012-01-01

    This article describes a multi-class project that employs statistical computing and writing in a statistics class. Three courses, General Ecology, Meteorology, and Introductory Statistics, cooperated on a project for the EPA's Student Design Competition. The continuing investigation has also spawned several undergraduate research projects in…

  14. 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

  15. 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.

  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. 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.

  18. 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...

  19. 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.

  20. 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.

  1. 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.

  2. Brain-computer interfaces and neurorehabilitation.

    Science.gov (United States)

    Carabalona, Roberta; Castiglioni, Paolo; Gramatica, Furio

    2009-01-01

    A brain-computer interface (BCI) directly uses brain-activity signals to allow users to operate the environment without any muscular activation. Thanks to this feature, BCI systems can be employed not only as assistive devices, but also as neurorehabilitation tools in clinical settings. However, several critical issues need to be addressed before using BCI in neurorehabilitation, issues ranging from signal acquisition and selection of the proper BCI paradigm to the evaluation of the affective state, cognitive load and system acceptability of the users. Here we discuss these issues, illustrating how a rehabilitation program can benefit from BCI sessions, and summarize the results obtained so far in this field. Also provided are experimental data concerning two important topics related to BCI usability in rehabilitation: the possibility of using dry electrodes for EEG acquisition, and the monitoring of psychophysiological effects during BCI tasks.

  3. 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.

  4. Mining discriminative class codes for multi-class classification based on minimizing generalization errors

    Science.gov (United States)

    Eiadon, Mongkon; Pipanmaekaporn, Luepol; Kamonsantiroj, Suwatchai

    2016-07-01

    Error Correcting Output Code (ECOC) has emerged as one of promising techniques for solving multi-class classification. In the ECOC framework, a multi-class problem is decomposed into several binary ones with a coding design scheme. Despite this, the suitable multi-class decomposition scheme is still ongoing research in machine learning. In this work, we propose a novel multi-class coding design method to mine the effective and compact class codes for multi-class classification. For a given n-class problem, this method decomposes the classes into subsets by embedding a structure of binary trees. We put forward a novel splitting criterion based on minimizing generalization errors across the classes. Then, a greedy search procedure is applied to explore the optimal tree structure for the problem domain. We run experiments on many multi-class UCI datasets. The experimental results show that our proposed method can achieve better classification performance than the common ECOC design methods.

  5. Multi-class EEG classification of voluntary hand movement directions

    Science.gov (United States)

    Robinson, Neethu; Guan, Cuntai; Vinod, A. P.; Keng Ang, Kai; Tee, Keng Peng

    2013-10-01

    Objective. Studies have shown that low frequency components of brain recordings provide information on voluntary hand movement directions. However, non-invasive techniques face more challenges compared to invasive techniques. Approach. This study presents a novel signal processing technique to extract features from non-invasive electroencephalography (EEG) recordings for classifying voluntary hand movement directions. The proposed technique comprises the regularized wavelet-common spatial pattern algorithm to extract the features, mutual information-based feature selection, and multi-class classification using the Fisher linear discriminant. EEG data from seven healthy human subjects were collected while they performed voluntary right hand center-out movement in four orthogonal directions. In this study, the movement direction dependent signal-to-noise ratio is used as a parameter to denote the effectiveness of each temporal frequency bin in the classification of movement directions. Main results. Significant (p movement direction dependent modulation in the EEG data was identified largely towards the end of movement at low frequencies (≤6 Hz) from the midline parietal and contralateral motor areas. Experimental results on single trial classification of the EEG data collected yielded an average accuracy of (80.24 ± 9.41)% in discriminating the four different directions using the proposed technique on features extracted from low frequency components. Significance. The proposed feature extraction strategy provides very high multi-class classification accuracies, and the results are proven to be more statistically significant than existing methods. The results obtained suggest the possibility of multi-directional movement classification from single-trial EEG recordings using the proposed technique in low frequency components.

  6. Brain-Computer Interfaces for HCI and Games

    NARCIS (Netherlands)

    Nijholt, Anton; Tan, Desney; Allison, Brandan; Millán, Jose del R.; Graimann, Bernhard; Jackson, Melody Moore

    2008-01-01

    We study the research themes and the state-of-the-art of brain-computer interaction. Brain-computer interface research has seen much progress in the medical domain, for example for prosthesis control or as biofeedback therapy for the treatment of neurological disorders. Here, however, we look at bra

  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. Brain Computer Interface on Track to Home

    Directory of Open Access Journals (Sweden)

    Felip Miralles

    2015-01-01

    Full Text Available The novel BackHome system offers individuals with disabilities a range of useful services available via brain-computer interfaces (BCIs, to help restore their independence. This is the time such technology is ready to be deployed in the real world, that is, at the target end users’ home. This has been achieved by the development of practical electrodes, easy to use software, and delivering telemonitoring and home support capabilities which have been conceived, implemented, and tested within a user-centred design approach. The final BackHome system is the result of a 3-year long process involving extensive user engagement to maximize effectiveness, reliability, robustness, and ease of use of a home based BCI system. The system is comprised of ergonomic and hassle-free BCI equipment; one-click software services for Smart Home control, cognitive stimulation, and web browsing; and remote telemonitoring and home support tools to enable independent home use for nonexpert caregivers and users. BackHome aims to successfully bring BCIs to the home of people with limited mobility to restore their independence and ultimately improve their quality of life.

  9. The Graz Brain-Computer Interface

    Science.gov (United States)

    Pfurtscheller, Gert; Brunner, Clemens; Leeb, Robert; Scherer, Reinhold; Müller-Putz, Gernot R.; Neuper, Christa

    Brain-computer interface (BCI) research at the Graz University of Technology started with the classification of event-related desynchronization (ERD) [36, 38] of single-trial electroencephalographic (EEG) data during actual (overt) and imagined (covert) hand movement [9, 18, 40]. At the beginning of our BCI research activities we had a cooperation with the Wadsworth Center in Albany, New York State, USA, with the common interest to control one-dimensional cursor movement on a monitor through mental activity [69]. With such a cursor control it is in principle possible to select letters of the alphabet, create words and sentences and realize a thought-based spelling system for patients in a complete or incomplete "locked-in" state [68]. At that time we already analyzed 64-channel EEG data from three patients who had accomplished a number of training sessions with the aim to search for optimal electrode positions and frequency components [38]. Using the distinction sensitive learning vector quantizer (DSLVQ) [54] it was found that for each subject there exist optimal electrode positions and frequency components for on-line EEG-based cursor control. This was confirmed recently by BCI studies in untrained subjects [2, 58].

  10. 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.

  11. Towards psychologically adaptive brain-computer interfaces

    Science.gov (United States)

    Myrden, A.; Chau, T.

    2016-12-01

    Objective. Brain-computer interface (BCI) performance is sensitive to short-term changes in psychological states such as fatigue, frustration, and attention. This paper explores the design of a BCI that can adapt to these short-term changes. Approach. Eleven able-bodied individuals participated in a study during which they used a mental task-based EEG-BCI to play a simple maze navigation game while self-reporting their perceived levels of fatigue, frustration, and attention. In an offline analysis, a regression algorithm was trained to predict changes in these states, yielding Pearson correlation coefficients in excess of 0.45 between the self-reported and predicted states. Two means of fusing the resultant mental state predictions with mental task classification were investigated. First, single-trial mental state predictions were used to predict correct classification by the BCI during each trial. Second, an adaptive BCI was designed that retrained a new classifier for each testing sample using only those training samples for which predicted mental state was similar to that predicted for the current testing sample. Main results. Mental state-based prediction of BCI reliability exceeded chance levels. The adaptive BCI exhibited significant, but practically modest, increases in classification accuracy for five of 11 participants and no significant difference for the remaining six despite a smaller average training set size. Significance. Collectively, these findings indicate that adaptation to psychological state may allow the design of more accurate BCIs.

  12. 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.

  13. Multi-class DTI Segmentation: A Convex Approach.

    Science.gov (United States)

    Xie, Yuchen; Chen, Ting; Ho, Jeffrey; Vemuri, Baba C

    2012-10-01

    In this paper, we propose a novel variational framework for multi-class DTI segmentation based on global convex optimization. The existing variational approaches to the DTI segmentation problem have mainly used gradient-descent type optimization techniques which are slow in convergence and sensitive to the initialization. This paper on the other hand provides a new perspective on the often difficult optimization problem in DTI segmentation by providing a reasonably tight convex approximation (relaxation) of the original problem, and the relaxed convex problem can then be efficiently solved using various methods such as primal-dual type algorithms. To the best of our knowledge, such a DTI segmentation technique has never been reported in literature. We also show that a variety of tensor metrics (similarity measures) can be easily incorporated in the proposed framework. Experimental results on both synthetic and real diffusion tensor images clearly demonstrate the advantages of our method in terms of segmentation accuracy and robustness. In particular, when compared with existing state-of-the-art methods, our results demonstrate convincingly the importance as well as the benefit of using more refined and elaborated optimization method in diffusion tensor MR image segmentation.

  14. 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.

  15. 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.

  16. 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

  17. Brain-Computer Link Restores Some Movement to Quadraplegic Man

    Science.gov (United States)

    ... page: https://medlineplus.gov/news/fullstory_164322.html Brain-Computer Link Restores Some Movement to Quadraplegic Man ... aspirin -- in the motor cortex area of his brain. That's the brain region responsible for hand movement. ...

  18. 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...

  19. 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.

  20. 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.

  1. 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).

  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. 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...

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

    DEFF Research Database (Denmark)

    Brandl, Stephanie; Frølich, Laura; Höhne, Johannes;

    2016-01-01

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

  5. Multimodal and Multi-Brain Computer Interfaces: A Review

    NARCIS (Netherlands)

    Nijholt, Anton

    2015-01-01

    In this short paper we survey recent research views in non-traditional brain-computer interfaces. That is, interfaces that can process brain activity input, but that are designed for non-clinical purposes, that are meant to be used by ‘healthy’ users, that process other user input as well, and that

  6. Playing with your Brain: Brain-Computer Interfaces and Games

    NARCIS (Netherlands)

    Nijholt, Anton; Tan, Desney; Bernhaupt, R.; Tscheligi, M.

    2007-01-01

    In this workshop we investigate a possible role of brain-computer interaction in computer games and entertainment computing. The assumption is that brain activity, whether it is consciously controlled and directed by the user or just recorded in order to obtain information about the user’s affective

  7. Turning Shortcomings into Challenges: Brain-Computer Interfaces for Games

    NARCIS (Netherlands)

    Nijholt, Anton; Reuderink, Boris; Oude Bos, Danny; Nijholt, A.; Reidsma, D.; Hondorp, G.H.W.

    2009-01-01

    In recent years we have seen a rising interest in brain-computer interfacing for human-computer interaction and potential game applications. Until now, however, we have almost only seen attempts where BCI is used to measure the affective state of the user or in neurofeeedback games. There have hardl

  8. 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

  9. Brain-Computer Interface Games: Towards a Framework

    NARCIS (Netherlands)

    Gürkök, Hayrettin; Nijholt, Anton; Poel, Mannes; Herrlich, Marc; Malaka, Rainer; Masuch, Maic

    2012-01-01

    The brain-computer interface (BCI) community started to consider games as potential applications while the games community started to consider BCI as a game controller. However, there is a discrepancy between the BCI games developed by the two communities. In this paper, we propose a preliminary BCI

  10. Turning Shortcomings into Challenges: Brain-Computer Interfaces for Games

    NARCIS (Netherlands)

    Nijholt, Anton; Oude Bos, Danny; Reuderink, Boris

    2009-01-01

    In recent years we have seen a rising interest in brain-computer interfacing for human-computer interaction and potential game applications. Until now, however, we have almost only seen proof-of-concepts where a single BCI paradigm is demonstrated to work as a simple control mechanism, as a measurem

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. A hybrid three-class brain-computer interface system utilizing SSSEPs and transient ERPs

    Science.gov (United States)

    Breitwieser, Christian; Pokorny, Christoph; Müller-Putz, Gernot R.

    2016-12-01

    Objective. This paper investigates the fusion of steady-state somatosensory evoked potentials (SSSEPs) and transient event-related potentials (tERPs), evoked through tactile simulation on the left and right-hand fingertips, in a three-class EEG based hybrid brain-computer interface. It was hypothesized, that fusing the input signals leads to higher classification rates than classifying tERP and SSSEP individually. Approach. Fourteen subjects participated in the studies, consisting of a screening paradigm to determine person dependent resonance-like frequencies and a subsequent online paradigm. The whole setup of the BCI system was based on open interfaces, following suggestions for a common implementation platform. During the online experiment, subjects were instructed to focus their attention on the stimulated fingertips as indicated by a visual cue. The recorded data were classified during runtime using a multi-class shrinkage LDA classifier and the outputs were fused together applying a posterior probability based fusion. Data were further analyzed offline, involving a combined classification of SSSEP and tERP features as a second fusion principle. The final results were tested for statistical significance applying a repeated measures ANOVA. Main results. A significant classification increase was achieved when fusing the results with a combined classification compared to performing an individual classification. Furthermore, the SSSEP classifier was significantly better in detecting a non-control state, whereas the tERP classifier was significantly better in detecting control states. Subjects who had a higher relative band power increase during the screening session also achieved significantly higher classification results than subjects with lower relative band power increase. Significance. It could be shown that utilizing SSSEP and tERP for hBCIs increases the classification accuracy and also that tERP and SSSEP are not classifying control- and non

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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 ...

  4. 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\\ud neuron disease patients to communicate and control devices. Mindfulness meditation has\\ud been claimed to enhance metacognitive regulation. The current study explores whether\\ud mindfulness meditation training can thus improve the performance of BCI users. To eliminate\\ud the possibility of expectation of improvement influencing the results, we introduced a music\\ud training condition. A norming study found...

  5. 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

    , 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...

  6. Minimally-sized balanced decomposition schemes for multi-class classification

    NARCIS (Netherlands)

    Smirnov, E.N.; Moed, M.; Nalbantov, G.I.; Sprinkhuizen-Kuyper, I.G.

    2011-01-01

    Error-Correcting Output Coding (ECOC) is a well-known class of decomposition schemes for multi-class classification. It allows representing any multiclass classification problem as a set of binary classification problems. Due to code redundancy ECOC schemes can significantly improve generalization p

  7. Performance analysis of traffic surges in multi-class communication networks

    NARCIS (Netherlands)

    Jonckheere, M.T.S.; Núñez-Queija, R.; Prabhu, B.J.

    2010-01-01

    In multi-class communication networks, traffic surges due to one class of users can significantly degrade the performance for other classes. During these transient periods, it is thus of crucial importance to implement priority mechanisms allowing the conservation of the quality of service experienc

  8. 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

  9. True zero-training brain-computer interfacing--an online study.

    Directory of Open Access Journals (Sweden)

    Pieter-Jan Kindermans

    Full Text Available Despite several approaches to realize subject-to-subject transfer of pre-trained classifiers, the full performance of a Brain-Computer Interface (BCI for a novel user can only be reached by presenting the BCI system with data from the novel user. In typical state-of-the-art BCI systems with a supervised classifier, the labeled data is collected during a calibration recording, in which the user is asked to perform a specific task. Based on the known labels of this recording, the BCI's classifier can learn to decode the individual's brain signals. Unfortunately, this calibration recording consumes valuable time. Furthermore, it is unproductive with respect to the final BCI application, e.g. text entry. Therefore, the calibration period must be reduced to a minimum, which is especially important for patients with a limited concentration ability. The main contribution of this manuscript is an online study on unsupervised learning in an auditory event-related potential (ERP paradigm. Our results demonstrate that the calibration recording can be bypassed by utilizing an unsupervised trained classifier, that is initialized randomly and updated during usage. Initially, the unsupervised classifier tends to make decoding mistakes, as the classifier might not have seen enough data to build a reliable model. Using a constant re-analysis of the previously spelled symbols, these initially misspelled symbols can be rectified posthoc when the classifier has learned to decode the signals. We compare the spelling performance of our unsupervised approach and of the unsupervised posthoc approach to the standard supervised calibration-based dogma for n = 10 healthy users. To assess the learning behavior of our approach, it is unsupervised trained from scratch three times per user. Even with the relatively low SNR of an auditory ERP paradigm, the results show that after a limited number of trials (30 trials, the unsupervised approach performs comparably to a classic

  10. 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

  11. Noninvasive brain-computer interface driven hand orthosis.

    Science.gov (United States)

    King, Christine E; Wang, Po T; Mizuta, Masato; Reinkensmeyer, David J; Do, An H; Moromugi, Shunji; Nenadic, Zoran

    2011-01-01

    Neurological conditions, such as stroke, can leave the affected individual with hand motor impairment despite intensive treatments. Novel technologies, such as brain-computer interface (BCI), may be able to restore or augment impaired motor behaviors by engaging relevant cortical areas. Here, we developed and tested an electroencephalogram (EEG) based BCI system for control of hand orthosis. An able-bodied subject performed contralateral hand grasping to achieve continuous online control of the hand orthosis, suggesting that the integration of a noninvasive BCI with a hand orthosis is feasible. The adoption of this technology to stroke survivors may provide a novel neurorehabilitation therapy for hand motor impairment in this population.

  12. Brain-computer interface after nervous system injury.

    Science.gov (United States)

    Burns, Alexis; Adeli, Hojjat; Buford, John A

    2014-12-01

    Brain-computer interface (BCI) has proven to be a useful tool for providing alternative communication and mobility to patients suffering from nervous system injury. BCI has been and will continue to be implemented into rehabilitation practices for more interactive and speedy neurological recovery. The most exciting BCI technology is evolving to provide therapeutic benefits by inducing cortical reorganization via neuronal plasticity. This article presents a state-of-the-art review of BCI technology used after nervous system injuries, specifically: amyotrophic lateral sclerosis, Parkinson's disease, spinal cord injury, stroke, and disorders of consciousness. Also presented is transcending, innovative research involving new treatment of neurological disorders.

  13. Brain-computer interface systems: progress and prospects.

    Science.gov (United States)

    Allison, Brendan Z; Wolpaw, Elizabeth Winter; Wolpaw, Jonathan R

    2007-07-01

    Brain-computer interface (BCI) systems support communication through direct measures of neural activity without muscle activity. BCIs may provide the best and sometimes the only communication option for users disabled by the most severe neuromuscular disorders and may eventually become useful to less severely disabled and/or healthy individuals across a wide range of applications. This review discusses the structure and functions of BCI systems, clarifies terminology and addresses practical applications. Progress and opportunities in the field are also identified and explicated.

  14. 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.

  15. 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.

  16. Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis

    Science.gov (United States)

    McCane, Lynn M.; Sellers, Eric W.; Mcfarland, Dennis J.; Mak, Joseph N.; Carmack, C. Steve; Zeitlin, Debra; Wolpaw, Jonathan R.; Vaughan, Theresa M.

    2015-01-01

    Brain-computer interfaces (BCIs) might restore communication to people severely disabled by amyotrophic lateral sclerosis (ALS) or other disorders. We sought to: 1) define a protocol for determining whether a person with ALS can use a visual P300-based BCI; 2) determine what proportion of this population can use the BCI; and 3) identify factors affecting BCI performance. Twenty-five individuals with ALS completed an evaluation protocol using a standard 6 × 6 matrix and parameters selected by stepwise linear discrimination. With an 8-channel EEG montage, the subjects fell into two groups in BCI accuracy (chance accuracy 3%). Seventeen averaged 92 (± 3)% (range 71–100%), which is adequate for communication (G70 group). Eight averaged 12 (± 6)% (range 0–36%), inadequate for communication (L40 subject group). Performance did not correlate with disability: 11/17 (65%) of G70 subjects were severely disabled (i.e. ALSFRS-R < 5). All L40 subjects had visual impairments (e.g. nystagmus, diplopia, ptosis). P300 was larger and more anterior in G70 subjects. A 16-channel montage did not significantly improve accuracy. In conclusion, most people severely disabled by ALS could use a visual P300-based BCI for communication. In those who could not, visual impairment was the principal obstacle. For these individuals, auditory P300-based BCIs might be effective. PMID:24555843

  17. 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...

  18. 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.

  19. BIOMARKER SIGNATURE IDENTIFICATION IN “OMICS” DATA WITH MULTI-CLASS OUTCOME

    Directory of Open Access Journals (Sweden)

    Vincenzo Lagani

    2013-03-01

    Full Text Available Biomarker signature identification in “omics” data is a complex challenge that requires specialized feature selection algorithms. The objective of these algorithms is to select the smallest set(s of molecular quantities that are able to predict a given outcome (target with maximal predictive performance. This task is even more challenging when the outcome comprises of multiple classes; for example, one may be interested in identifying the genes whose expressions allow discrimination among different types of cancer (nominal outcome or among different stages of the same cancer, e.g. Stage 1, 2, 3 and 4 of Lung Adenocarcinoma (ordinal outcome. In this work, we consider a particular type of successful feature selection methods, named constraint-based, local causal discovery algorithms. These algorithms depend on performing a series of conditional independence tests. We extend these algorithms for the analysis of problems with continuous predictors and multi-class outcomes, by developing and equipping them with an appropriate conditional independence test procedure for both nominal and ordinal multi-class targets. The test is based on multinomial logistic regression and employs the log-likelihood ratio test for model selection. We present a comparative, experimental evaluation on seven real-world, high-dimensional, gene-expression datasets. Within the scope of our analysis the results indicate that the new conditional independence test allows the identification of smaller and better performing signatures for multi-class outcome datasets, with respect to the current alternatives for performing the independence tests.

  20. 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...

  1. 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.

  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. 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

  4. 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.

  5. 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)

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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'.

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

    Science.gov (United States)

    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 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

  13. 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

  14. 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.

  15. 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.

  16. Admission Control Scheme for Multi-class Services in QoS-based Mobile Cellular Networks

    Institute of Scientific and Technical Information of China (English)

    YINZhiming; XIEJianying

    2004-01-01

    Call admission control (CAC) is one of the key schemes to guarantee Quality of service (QoS) in mobile cellular networks. In this paper, we propose an optimal CAC scheme based on Semi-Markov decision processes (SMDP) theory to support multi-class services for QoS wireless networks. Linear programming formulation is used to find the optimal solution, which maximizes the channel utilization while meeting the requirements of QoS constraints. The numerical results show that the performance of our scheme outperforms DCAC scheme.

  17. 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.

  18. 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.

  19. Auditory hallucinations.

    Science.gov (United States)

    Blom, Jan Dirk

    2015-01-01

    Auditory hallucinations constitute a phenomenologically rich group of endogenously mediated percepts which are associated with psychiatric, neurologic, otologic, and other medical conditions, but which are also experienced by 10-15% of all healthy individuals in the general population. The group of phenomena is probably best known for its verbal auditory subtype, but it also includes musical hallucinations, echo of reading, exploding-head syndrome, and many other types. The subgroup of verbal auditory hallucinations has been studied extensively with the aid of neuroimaging techniques, and from those studies emerges an outline of a functional as well as a structural network of widely distributed brain areas involved in their mediation. The present chapter provides an overview of the various types of auditory hallucination described in the literature, summarizes our current knowledge of the auditory networks involved in their mediation, and draws on ideas from the philosophy of science and network science to reconceptualize the auditory hallucinatory experience, and point out directions for future research into its neurobiologic substrates. In addition, it provides an overview of known associations with various clinical conditions and of the existing evidence for pharmacologic and non-pharmacologic treatments.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Flexibility and practicality graz brain-computer interface approach.

    Science.gov (United States)

    Scherer, Reinhold; Müller-Putz, Gernot R; Pfurtscheller, Gert

    2009-01-01

    "Graz brain-computer interface (BCI)" transforms changes in oscillatory electroencephalogram (EEG) activity into control signals for external devices and feedback. Steady-state evoked potentials (SSEPs) and event-related desynchronization (ERD) are employed to encode user messages. User-specific setup and training are important issues for robust and reliable classification. Furthermore, in order to implement small and thus affordable systems, focus is put on the minimization of the number of EEG sensors. The system also supports the self-paced operation mode, that is, users have on-demand access to the system at any time and can autonomously initiate communication. Flexibility, usability, and practicality are essential to increase user acceptance. Here, we illustrate the possibilities offered by now from EEG-based communication. Results of several studies with able-bodied and disabled individuals performed inside the laboratory and in real-world environments are presented; their characteristics are shown and open issues are mentioned. The applications include the control of neuroprostheses and spelling devices, the interaction with Virtual Reality, and the operation of off-the-shelf software such as Google Earth.

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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

  14. 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.

  15. Evaluation of a Compact Hybrid Brain-Computer Interface System

    Directory of Open Access Journals (Sweden)

    Jaeyoung Shin

    2017-01-01

    Full Text Available We realized a compact hybrid brain-computer interface (BCI system by integrating a portable near-infrared spectroscopy (NIRS device with an economical electroencephalography (EEG system. The NIRS array was located on the subjects’ forehead, covering the prefrontal area. The EEG electrodes were distributed over the frontal, motor/temporal, and parietal areas. The experimental paradigm involved a Stroop word-picture matching test in combination with mental arithmetic (MA and baseline (BL tasks, in which the subjects were asked to perform either MA or BL in response to congruent or incongruent conditions, respectively. We compared the classification accuracies of each of the modalities (NIRS or EEG with that of the hybrid system. We showed that the hybrid system outperforms the unimodal EEG and NIRS systems by 6.2% and 2.5%, respectively. Since the proposed hybrid system is based on portable platforms, it is not confined to a laboratory environment and has the potential to be used in real-life situations, such as in neurorehabilitation.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  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. 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

  3. Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general

    Science.gov (United States)

    Zander, Thorsten O.; Kothe, Christian

    2011-04-01

    Cognitive monitoring is an approach utilizing realtime brain signal decoding (RBSD) for gaining information on the ongoing cognitive user state. In recent decades this approach has brought valuable insight into the cognition of an interacting human. Automated RBSD can be used to set up a brain-computer interface (BCI) providing a novel input modality for technical systems solely based on brain activity. In BCIs the user usually sends voluntary and directed commands to control the connected computer system or to communicate through it. In this paper we propose an extension of this approach by fusing BCI technology with cognitive monitoring, providing valuable information about the users' intentions, situational interpretations and emotional states to the technical system. We call this approach passive BCI. In the following we give an overview of studies which utilize passive BCI, as well as other novel types of applications resulting from BCI technology. We especially focus on applications for healthy users, and the specific requirements and demands of this user group. Since the presented approach of combining cognitive monitoring with BCI technology is very similar to the concept of BCIs itself we propose a unifying categorization of BCI-based applications, including the novel approach of passive BCI.

  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. 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.

  6. 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

  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.

  8. 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

    Over the past few years kernel methods have gained a tremendous amount of attention as existing linear algorithms can easily be extended to account for highly non-linear data in a computationally efficient manner. Unfortunately most kernels require careful tuning of intrinsic parameters to correc......Over the past few years kernel methods have gained a tremendous amount of attention as existing linear algorithms can easily be extended to account for highly non-linear data in a computationally efficient manner. Unfortunately most kernels require careful tuning of intrinsic parameters....... 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...

  9. 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.

  10. 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.

  11. 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.

  12. Brain-computer interfaces as new brain output pathways.

    Science.gov (United States)

    Wolpaw, Jonathan R

    2007-03-15

    Brain-computer interfaces (BCIs) can provide non-muscular communication and control for people with severe motor disabilities. Current BCIs use a variety of invasive and non-invasive methods to record brain signals and a variety of signal processing methods. Whatever the recording and processing methods used, BCI performance (e.g. the ability of a BCI to control movement of a computer cursor) is highly variable and, by the standards applied to neuromuscular control, could be described as ataxic. In an effort to understand this imperfection, this paper discusses the relevance of two principles that underlie the brain's normal motor outputs. The first principle is that motor outputs are normally produced by the combined activity of many CNS areas, from the cortex to the spinal cord. Together, these areas produce appropriate control of the spinal motoneurons that activate muscles. The second principle is that the acquisition and life-long preservation of motor skills depends on continual adaptive plasticity throughout the CNS. This plasticity optimizes the control of spinal motoneurons. In the light of these two principles, a BCI may be viewed as a system that changes the outcome of CNS activity from control of spinal motoneurons to, instead, control of the cortical (or other) area whose signals are used by the BCI to determine the user's intent. In essence, a BCI attempts to assign to cortical neurons the role normally performed by spinal motoneurons. Thus, a BCI requires that the many CNS areas involved in producing normal motor actions change their roles so as to optimize the control of cortical neurons rather than spinal motoneurons. The disconcerting variability of BCI performance may stem in large part from the challenge presented by the need for this unnatural adaptation. This difficulty might be reduced, and BCI development might thereby benefit, by adopting a 'goal-selection' rather than a 'process- control' strategy. In 'process control', a BCI manages all

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

    OpenAIRE

    Kristín Guðmundsdóttir 1972

    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...

  14. TSG: a new algorithm for binary and multi-class cancer classification and informative genes selection

    Directory of Open Access Journals (Sweden)

    Wang Haiyan

    2013-01-01

    Full Text Available Abstract Background One of the challenges in classification of cancer tissue samples based on gene expression data is to establish an effective method that can select a parsimonious set of informative genes. The Top Scoring Pair (TSP, k-Top Scoring Pairs (k-TSP, Support Vector Machines (SVM, and prediction analysis of microarrays (PAM are four popular classifiers that have comparable performance on multiple cancer datasets. SVM and PAM tend to use a large number of genes and TSP, k-TSP always use even number of genes. In addition, the selection of distinct gene pairs in k-TSP simply combined the pairs of top ranking genes without considering the fact that the gene set with best discrimination power may not be the combined pairs. The k-TSP algorithm also needs the user to specify an upper bound for the number of gene pairs. Here we introduce a computational algorithm to address the problems. The algorithm is named Chisquare-statistic-based Top Scoring Genes (Chi-TSG classifier simplified as TSG. Results The TSG classifier starts with the top two genes and sequentially adds additional gene into the candidate gene set to perform informative gene selection. The algorithm automatically reports the total number of informative genes selected with cross validation. We provide the algorithm for both binary and multi-class cancer classification. The algorithm was applied to 9 binary and 10 multi-class gene expression datasets involving human cancers. The TSG classifier outperforms TSP family classifiers by a big margin in most of the 19 datasets. In addition to improved accuracy, our classifier shares all the advantages of the TSP family classifiers including easy interpretation, invariant to monotone transformation, often selects a small number of informative genes allowing follow-up studies, resistant to sampling variations due to within sample operations. Conclusions Redefining the scores for gene set and the classification rules in TSP family

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

    Science.gov (United States)

    Rothschild, Ryan Mark

    2010-01-01

    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 and near-infrared spectroscopy. 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 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 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.

  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. A robust and accurate method for feature selection and prioritization from multi-class OMICs data.

    Directory of Open Access Journals (Sweden)

    Vittorio Fortino

    Full Text Available Selecting relevant features is a common task in most OMICs data analysis, where the aim is to identify a small set of key features to be used as biomarkers. To this end, two alternative but equally valid methods are mainly available, namely the univariate (filter or the multivariate (wrapper approach. The stability of the selected lists of features is an often neglected but very important requirement. If the same features are selected in multiple independent iterations, they more likely are reliable biomarkers. In this study, we developed and evaluated the performance of a novel method for feature selection and prioritization, aiming at generating robust and stable sets of features with high predictive power. The proposed method uses the fuzzy logic for a first unbiased feature selection and a Random Forest built from conditional inference trees to prioritize the candidate discriminant features. Analyzing several multi-class gene expression microarray data sets, we demonstrate that our technique provides equal or better classification performance and a greater stability as compared to other Random Forest-based feature selection methods.

  19. Case based reasoning applied to medical diagnosis using multi-class classifier: A preliminary study

    Directory of Open Access Journals (Sweden)

    D. Viveros-Melo

    2017-02-01

    Full Text Available Case-based reasoning (CBR is a process used for computer processing that tries to mimic the behavior of a human expert in making decisions regarding a subject and learn from the experience of past cases. CBR has demonstrated to be appropriate for working with unstructured domains data or difficult knowledge acquisition situations, such as medical diagnosis, where it is possible to identify diseases such as: cancer diagnosis, epilepsy prediction and appendicitis diagnosis. Some of the trends that may be developed for CBR in the health science are oriented to reduce the number of features in highly dimensional data. An important contribution may be the estimation of probabilities of belonging to each class for new cases. In this paper, in order to adequately represent the database and to avoid the inconveniences caused by the high dimensionality, noise and redundancy, a number of algorithms are used in the preprocessing stage for performing both variable selection and dimension reduction procedures. Also, a comparison of the performance of some representative multi-class classifiers is carried out to identify the most effective one to include within a CBR scheme. Particularly, four classification techniques and two reduction techniques are employed to make a comparative study of multiclass classifiers on CBR

  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.

  1. Computationally efficient SVM multi-class image recognition with confidence measures

    Energy Technology Data Exchange (ETDEWEB)

    Makili, Lazaro [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Vega, Jesus, E-mail: jesus.vega@ciemat.es [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Dormido-Canto, Sebastian [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Pastor, Ignacio [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Murari, Andrea [Associazione EURATOM-CIEMAT per la Fusione, Consorzio RFX, Padova (Italy)

    2011-10-15

    Typically, machine learning methods produce non-qualified estimates, i.e. the accuracy and reliability of the predictions are not provided. Transductive predictors are very recent classifiers able to provide, simultaneously with the prediction, a couple of values (confidence and credibility) to reflect the quality of the prediction. Usually, a drawback of the transductive techniques for huge datasets and large dimensionality is the high computational time. To overcome this issue, a more efficient classifier has been used in a multi-class image classification problem in the TJ-II stellarator database. It is based on the creation of a hash function to generate several 'one versus the rest' classifiers for every class. By using Support Vector Machines as the underlying classifier, a comparison between the pure transductive approach and the new method has been performed. In both cases, the success rates are high and the computation time with the new method is up to 0.4 times the old one.

  2. Computer-assisted lip diagnosis on traditional Chinese medicine using multi-class support vector machines

    Directory of Open Access Journals (Sweden)

    Li FuFeng

    2012-08-01

    Full Text Available Abstract Background In Traditional Chinese Medicine (TCM, the lip diagnosis is an important diagnostic method which has a long history and is applied widely. The lip color of a person is considered as a symptom to reflect the physical conditions of organs in the body. However, the traditional diagnostic approach is mainly based on observation by doctor’s nude eyes, which is non-quantitative and subjective. The non-quantitative approach largely depends on the doctor’s experience and influences accurate the diagnosis and treatment in TCM. Developing new quantification methods to identify the exact syndrome based on the lip diagnosis of TCM becomes urgent and important. In this paper, we design a computer-assisted classification model to provide an automatic and quantitative approach for the diagnosis of TCM based on the lip images. Methods A computer-assisted classification method is designed and applied for syndrome diagnosis based on the lip images. Our purpose is to classify the lip images into four groups: deep-red, red, purple and pale. The proposed scheme consists of four steps including the lip image preprocessing, image feature extraction, feature selection and classification. The extracted 84 features contain the lip color space component, texture and moment features. Feature subset selection is performed by using SVM-RFE (Support Vector Machine with recursive feature elimination, mRMR (minimum Redundancy Maximum Relevance and IG (information gain. Classification model is constructed based on the collected lip image features using multi-class SVM and Weighted multi-class SVM (WSVM. In addition, we compare SVM with k-nearest neighbor (kNN algorithm, Multiple Asymmetric Partial Least Squares Classifier (MAPLSC and Naïve Bayes for the diagnosis performance comparison. All displayed faces image have obtained consent from the participants. Results A total of 257 lip images are collected for the modeling of lip diagnosis in TCM. The

  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. Auditory Hallucination

    Directory of Open Access Journals (Sweden)

    MohammadReza Rajabi

    2003-09-01

    Full Text Available Auditory Hallucination or Paracusia is a form of hallucination that involves perceiving sounds without auditory stimulus. A common is hearing one or more talking voices which is associated with psychotic disorders such as schizophrenia or mania. Hallucination, itself, is the most common feature of perceiving the wrong stimulus or to the better word perception of the absence stimulus. Here we will discuss four definitions of hallucinations:1.Perceiving of a stimulus without the presence of any subject; 2. hallucination proper which are the wrong perceptions that are not the falsification of real perception, Although manifest as a new subject and happen along with and synchronously with a real perception;3. hallucination is an out-of-body perception which has no accordance with a real subjectIn a stricter sense, hallucinations are defined as perceptions in a conscious and awake state in the absence of external stimuli which have qualities of real perception, in that they are vivid, substantial, and located in external objective space. We are going to discuss it in details here.

  5. 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.

  6. LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information

    Directory of Open Access Journals (Sweden)

    Zhongjie Shen

    2013-07-01

    Full Text Available 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. 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.

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

    DEFF Research Database (Denmark)

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

    2017-01-01

    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 perception is a complex cognitive process that involves information from the cochlea travelling via the auditory nerve to subcortical areas and finally the auditory cortices, where the auditory 'experience' is believed to arise. When subjects report on their auditory experience...... 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...

  9. 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

  10. 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.

  11. 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...

  12. The Asilomar Survey: Stakeholders’ Opinions on Ethical Issues Related to Brain-Computer Interfacing

    NARCIS (Netherlands)

    Nijboer, F.; Clausen, J.; Allison, B.Z.; Haselager, W.F.G.

    2011-01-01

    Brain-Computer Interface (BCI) research and (future) applications raise important ethical issues that need to be addressed to promote societal acceptance and adequate policies. Here we report on a survey we conducted among 145 BCI researchers at the 4th International BCI conference, which took place

  13. The Asilomar Survey: Stakeholders' Opinions on Ethical Issues Related to Brain-Computer Interfacing

    NARCIS (Netherlands)

    Nijboer, Femke; Clausen, Jens; Allison, Brendan Z.; Haselager, Pim

    2011-01-01

    Brain-Computer Interface (BCI) research and (future) applications raise important ethical issues that need to be addressed to promote societal acceptance and adequate policies. Here we report on a survey we conducted among 145 BCI researchers at the 4th International BCI conference, which took place

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

    DEFF Research Database (Denmark)

    Rohani, Darius Adam; Sørensen, Helge Bjarup Dissing; 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...

  15. Review: Human Intracortical recording and neural decoding for brain-computer interfaces.

    Science.gov (United States)

    Brandman, David M; Cash, Sydney S; Hochberg, Leigh R

    2017-03-02

    Brain Computer Interfaces (BCIs) use neural information recorded from the brain for voluntary control of external devices. The development of BCI systems has largely focused on improving functional independence for individuals with severe motor impairments, including providing tools for communication and mobility. In this review, we describe recent advances in intracortical BCI technology and provide potential directions for further research.

  16. Visuospatial attention based brain-computer interfacing using real-time fMRI

    NARCIS (Netherlands)

    Andersson, J.P.

    2012-01-01

    Despite all the literature showing that brain-computer interfacing (BCI) is achievable in a research setting, reports of paralyzed people utilizing the proposed systems are few. The performances of the BCI systems must most likely improve before they will move from research and become an assistive t

  17. 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 ...

  18. 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

  19. 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

  20. 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

  1. 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

  2. Bigger data for big data: from Twitter to brain-computer interfaces.

    Science.gov (United States)

    Roesch, Etienne B; Stahl, Frederic; Gaber, Mohamed Medhat

    2014-02-01

    We are sympathetic with Bentley et al.'s attempt to encompass the wisdom of crowds in a generative model, but posit that a successful attempt at using big data will include more sensitive measurements, more varied sources of information, and will also build from the indirect information available through technology, from ancillary technical features to data from brain-computer interfaces.

  3. 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

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. Auditory Imagery: Empirical Findings

    Science.gov (United States)

    Hubbard, Timothy L.

    2010-01-01

    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)…

  11. 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

  12. 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.

  13. 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.

  14. Prediction of brain-computer interface aptitude from individual brain structure

    OpenAIRE

    Halder, S.; Varkuti, B.; Bogdan, M.; Kübler, A; Rosenstiel, W.; R. Sitaram; Birbaumer, N.

    2013-01-01

    Objective: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. Methods: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analy...

  15. Prediction of brain-computer interface aptitude from individual brain structure

    OpenAIRE

    Sebastian eHalder; Balint eVarkuti; Martin eBogdan; Andrea eKübler; Wolfgang eRosenstiel; Ranganatha eSitaram; Niels eBirbaumer

    2013-01-01

    Objective: Brain-computer interfaces (BCIs) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary.Methods: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyze...

  16. 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.

  17. DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach.

    Directory of Open Access Journals (Sweden)

    Zhiheng Wang

    Full Text Available The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database.The DisoMCS is available at http://cal.tongji.edu.cn/disorder/.

  18. 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.

  19. 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.

  20. Hybrid EEG-EOG brain-computer interface system for practical machine control.

    Science.gov (United States)

    Punsawad, Yunyong; Wongsawat, Yodchanan; Parnichkun, Manukid

    2010-01-01

    Practical issues such as accuracy with various subjects, number of sensors, and time for training are important problems of existing brain-computer interface (BCI) systems. In this paper, we propose a hybrid framework for the BCI system that can make machine control more practical. The electrooculogram (EOG) is employed to control the machine in the left and right directions while the electroencephalogram (EEG) is employed to control the forword, no action, and complete stop motions of the machine. By using only 2-channel biosignals, the average classification accuracy of more than 95% can be achieved.

  1. 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

  2. [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.

  3. 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...

  4. 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%.

  5. 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.

  6. The Berlin Brain-Computer Interface: Non-Medical Uses of BCI Technology

    Directory of Open Access Journals (Sweden)

    Benjamin Blankertz

    2010-12-01

    Full Text Available Brain-Computer Interfacing (BCI is a steadily growing area ofresearch. While initially BCI research was focused on applicationsfor paralyzed patients, increasingly more alternative applications inhealthy human subjects are proposed and investigated. In particular,monitoring of mental states and decoding of covert user states haveseen a strong rise of interest. Here, we present some examples ofsuch novel applications which provide evidence for the promisingpotential of BCI technology for non-medical uses. Furthermore, wediscuss distinct methodological improvements required to bringnon-medical applications of BCI technology to a diversity of laypersontarget groups, e.g., ease of use, minimal training, general usability,short control latencies.

  7. Biosensor Technologies for Augmented Brain-Computer Interfaces in the Next Decades

    Science.gov (United States)

    2012-05-13

    E. Wickenden, Member IEEE, Klaus Gramann, Tzyy-Ping Jung, Senior Member IEEE, Li-Wei Ko, Member IEEE, and Jyh-Yeong Chang, Member IEEE ABSTRACT | The...V. Chen, B.-C. Shyu, Z.-J. Lin, Y.-C. Chiang, F.-S. Jaw, Y.-Y. Chen, and C. Chang, BA new scenario for negative functional magnetic resonance imaging... BA general framework for brain computer interface design,[ IEEE Trans. Neural Syst. Rehab. Eng., vol. 11, pp. 70–85, 2003. [21] R. Rao and R. Scherer

  8. Critical issues in state-of-the-art brain-computer interface signal processing.

    Science.gov (United States)

    Krusienski, Dean J; Grosse-Wentrup, Moritz; Galán, Ferran; Coyle, Damien; Miller, Kai J; Forney, Elliott; Anderson, Charles W

    2011-04-01

    This paper reviews several critical issues facing signal processing for brain-computer interfaces (BCIs) and suggests several recent approaches that should be further examined. The topics were selected based on discussions held during the 4th International BCI Meeting at a workshop organized to review and evaluate the current state of, and issues relevant to, feature extraction and translation of field potentials for BCIs. The topics presented in this paper include the relationship between electroencephalography and electrocorticography, novel features for performance prediction, time-embedded signal representations, phase information, signal non-stationarity, and unsupervised adaptation.

  9. 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.

  10. 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.

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

    Science.gov (United States)

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

    2014-02-01

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

  12. 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.

  13. 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.

  14. Denoising of multiscale/multiresolution structural feature dictionaries for rapid training of a brain computer interface.

    Science.gov (United States)

    Ince, Nuri Firat; Tadipatri, Vijay Aditya; Göksu, Fikri; Tewfik, Ahmed H

    2009-01-01

    Multichannel neural activities such as EEG or ECoG in a brain computer interface can be classified with subset selection algorithms running on large feature dictionaries describing subject specific features in spectral, temporal and spatial domain. While providing high accuracies in classification, the subset selection techniques are associated with long training times due to the large feature set constructed from multichannel neural recordings. In this paper we study a novel denoising technique for reducing the dimensionality of the feature space which decreases the computational complexity of the subset selection step radically without causing any degradation in the final classification accuracy. The denoising procedure was based on the comparison of the energy in a particular time segment and in a given scale/level to the energy of the raw data. By setting denoising threshold a priori the algorithm removes those nodes which fail to capture the energy in the raw data in a given scale. We provide experimental studies towards the classification of motor imagery related multichannel ECoG recordings for a brain computer interface. The denoising procedure was able to reach the same classification accuracy without denoising and a computational complexity around 5 times smaller. We also note that in some cases the denoised procedure performed better classification.

  15. 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.

  16. 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.

  17. 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.

  18. Control of a humanoid robot by a noninvasive brain-computer interface in humans.

    Science.gov (United States)

    Bell, Christian J; Shenoy, Pradeep; Chalodhorn, Rawichote; Rao, Rajesh P N

    2008-06-01

    We describe a brain-computer interface for controlling a humanoid robot directly using brain signals obtained non-invasively from the scalp through electroencephalography (EEG). EEG has previously been used for tasks such as controlling a cursor and spelling a word, but it has been regarded as an unlikely candidate for more complex forms of control owing to its low signal-to-noise ratio. Here we show that by leveraging advances in robotics, an interface based on EEG can be used to command a partially autonomous humanoid robot to perform complex tasks such as walking to specific locations and picking up desired objects. Visual feedback from the robot's cameras allows the user to select arbitrary objects in the environment for pick-up and transport to chosen locations. Results from a study involving nine users indicate that a command for the robot can be selected from four possible choices in 5 s with 95% accuracy. Our results demonstrate that an EEG-based brain-computer interface can be used for sophisticated robotic interaction with the environment, involving not only navigation as in previous applications but also manipulation and transport of objects.

  19. 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.

  20. Multi-class, multi-residue analysis of pesticides, polychlorinated biphenyls, polycyclic aromatic hydrocarbons, polybrominated diphenyl ethers and novel flame retardants....mass spectrometry

    Science.gov (United States)

    A multi-class, multi-residue method for the analysis of 13 novel flame retardants, 18 representative pesticides, 14 polychlorinated biphenyl (PCB) congeners, 16 polycyclic aromatic hydrocarbons (PAHs), and 7 polybrominated diphenyl ether (PBDE) congeners in catfish muscle was developed and evaluated...

  1. 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

  2. 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.

  3. Listen, you are writing!Speeding up online spelling with a dynamic auditory BCI

    Directory of Open Access Journals (Sweden)

    Martijn eSchreuder

    2011-10-01

    Full Text Available Representing an intuitive spelling interface for Brain-Computer Interfaces (BCI in the auditory domain is not straightforward. In consequence, all existing approaches based on event-related potentials (ERP rely at least partially on a visual representation of the interface. This online study introduces an auditory spelling interface that eliminates the necessity for such a visualization. In up to two sessions, a group of healthy subjects (N=21 was asked to use a text entry application, utilizing the spatial cues of the AMUSE paradigm (Auditory Multiclass Spatial ERP. The speller relies on the auditory sense both for stimulation and the core feedback. Without prior BCI experience, 76% of the participants were able to write a full sentence during the first session. By exploiting the advantages of a newly introduced dynamic stopping method, a maximum writing speed of 1.41 characters/minute (7.55 bits/minute could be reached during the second session (average: .94 char/min, 5.26 bits/min. For the first time, the presented work shows that an auditory BCI can reach performances similar to state-of-the-art visual BCIs based on covert attention. These results represent an important step towards a purely auditory BCI.

  4. 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.

  5. 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.

  6. Feature selection for multi-class problems by using pairwise-class and all-class techniques

    Science.gov (United States)

    You, Mingyu; Li, Guo-Zheng

    2011-05-01

    Feature selection has been a key technology in massive data processing, e.g. in microarray data analysis with few samples but high-dimensional genes. One common problem in multi-class microarray data analysis is the unbalanced recognition or prediction accuracies among classes, which usually leads to poor system performance. One of the main reasons is the unfair feature (gene) selection method. In this paper, a novel feature selection framework by using pairwise-class and all-class techniques (namely FrPA) is proposed to balance the performance among classes and improve the average accuracy. The feature (gene) rank list on all classes and the lists on each pair of classes are all taken into consideration during feature selection. The strategy of round-robin is embedded into the framework to select final features from the different rank lists. Experimental results on several microarray data sets show that FrPA helps to achieve higher classification accuracy and balance the performance among classes.

  7. 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.

  8. 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.

  9. 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.

  10. 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...

  11. 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’).

  12. 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.

  13. 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%.

  14. 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.

  15. Multi-objective metaheuristics for preprocessing EEG data in brain-computer interfaces

    Science.gov (United States)

    Aler, Ricardo; Vega, Alicia; Galván, Inés M.; Nebro, Antonio J.

    2012-03-01

    In the field of brain-computer interfaces, one of the main issues is to classify the electroencephalogram (EEG) accurately. EEG signals have a good temporal resolution, but a low spatial one. In this article, metaheuristics are used to compute spatial filters to improve the spatial resolution. Additionally, from a physiological point of view, not all frequency bands are equally relevant. Both spatial filters and relevant frequency bands are user-dependent. In this article a multi-objective formulation for spatial filter optimization and frequency-band selection is proposed. Several multi-objective metaheuristics have been tested for this purpose. The experimental results show, in general, that multi-objective algorithms are able to select a subset of the available frequency bands, while maintaining or improving the accuracy obtained with the whole set. Also, among the different metaheuristics tested, GDE3, which is based on differential evolution, is the most useful algorithm in this context.

  16. 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.

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

    Science.gov (United States)

    Zhang, Rui; Xu, Peng; Guo, Lanjin; Zhang, Yangsong; Li, Peiyang; Yao, Dezhong

    2013-01-01

    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.

  18. 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.

  19. Performance Measurement for Brain-Computer or Brain-Machine Interfaces: A Tutorial

    Science.gov (United States)

    Thompson, David E.; Quitadamo, Lucia R.; Mainardi, Luca; Laghari, Khalil ur Rehman; Gao, Shangkai; Kindermans, Pieter-Jan; Simeral, John D.; Fazel-Rezai, Reza; Matteucci, Matteo; Falk, Tiago H.; Bianchi, Luigi; Chestek, Cynthia A.; Huggins, Jane E.

    2014-01-01

    Objective Brain-Computer Interfaces (BCIs) have the potential to be valuable clinical tools. However, the varied nature of BCIs, combined with the large number of laboratories participating in BCI research, makes uniform performance reporting difficult. To address this situation, we present a tutorial on performance measurement in BCI research. Approach A workshop on this topic was held at the 2013 International BCI Meeting at Asilomar Conference Center in Pacific Grove, California. This manuscript contains the consensus opinion of the workshop members, refined through discussion in the following months and the input of authors who were unable to attend the workshop. Main Results Checklists for methods reporting were developed for both discrete and continuous BCIs. Relevant metrics are reviewed for different types of BCI research, with notes on their application to encourage uniform application between laboratories. Significance Graduate students and other researchers new to BCI research may find this tutorial a helpful introduction to performance measurement in the field. PMID:24838070

  20. Brain-computer interfaces for dissecting cognitive processes underlying sensorimotor control.

    Science.gov (United States)

    Golub, Matthew D; Chase, Steven M; Batista, Aaron P; Yu, Byron M

    2016-04-01

    Sensorimotor control engages cognitive processes such as prediction, learning, and multisensory integration. Understanding the neural mechanisms underlying these cognitive processes with arm reaching is challenging because we currently record only a fraction of the relevant neurons, the arm has nonlinear dynamics, and multiple modalities of sensory feedback contribute to control. A brain-computer interface (BCI) is a well-defined sensorimotor loop with key simplifying advantages that address each of these challenges, while engaging similar cognitive processes. As a result, BCI is becoming recognized as a powerful tool for basic scientific studies of sensorimotor control. Here, we describe the benefits of BCI for basic scientific inquiries and review recent BCI studies that have uncovered new insights into the neural mechanisms underlying sensorimotor control.

  1. 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.

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

    Directory of Open Access Journals (Sweden)

    Benjamin Blankertz

    2016-11-01

    Full Text Available The combined effect of fundamental results about neurocognitive processes and advancements in decoding mental states from ongoing brain signals has brought forth a whole range of potential neurotechnological applications. In this article, we review our developments in this area and put them into perspective. These examples cover a wide range of maturity levels with respect to their applicability. While we assume we are still a long way away from integrating Brain-Computer Interface (BCI technology in general interaction with computers, or from implementing neurotechnological measures in safety-critical workplaces, results have already now been obtained involving a BCI as research tool. In this article, we discuss the reasons why, in some of the prospective application domains, considerable effort is still required to make the systems ready to deal with the full complexity of the real world.

  9. 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.

  10. 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.

  11. 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.

  12. [Design and implementation of controlling smart car systems using P300 brain-computer interface].

    Science.gov (United States)

    Wang, Jinjia; Yang, Chengjie; Hu, Bei

    2013-04-01

    Using human electroencephalogram (EEG) to control external devices in order to achieve a variety of functions has been focus of the field of brain-computer interface (BCI) research. P300 is experiments which stimulate the eye to produce EEG by using letters flashing, and then identify the corresponding letters. In this paper, some improvements based on the P300 experiments were made??. Firstly, the matrix of flashing letters were modified into words which represent a certain sense. Secondly, the BCI2000 procedures were added with the corresponding source code. Thirdly, the smart car systems were designed using the radiofrequency signal. Finally it was realized that the evoked potentials were used to control the state of the smart car.

  13. 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.

  14. Incidental Diagnosis of Cerebral Cortical Venous Thrombosis in Postdural Puncture Headache on Brain Computed Tomography.

    Science.gov (United States)

    Humbertjean, Lisa; Ducrocq, Xavier; Lacour, Jean-Christophe; Mione, Gioia; Richard, Sébastien

    2015-01-01

    Diagnosis of cerebral cortical venous thrombosis in patients with postdural puncture headache (PDPH) is usually secondary to changes in headache pattern or cerebral infarctions. Nevertheless, incidental discovery of asymptomatic forms on brain imaging has never been reported before and its management thus remains ill-defined. We describe 2 cases of patients with asymptomatic cortical vein thrombosis in the context of PDPH. In both cases, brain computed tomography (CT) scans showed an isolated cortical vein thrombosis without cerebral damage. Neurological examination revealed the typical orthostatic feature of PDPH, independently of cortical vein thrombosis which was considered as a radiological incidental finding. Clinical and radiological signs resolved after bed rest, oral caffeine, and anticoagulation therapy. Asymptomatic cortical vein thrombosis may be found on radiological exploration, even basic like brain CT scan without contrast, of PDPH. Utility of anticoagulation therapy, which could increase the risk of cerebral hemorrhagic complications in this specific context, has to be assessed.

  15. 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.

  16. 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.

  17. [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.

  18. fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment

    Directory of Open Access Journals (Sweden)

    Ranganatha Sitaram

    2007-01-01

    Full Text Available Brain-computer interfaces based on functional magnetic resonance imaging (fMRI-BCI allow volitional control of anatomically specific regions of the brain. Technological advancement in higher field MRI scanners, fast data acquisition sequences, preprocessing algorithms, and robust statistical analysis are anticipated to make fMRI-BCI more widely available and applicable. This noninvasive technique could potentially complement the traditional neuroscientific experimental methods by varying the activity of the neural substrates of a region of interest as an independent variable to study its effects on behavior. If the neurobiological basis of a disorder (e.g., chronic pain, motor diseases, psychopathy, social phobia, depression is known in terms of abnormal activity in certain regions of the brain, fMRI-BCI can be targeted to modify activity in those regions with high specificity for treatment. In this paper, we review recent results of the application of fMRI-BCI to neuroscientific research and psychophysiological treatment.

  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. 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.

  1. Auditory Integration Training

    Directory of Open Access Journals (Sweden)

    Zahra Jafari

    2002-07-01

    Full Text Available Auditory integration training (AIT is a hearing enhancement training process for sensory input anomalies found in individuals with autism, attention deficit hyperactive disorder, dyslexia, hyperactivity, learning disability, language impairments, pervasive developmental disorder, central auditory processing disorder, attention deficit disorder, depressin, and hyperacute hearing. AIT, recently introduced in the United States, and has received much notice of late following the release of The Sound of a Moracle, by Annabel Stehli. In her book, Mrs. Stehli describes before and after auditory integration training experiences with her daughter, who was diagnosed at age four as having autism.

  2. 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)

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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...

  8. 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...

  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. 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.

  11. 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.

  12. 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

  13. Self-calibration algorithm in an asynchronous P300-based brain-computer interface

    Science.gov (United States)

    Schettini, F.; Aloise, F.; Aricò, P.; Salinari, S.; Mattia, D.; Cincotti, F.

    2014-06-01

    Objective. Reliability is a desirable characteristic of brain-computer interface (BCI) systems when they are intended to be used under non-experimental operating conditions. In addition, their overall usability is influenced by the complex and frequent procedures that are required for configuration and calibration. Earlier studies examined the issue of asynchronous control in P300-based BCIs, introducing dynamic stopping and automatic control suspension features. This report proposes and evaluates an algorithm for the automatic recalibration of the classifier's parameters using unsupervised data. Approach. Ten healthy subjects participated in five P300-based BCI sessions throughout a single day. First, we examined whether continuous adaptation of control parameters improved the accuracy of the asynchronous system over time. Then, we assessed the performance of the self-calibration algorithm with respect to the no-recalibration and supervised calibration conditions with regard to system accuracy and communication efficiency. Main results. Offline tests demonstrated that continuous adaptation of the control parameters significantly increased the communication efficiency of asynchronous P300-based BCIs. The self-calibration algorithm correctly assigned labels to unsupervised data with 95% accuracy, effecting communication efficiency that was comparable with that of supervised repeated calibration. Significance. Although additional online tests that involve end-users under non-experimental conditions are needed, these preliminary results are encouraging, from which we conclude that the self-calibration algorithm is a promising solution to improve P300-based BCI usability and reliability.

  14. 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.

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. P300-based brain-computer interface for environmental control: an asynchronous approach

    Science.gov (United States)

    Aloise, F.; Schettini, F.; Aricò, P.; Leotta, F.; Salinari, S.; Mattia, D.; Babiloni, F.; Cincotti, F.

    2011-04-01

    Brain-computer interface (BCI) systems allow people with severe motor disabilities to communicate and interact with the external world. The P300 potential is one of the most used control signals for EEG-based BCIs. Classic P300-based BCIs work in a synchronous mode; the synchronous control assumes that the user is constantly attending to the stimulation, and the number of stimulation sequences is fixed a priori. This issue is an obstacle for the use of these systems in everyday life; users will be engaged in a continuous control state, their distractions will cause misclassification and the speed of selection will not take into account users' current psychophysical condition. An efficient BCI system should be able to understand the user's intentions from the ongoing EEG instead. Also, it has to refrain from making a selection when the user is engaged in a different activity and it should increase or decrease its speed of selection depending on the current user's state. We addressed these issues by introducing an asynchronous BCI and tested its capabilities for effective environmental monitoring, involving 11 volunteers in three recording sessions. Results show that this BCI system can increase the bit rate during control periods while the system is proved to be very efficient in avoiding false negatives when the users are engaged in other tasks.

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

    Directory of Open Access Journals (Sweden)

    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.

  1. Learning from feedback training data at a self-paced brain-computer interface

    Science.gov (United States)

    Zhang, Haihong; Liyanage, Sidath Ravindra; Wang, Chuanchu; Guan, Cuntai

    2011-08-01

    Inherent changes that appear in brain signals when transferring from calibration to feedback sessions are a challenging but critical issue in brain-computer interface (BCI) applications. While previous studies have mostly focused on the adaptation of classifiers, in this paper we study the feasibility and the importance of the adaptation of feature extraction in a self-paced BCI paradigm. First, we conduct calibration and feedback training on able-bodied naïve subjects using a new self-paced motor imagery BCI including the idle state. The online results suggest that the feature space constructed from calibration data may become ineffective during feedback sessions. Hence, we propose a new supervised method that learns from a feedback session to construct a more appropriate feature space, on the basis of the maximum mutual information principle between feedback signal, target signal and EEG. Specifically, we formulate the learning objective as maximizing a kernel-based mutual information estimate with respect to the spatial-spectral filtering parameters. We then derive a gradient-based optimization algorithm for the learning task. An experimental study is conducted using offline simulation. The results show that the proposed method is able to construct effective feature spaces to capture the discriminative information in feedback training data and, consequently, the prediction error can be significantly reduced using the new features.

  2. 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.

  3. Using the electrocorticographic speech network to control a brain-computer interface in humans

    Science.gov (United States)

    Leuthardt, Eric C.; Gaona, Charles; Sharma, Mohit; Szrama, Nicholas; Roland, Jarod; Freudenberg, Zac; Solis, Jamie; Breshears, Jonathan; Schalk, Gerwin

    2011-06-01

    Electrocorticography (ECoG) has emerged as a new signal platform for brain-computer interface (BCI) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been brain signals from the sensorimotor cortex. Hence, it was unknown whether other neurophysiological substrates, such as the speech network, could be used to further improve on or complement existing motor-based control paradigms. We demonstrate here for the first time that ECoG signals associated with different overt and imagined phoneme articulation can enable invasively monitored human patients to control a one-dimensional computer cursor rapidly and accurately. This phonetic content was distinguishable within higher gamma frequency oscillations and enabled users to achieve final target accuracies between 68% and 91% within 15 min. Additionally, one of the patients achieved robust control using recordings from a microarray consisting of 1 mm spaced microwires. These findings suggest that the cortical network associated with speech could provide an additional cognitive and physiologic substrate for BCI operation and that these signals can be acquired from a cortical array that is small and minimally invasive.

  4. 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.

  5. Novel hold-release functionality in a P300 brain-computer interface

    Science.gov (United States)

    Alcaide-Aguirre, R. E.; Huggins, J. E.

    2014-12-01

    Assistive technology control interface theory describes interface activation and interface deactivation as distinct properties of any control interface. Separating control of activation and deactivation allows precise timing of the duration of the activation. Objective. We propose a novel P300 brain-computer interface (BCI) functionality with separate control of the initial activation and the deactivation (hold-release) of a selection. Approach. Using two different layouts and off-line analysis, we tested the accuracy with which subjects could (1) hold their selection and (2) quickly change between selections. Main results. Mean accuracy across all subjects for the hold-release algorithm was 85% with one hold-release classification and 100% with two hold-release classifications. Using a layout designed to lower perceptual errors, accuracy increased to a mean of 90% and the time subjects could hold a selection was 40% longer than with the standard layout. Hold-release functionality provides improved response time (6-16 times faster) over the initial P300 BCI selection by allowing the BCI to make hold-release decisions from very few flashes instead of after multiple sequences of flashes. Significance. For the BCI user, hold-release functionality allows for faster, more continuous control with a P300 BCI, creating new options for BCI applications.

  6. A high-speed brain-computer interface (BCI) using dry EEG electrodes

    Science.gov (United States)

    Spüler, Martin

    2017-01-01

    Recently, brain-computer interfaces (BCIs) based on visual evoked potentials (VEPs) have been shown to achieve remarkable communication speeds. As they use electroencephalography (EEG) as non-invasive method for recording neural signals, the application of gel-based EEG is time-consuming and cumbersome. In order to achieve a more user-friendly system, this work explores the usability of dry EEG electrodes with a VEP-based BCI. While the results show a high variability between subjects, they also show that communication speeds of more than 100 bit/min are possible using dry EEG electrodes. To reduce performance variability and deal with the lower signal-to-noise ratio of the dry EEG electrodes, an averaging method and a dynamic stopping method were introduced to the BCI system. Those changes were shown to improve performance significantly, leading to an average classification accuracy of 76% with an average communication speed of 46 bit/min, which is equivalent to a writing speed of 8.8 error-free letters per minute. Although the BCI system works substantially better with gel-based EEG, dry EEG electrodes are more user-friendly and still allow high-speed BCI communication. PMID:28225794

  7. 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.

  8. A 3D learning playground for potential attention training in ADHD: A brain computer interface approach.

    Science.gov (United States)

    Ali, Abdulla; Puthusserypady, Sadasivan

    2015-01-01

    This paper presents a novel brain-computer-interface (BCI) system that could potentially be used for enhancing the attention ability of subjects with attention deficit hyperactivity disorder (ADHD). It employs the steady state visual evoked potential (SSVEP) paradigm. The developed system consists of a 3D classroom environment with active 3D distractions and 2D games executed on the blackboard. The system is concealed as a game (with stages of varying difficulty) with an underlying story to motivate the subjects. It was tested on eleven healthy subjects and the results undeniably establish that by moving to a higher stage in the game where the 2D environment is changed to 3D along with the added 3D distractions, the difficulty level in keeping attention on the main task increases for the subjects. Results also show a mean accuracy of 92.26 ± 7.97% and a mean average selection time of 3.07 ± 1.09 seconds.

  9. 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.

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

    Directory of Open Access Journals (Sweden)

    Alessio Paolo Buccino

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

  11. 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.

  12. 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.

  13. Design of a mobile brain computer interface-based smart multimedia controller.

    Science.gov (United States)

    Tseng, Kevin C; Lin, Bor-Shing; Wong, Alice May-Kuen; Lin, Bor-Shyh

    2015-03-06

    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.

  14. 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.

  15. Current trends in hardware and software for brain-computer interfaces (BCIs).

    Science.gov (United States)

    Brunner, P; Bianchi, L; Guger, C; Cincotti, F; Schalk, G

    2011-04-01

    A brain-computer interface (BCI) provides a non-muscular communication channel to people with and without disabilities. BCI devices consist of hardware and software. BCI hardware records signals from the brain, either invasively or non-invasively, using a series of device components. BCI software then translates these signals into device output commands and provides feedback. One may categorize different types of BCI applications into the following four categories: basic research, clinical/translational research, consumer products, and emerging applications. These four categories use BCI hardware and software, but have different sets of requirements. For example, while basic research needs to explore a wide range of system configurations, and thus requires a wide range of hardware and software capabilities, applications in the other three categories may be designed for relatively narrow purposes and thus may only need a very limited subset of capabilities. This paper summarizes technical aspects for each of these four categories of BCI applications. The results indicate that BCI technology is in transition from isolated demonstrations to systematic research and commercial development. This process requires several multidisciplinary efforts, including the development of better integrated and more robust BCI hardware and software, the definition of standardized interfaces, and the development of certification, dissemination and reimbursement procedures.

  16. Brain-computer interface: changes in performance using virtual reality techniques.

    Science.gov (United States)

    Ron-Angevin, Ricardo; Díaz-Estrella, Antonio

    2009-01-09

    The ability to control electroencephalographic (EEG) signals when different mental tasks are carried out would provide a method of communication for people with serious motor function problems. This system is known as a brain-computer interface (BCI). Due to the difficulty of controlling one's own EEG signals, a suitable training protocol is required to motivate subjects, as it is necessary to provide some type of visual feedback allowing subjects to see their progress. Conventional systems of feedback are based on simple visual presentations, such as a horizontal bar extension. However, virtual reality is a powerful tool with graphical possibilities to improve BCI-feedback presentation. The objective of the study is to explore the advantages of the use of feedback based on virtual reality techniques compared to conventional systems of feedback. Sixteen untrained subjects, divided into two groups, participated in the experiment. A group of subjects was trained using a BCI system, which uses conventional feedback (bar extension), and another group was trained using a BCI system, which submits subjects to a more familiar environment, such as controlling a car to avoid obstacles. The obtained results suggest that EEG behaviour can be modified via feedback presentation. Significant differences in classification error rates between both interfaces were obtained during the feedback period, confirming that an interface based on virtual reality techniques can improve the feedback control, specifically for untrained subjects.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    Millán, J D R; Rupp, R; Müller-Putz, G R; Murray-Smith, R; Giugliemma, C; Tangermann, M; Vidaurre, C; Cincotti, F; Kübler, A; Leeb, R; Neuper, C; Müller, K-R; Mattia, D

    2010-01-01

    In recent years, new research has brought the field of electroencephalogram (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 and 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.

  19. Classification of EEG with structural feature dictionaries in a brain computer interface.

    Science.gov (United States)

    Göksu, Fikri; Ince, Nuri Firat; Tadipatri, Vijay Aditya; Tewfik, Ahmed H

    2008-01-01

    We present a new method for the classification of EEG in a brain computer interface by adapting subject specific features in spectral, temporal and spatial domain. For this particular purpose we extend our previous work on ECoG classification based on structural feature dictionary and apply it to extract the spectro-temporal patterns of multichannel EEG recordings related to a motor imagery task. The construction of the feature dictionary based on undecimated wavelet packet transform is extended to block FFT. We evaluate several subset selection algorithms to select a small number of features for final classification. We tested our proposed approach on five subjects of BCI Competition 2005 dataset- IVa. By adapting the wavelet filter for each subject, the algorithm achieved an average classification accuracy of 91.4% The classification results and characteristic of selected features indicate that the proposed algorithm can jointly adapt to EEG patterns in spectro-spatio-temporal domain and provide classification accuracies as good as existing methods used in the literature.

  20. 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.

  1. A reductionist approach to the analysis of learning in brain-computer interfaces.

    Science.gov (United States)

    Danziger, Zachary

    2014-04-01

    The complexity and scale of brain-computer interface (BCI) studies limit our ability to investigate how humans learn to use BCI systems. It also limits our capacity to develop adaptive algorithms needed to assist users with their control. Adaptive algorithm development is forced offline and typically uses static data sets. But this is a poor substitute for the online, dynamic environment where algorithms are ultimately deployed and interact with an adapting user. This work evaluates a paradigm that simulates the control problem faced by human subjects when controlling a BCI, but which avoids the many complications associated with full-scale BCI studies. Biological learners can be studied in a reductionist way as they solve BCI-like control problems, and machine learning algorithms can be developed and tested in closed loop with the subjects before being translated to full BCIs. The method is to map 19 joint angles of the hand (representing neural signals) to the position of a 2D cursor which must be piloted to displayed targets (a typical BCI task). An investigation is presented on how closely the joint angle method emulates BCI systems; a novel learning algorithm is evaluated, and a performance difference between genders is discussed.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

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

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    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.

  9. 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.

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

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    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.

  11. Binary Color Classification For Brain Computer Interface Using Neural Networks And Support Vector Machines

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    Charmi Sunil Mehta

    2014-04-01

    Full Text Available As the power of modern computers grows alongside our understanding of the human brain, we move a step closer in transforming some pretty spectacular science fiction into reality. The advent of Brain Computer Interface (BCI is indeed leading us to a burgeoning era of complete automation empowering our interaction with computer not only with robustness but with also a gift of intelligence. For the fraction of our society suffering from severe motor disabilities BCI has offered a novel solution of overcoming the problems faced in communicating and environment control. Thus the purpose of our current research is to harness the brain‟s ability to generate Visually Evoked Potentials (VEPs by capturing the response of the brain to the transitions of color from grey to green and grey to red. Our prime focus is to explore EEG-based signal processing techniques in order to classify two colors; which can be further deployed in future by coupling the actuators so as to perform few basic tasks. The extracted EEG features are classified using Support Vector Machines (SVM and Artificial Neural Networks (ANN. We recorded 100% accuracy on testing the model after training and validation process. Moreover, we obtained 90% accuracy on re-testing the model with all samples acquired for the task using Quadratic SVM classifier.

  12. Automated selection of brain regions for real-time fMRI brain-computer interfaces

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    Lührs, Michael; Sorger, Bettina; Goebel, Rainer; Esposito, Fabrizio

    2017-02-01

    Objective. Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI. Approach. 3 T fMRI data of six volunteers were re-analyzed in simulated real-time. During a localizer run, participants performed three mental tasks following visual cues. During two communication runs, a letter-spelling display guided the subjects to freely encode letters by performing one of the mental tasks with a specific timing. GLM- and ICA-based procedures were used to decode each letter, respectively using compact ROIs and whole-brain distributed spatio-temporal patterns of fMRI activity, automatically defined from subject-specific or group-level maps. Main results. Letter-decoding performances were comparable to supervised methods. In combination with a similarity-based criterion, GLM- and ICA-based approaches successfully decoded more than 80% (average) of the letters. Subject-specific maps yielded optimal performances. Significance. Automated solutions for ROI selection may help accelerating the translation of rt-fMRI BCIs from research to clinical applications.

  13. 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.

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

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    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

  15. Evaluation of different EEG acquisition systems concerning their suitability for building a brain-computer interface

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    Andreas Pinegger

    2016-09-01

    Full Text Available One important aspect in non-invasive brain-computer interface (BCI research is to acquire the electroencephalogram (EEG in a proper way. From an end-user perspective this means with maximum comfort and without any extra inconveniences (e.g., washing the hair. Whereas from a technical perspective, the signal quality has to be optimal to make the BCI work effectively and efficiently.In this work we evaluated three different commercially available EEG acquisition systems that differ in the type of electrode (gel-, water-, and dry-based, the amplifier technique, and the data transmission method. Every system was tested regarding three different aspects, namely, technical, BCI effectiveness and efficiency (P300 communication and control, and user satisfaction (comfort.We found that the water-based system had the lowest short circuit noise level, the hydrogel-based system had the highest P300 spelling accuracies, and the dry electrode system caused the least inconveniences.Therefore, building a reliable BCI is possible with all evaluated systems and it is on the user to decide which system meets the given requirements best.

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

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    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.

  17. 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.

  18. 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.

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

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    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.

  20. An Asynchronous P300-Based Brain-Computer Interface Web Browser for Severely Disabled People.

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    Martinez-Cagigal, Victor; Gomez-Pilar, Javier; Alvarez, Daniel; Hornero, Roberto

    2016-10-31

    This paper presents an electroencephalographic (EEG) P300-based brain-computer interface (BCI) Internet browser. The system uses the "odd-ball" row-col paradigm for generating the P300 evoked potentials on the scalp of the user, which are immediately processed and translated into web browser commands. There were previous approaches for controlling a BCI web browser. However, to the best of our knowledge, none of them was focused on an assistive context, failing to test their applications with a suitable number of end users. In addition, all of them were synchronous applications, where it was necessary to introduce a "read-mode" command in order to avoid a continuous command selection. Thus, the aim of this study is twofold: (i) to test our web browser with a population of multiple sclerosis (MS) patients in order to assess the usefulness of our proposal to meet their daily communication needs; and (ii) to overcome the aforementioned limitation by adding a threshold that discerns between control and non-control states, allowing the user to calmly read the web page without undesirable selections. The browser was tested with sixteen MS patients and five healthy volunteers. Both quantitative and qualitative metrics were obtained. MS participants reached an average accuracy of 84.14%, whereas 95.75% was achieved by control subjects. Results show that MS patients can successfully control the BCI web browser, improving their personal autonomy.

  1. Spectral Transfer Learning using Information Geometry for a User-Independent Brain-Computer Interface

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    Nicholas Roy Waytowich

    2016-09-01

    Full Text Available 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 offline 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.

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

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    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.

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

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    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.

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

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    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.

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

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    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.

  6. A Study on a Brain-Computer Interface for Motor Assist by Prefrontal Cortex

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    Misawa, Tadanobu; Takano, Shinya; Shimokawa, Tetsuya; Hirobayashi, Shigeki

    In recent times, considerable research has been conducted on the development of brain-computer interfaces (BCIs). Although there have been several reports on BCIs that assist motor functions by measurement of brain activity in the motor cortex, only a few studies have reported on BCI that assist motor functions by measurement of activity in areas other than the motor cortex. In this study, we experimentally develop a BCI that assists motor functions on the basis of brain activity in the prefrontal cortex. In this BCI system, subjects are shown the labyrinth problem. Concretely, brain activity is measured using fNIRS and the data are acquired in real time. The signal processing module implements low pass filtering of these signals. Further, the pattern classification module used in this system currently is a support vector machine. 22 subjects, both male and female, volunteered to participate in this experiment. 8 of these 22 subjects were able to solve the labyrinth problem. In this experiment, we could not obtain a high distinction. However, these results show that it is possible to develop BCI systems that assist motor functions using information from the prefrontal cortex.

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

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    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. A Generalizable Brain-Computer Interface (BCI Using Machine Learning for Feature Discovery.

    Directory of Open Access Journals (Sweden)

    Ewan S Nurse

    Full Text Available This work describes a generalized method for classifying motor-related neural signals for a brain-computer interface (BCI, based on a stochastic machine learning method. The method differs from the various feature extraction and selection techniques employed in many other BCI systems. The classifier does not use extensive a-priori information, resulting in reduced reliance on highly specific domain knowledge. Instead of pre-defining features, the time-domain signal is input to a population of multi-layer perceptrons (MLPs in order to perform a stochastic search for the best structure. The results showed that the average performance of the new algorithm outperformed other published methods using the Berlin BCI IV (2008 competition dataset and was comparable to the best results in the Berlin BCI II (2002-3 competition dataset. The new method was also applied to electroencephalography (EEG data recorded from five subjects undertaking a hand squeeze task and demonstrated high levels of accuracy with a mean classification accuracy of 78.9% after five-fold cross-validation. Our new approach has been shown to give accurate results across different motor tasks and signal types as well as between subjects.

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

    Science.gov (United States)

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

    2016-04-01

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

  10. New KF-PP-SVM classification method for EEG in brain-computer interfaces.

    Science.gov (United States)

    Yang, Banghua; Han, Zhijun; Zan, Peng; Wang, Qian

    2014-01-01

    Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.

  11. sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces.

    Science.gov (United States)

    Jrad, N; Congedo, M; Phlypo, R; Rousseau, S; Flamary, R; Yger, F; Rakotomamonjy, A

    2011-10-01

    In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.

  12. 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.

  13. 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.

  14. 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.

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

    Directory of Open Access Journals (Sweden)

    Reinhold Scherer

    2007-01-01

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

  16. 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.

  17. Feasibility of a Hybrid Brain-Computer Interface for Advanced Functional Electrical Therapy

    Directory of Open Access Journals (Sweden)

    Andrej M. Savić

    2014-01-01

    Full Text Available We present a feasibility study of a novel hybrid brain-computer interface (BCI system for advanced functional electrical therapy (FET of grasp. FET procedure is improved with both automated stimulation pattern selection and stimulation triggering. The proposed hybrid BCI comprises the two BCI control signals: steady-state visual evoked potentials (SSVEP and event-related desynchronization (ERD. The sequence of the two stages, SSVEP-BCI and ERD-BCI, runs in a closed-loop architecture. The first stage, SSVEP-BCI, acts as a selector of electrical stimulation pattern that corresponds to one of the three basic types of grasp: palmar, lateral, or precision. In the second stage, ERD-BCI operates as a brain switch which activates the stimulation pattern selected in the previous stage. The system was tested in 6 healthy subjects who were all able to control the device with accuracy in a range of 0.64–0.96. The results provided the reference data needed for the planned clinical study. This novel BCI may promote further restoration of the impaired motor function by closing the loop between the “will to move” and contingent temporally synchronized sensory feedback.

  18. 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.

  19. 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.

  20. 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.

  1. A multi-signature brain-computer interface: use of transient and steady-state responses

    Science.gov (United States)

    Severens, Marianne; Farquhar, Jason; Duysens, Jacques; Desain, Peter

    2013-04-01

    Objective. The aim of this paper was to increase the information transfer in brain-computer interfaces (BCI). Therefore, a multi-signature BCI was developed and investigated. Stimuli were designed to simultaneously evoke transient somatosensory event-related potentials (ERPs) and steady-state somatosensory potentials (SSSEPs) and the ERPs and SSSEPs in isolation. Approach. Twelve subjects participated in two sessions. In the first session, the single and combined stimulation conditions were compared on these somatosensory responses and on the classification performance. In the second session the on-line performance with the combined stimulation was evaluated while subjects received feedback. Furthermore, in both sessions, the performance based on ERP and SSSEP features was compared. Main results. No difference was found in the ERPs and SSSEPs between stimulation conditions. The combination of ERP and SSSEP features did not perform better than with ERP features only. In both sessions, the classification performances based on ERP and combined features were higher than the classification based on SSSEP features. Significance. Although the multi-signature BCI did not increase performance, it also did not negatively impact it. Therefore, such stimuli could be used and the best performing feature set could then be chosen individually.

  2. 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.

  3. 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...

  4. 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

  5. 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.

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

    OpenAIRE

    Corraleja Palacios, Rebeca; Hornero Sánchez, Roberto; Álvarez González, Daniel; Martín González, Laura

    2012-01-01

    The aim of this study was to develop a Brain Computer Interface (BCI) application to control domotic devices usually present at home. Previous studies have shown that people with severe disabilities, both physical and cognitive ones, do not achieve high accuracy results using motor imagery-based BCIs. To overcome this limitation, we propose the implementation of a BCI application using P300 evoked potentials, because neither extensive training nor extremely high concentration level are requir...

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

    Directory of Open Access Journals (Sweden)

    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.

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

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    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.

  9. 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.

  10. 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.

  11. Electroencephalography (EEG)-based neurofeedback training for brain-computer interface (BCI).

    Science.gov (United States)

    Choi, Kyuwan

    2013-11-01

    Electroencephalography has become a popular tool in basic brain research, but in recent years, several practical limitations have been highlighted. Some of the drawbacks pertain to the offline analyses of the neural signal that prevent the subjects from engaging in real-time error correction during learning. Other limitations include the complex nature of the visual stimuli, often inducing fatigue and introducing considerable delays, possibly interfering with spontaneous performance. By replacing the complex external visual input with internally driven motor imagery, we can overcome some delay problems, at the expense of losing the ability to precisely parameterize features of the input stimulus. To address these issues, we here introduce a nontrivial modification to brain-computer Interfaces (BCI). We combine the fast signal processing of motor imagery with the ability to parameterize external visual feedback in the context of a very simple control task: attempting to intentionally control the direction of an external cursor on command. By engaging the subject in motor imagery while providing real-time visual feedback on their instantaneous performance, we can take advantage of positive features present in both externally- and internally driven learning. We further use a classifier that automatically selects the cortical activation features that most likely maximize the performance accuracy. Under this closed loop coadaptation system, we saw a progression of the cortical activation that started in sensorymotor areas, when at chance performance motor imagery was explicitly used, migrated to BA6 under deliberate control and ended in the more frontal regions of prefrontal cortex, when at maximal performance accuracy, the subjects reportedly developed spontaneous mental control of the instructed direction. We discuss our results in light of possible applications of this simple BCI paradigm to study various cognitive phenomena involving the deliberate control of a

  12. High performance communication by people with paralysis using an intracortical brain-computer interface

    Science.gov (United States)

    Pandarinath, Chethan; Nuyujukian, Paul; Blabe, Christine H; Sorice, Brittany L; Saab, Jad; Willett, Francis R; Hochberg, Leigh R

    2017-01-01

    Brain-computer interfaces (BCIs) have the potential to restore communication for people with tetraplegia and anarthria by translating neural activity into control signals for assistive communication devices. While previous pre-clinical and clinical studies have demonstrated promising proofs-of-concept (Serruya et al., 2002; Simeral et al., 2011; Bacher et al., 2015; Nuyujukian et al., 2015; Aflalo et al., 2015; Gilja et al., 2015; Jarosiewicz et al., 2015; Wolpaw et al., 1998; Hwang et al., 2012; Spüler et al., 2012; Leuthardt et al., 2004; Taylor et al., 2002; Schalk et al., 2008; Moran, 2010; Brunner et al., 2011; Wang et al., 2013; Townsend and Platsko, 2016; Vansteensel et al., 2016; Nuyujukian et al., 2016; Carmena et al., 2003; Musallam et al., 2004; Santhanam et al., 2006; Hochberg et al., 2006; Ganguly et al., 2011; O’Doherty et al., 2011; Gilja et al., 2012), the performance of human clinical BCI systems is not yet high enough to support widespread adoption by people with physical limitations of speech. Here we report a high-performance intracortical BCI (iBCI) for communication, which was tested by three clinical trial participants with paralysis. The system leveraged advances in decoder design developed in prior pre-clinical and clinical studies (Gilja et al., 2015; Kao et al., 2016; Gilja et al., 2012). For all three participants, performance exceeded previous iBCIs (Bacher et al., 2015; Jarosiewicz et al., 2015) as measured by typing rate (by a factor of 1.4–4.2) and information throughput (by a factor of 2.2–4.0). This high level of performance demonstrates the potential utility of iBCIs as powerful assistive communication devices for people with limited motor function. Clinical Trial No: NCT00912041 DOI: http://dx.doi.org/10.7554/eLife.18554.001 PMID:28220753

  13. High performance communication by people with paralysis using an intracortical brain-computer interface.

    Science.gov (United States)

    Pandarinath, Chethan; Nuyujukian, Paul; Blabe, Christine H; Sorice, Brittany L; Saab, Jad; Willett, Francis R; Hochberg, Leigh R; Shenoy, Krishna V; Henderson, Jaimie M

    2017-02-21

    Brain-computer interfaces (BCIs) have the potential to restore communication for people with tetraplegia and anarthria by translating neural activity into control signals for assistive communication devices. While previous pre-clinical and clinical studies have demonstrated promising proofs-of-concept (Serruya et al., 2002; Simeral et al., 2011; Bacher et al., 2015; Nuyujukian et al., 2015; Aflalo et al., 2015; Gilja et al., 2015; Jarosiewicz et al., 2015; Wolpaw et al., 1998; Hwang et al., 2012; Spüler et al., 2012; Leuthardt et al., 2004; Taylor et al., 2002; Schalk et al., 2008; Moran, 2010; Brunner et al., 2011; Wang et al., 2013; Townsend and Platsko, 2016; Vansteensel et al., 2016; Nuyujukian et al., 2016; Carmena et al., 2003; Musallam et al., 2004; Santhanam et al., 2006; Hochberg et al., 2006; Ganguly et al., 2011; O'Doherty et al., 2011; Gilja et al., 2012), the performance of human clinical BCI systems is not yet high enough to support widespread adoption by people with physical limitations of speech. Here we report a high-performance intracortical BCI (iBCI) for communication, which was tested by three clinical trial participants with paralysis. The system leveraged advances in decoder design developed in prior pre-clinical and clinical studies (Gilja et al., 2015; Kao et al., 2016; Gilja et al., 2012). For all three participants, performance exceeded previous iBCIs (Bacher et al., 2015; Jarosiewicz et al., 2015) as measured by typing rate (by a factor of 1.4-4.2) and information throughput (by a factor of 2.2-4.0). This high level of performance demonstrates the potential utility of iBCIs as powerful assistive communication devices for people with limited motor function.Clinical Trial No: NCT00912041.

  14. Neural correlates of user-initiated motor success and failure - A brain-computer interface perspective.

    Science.gov (United States)

    Yazmir, Boris; Reiner, Miriam

    2016-11-02

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

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

    Science.gov (United States)

    Mrachacz-Kersting, Natalie; Jiang, Ning; Stevenson, Andrew James Thomas; Niazi, Imran Khan; Kostic, Vladimir; Pavlovic, Aleksandra; Radovanovic, Sasa; Djuric-Jovicic, Milica; Agosta, Federica; Dremstrup, Kim; Farina, Dario

    2016-03-01

    Brain-computer interfaces (BCIs) have the potential to improve functionality in chronic stoke patients when applied over a large number of sessions. Here we evaluated the effect and the underlying mechanisms of three BCI training sessions in a double-blind sham-controlled design. The applied BCI is based on Hebbian principles of associativity that hypothesize that neural assemblies activated in a correlated manner will strengthen synaptic connections. Twenty-two chronic stroke patients were divided into two training groups. Movement-related cortical potentials (MRCPs) were detected by electroencephalography during repetitions of foot dorsiflexion. Detection triggered a single electrical stimulation of the common peroneal nerve timed so that the resulting afferent volley arrived at the peak negative phase of the MRCP (BCIassociative group) or randomly (BCInonassociative group). Fugl-Meyer motor assessment (FM), 10-m walking speed, foot and hand tapping frequency, diffusion tensor imaging (DTI) data, and the excitability of the corticospinal tract to the target muscle [tibialis anterior (TA)] were quantified. The TA motor evoked potential (MEP) increased significantly after the BCIassociative intervention, but not for the BCInonassociative group. FM 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. Corticospinal tract integrity on DTI did not correlate with clinical or physiological changes. For the BCI as applied here, the precise coupling between the brain command and the afferent signal was imperative for the behavioral, clinical, and neurophysiological changes reported. This association may become the driving principle for the design of BCI rehabilitation in the future. Indeed, no available BCIs can match this degree of functional improvement with such a short intervention.

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

    Directory of Open Access Journals (Sweden)

    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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. Non invasive Brain-Computer Interface system: towards its application as assistive technology

    Science.gov (United States)

    Cincotti, Febo; Mattia, Donatella; Aloise, Fabio; Bufalari, Simona; Schalk, Gerwin; Oriolo, Giuseppe; Cherubini, Andrea; Marciani, Maria Grazia; Babiloni, Fabio

    2010-01-01

    The quality of life of people suffering from severe motor disabilities can benefit from the use of current assistive technology capable of ameliorating communication, house-environment management and mobility, according to the user's residual motor abilities. Brain Computer Interfaces (BCIs) are systems that can translate brain activity into signals that control external devices. Thus they can represent the only technology for severely paralyzed patients to increase or maintain their communication and control options. Here we report on a pilot study in which a system was implemented and validated to allow disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Patients (n=14) with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program carried out in a house-like furnished space. All users utilized regular assistive control options (e.g., microswitches or head trackers). In addition, four subjects learned to operate the system by means of a non-invasive EEG-based BCI. This system was controlled by the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp; this skill was learnt even though the subjects have not had control over their limbs for a long time. We conclude that such a prototype system, which integrates several different assistive technologies including a BCI system, can potentially facilitate the translation from pre-clinical demonstrations to a clinical useful BCI. PMID:18394526

  4. 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.

  5. Adaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces

    Science.gov (United States)

    Lu, Jun; McFarland, Dennis J.; Wolpaw, Jonathan R.

    2013-02-01

    Objective. Sensorimotor rhythms (SMRs) are 8-30 Hz oscillations in the electroencephalogram (EEG) recorded from the scalp over sensorimotor cortex that change with movement and/or movement imagery. Many brain-computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and other objects in one, two or three dimensions. At the same time, if SMR-based BCIs are to be useful for people with neuromuscular disabilities, their accuracy and reliability must be improved substantially. These BCIs often use spatial filtering methods such as common average reference (CAR), Laplacian (LAP) filter or common spatial pattern (CSP) filter to enhance the signal-to-noise ratio of EEG. Here, we test the hypothesis that a new filter design, called an ‘adaptive Laplacian (ALAP) filter’, can provide better performance for SMR-based BCIs. Approach. An ALAP filter employs a Gaussian kernel to construct a smooth spatial gradient of channel weights and then simultaneously seeks the optimal kernel radius of this spatial filter and the regularization parameter of linear ridge regression. This optimization is based on minimizing the leave-one-out cross-validation error through a gradient descent method and is computationally feasible. Main results. Using a variety of kinds of BCI data from a total of 22 individuals, we compare the performances of ALAP filter to CAR, small LAP, large LAP and CSP filters. With a large number of channels and limited data, ALAP performs significantly better than CSP, CAR, small LAP and large LAP both in classification accuracy and in mean-squared error. Using fewer channels restricted to motor areas, ALAP is still superior to CAR, small LAP and large LAP, but equally matched to CSP. Significance. Thus, ALAP may help to improve the accuracy and robustness of SMR-based BCIs.

  6. An independent SSVEP-based brain-computer interface in locked-in syndrome

    Science.gov (United States)

    Lesenfants, D.; Habbal, D.; Lugo, Z.; Lebeau, M.; Horki, P.; Amico, E.; Pokorny, C.; Gómez, F.; Soddu, A.; Müller-Putz, G.; Laureys, S.; Noirhomme, Q.

    2014-06-01

    Objective. Steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs) allow healthy subjects to communicate. However, their dependence on gaze control prevents their use with severely disabled patients. Gaze-independent SSVEP-BCIs have been designed but have shown a drop in accuracy and have not been tested in brain-injured patients. In the present paper, we propose a novel independent SSVEP-BCI based on covert attention with an improved classification rate. We study the influence of feature extraction algorithms and the number of harmonics. Finally, we test online communication on healthy volunteers and patients with locked-in syndrome (LIS). Approach. Twenty-four healthy subjects and six LIS patients participated in this study. An independent covert two-class SSVEP paradigm was used with a newly developed portable light emitting diode-based ‘interlaced squares' stimulation pattern. Main results. Mean offline and online accuracies on healthy subjects were respectively 85 ± 2% and 74 ± 13%, with eight out of twelve subjects succeeding to communicate efficiently with 80 ± 9% accuracy. Two out of six LIS patients reached an offline accuracy above the chance level, illustrating a response to a command. One out of four LIS patients could communicate online. Significance. We have demonstrated the feasibility of online communication with a covert SSVEP paradigm that is truly independent of all neuromuscular functions. The potential clinical use of the presented BCI system as a diagnostic (i.e., detecting command-following) and communication tool for severely brain-injured patients will need to be further explored.

  7. 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.

  8. Real-Time Control of an Articulatory-Based Speech Synthesizer for Brain Computer Interfaces

    Science.gov (United States)

    Bocquelet, Florent; Hueber, Thomas; Girin, Laurent; Savariaux, Christophe; Yvert, Blaise

    2016-01-01

    Restoring natural speech in paralyzed and aphasic people could be achieved using a Brain-Computer Interface (BCI) controlling a speech synthesizer in real-time. To reach this goal, a prerequisite is to develop a speech synthesizer producing intelligible speech in real-time with a reasonable number of control parameters. We present here an articulatory-based speech synthesizer that can be controlled in real-time for future BCI applications. This synthesizer converts movements of the main speech articulators (tongue, jaw, velum, and lips) into intelligible speech. The articulatory-to-acoustic mapping is performed using a deep neural network (DNN) trained on electromagnetic articulography (EMA) data recorded on a reference speaker synchronously with the produced speech signal. This DNN is then used in both offline and online modes to map the position of sensors glued on different speech articulators into acoustic parameters that are further converted into an audio signal using a vocoder. In offline mode, highly intelligible speech could be obtained as assessed by perceptual evaluation performed by 12 listeners. Then, to anticipate future BCI applications, we further assessed the real-time control of the synthesizer by both the reference speaker and new speakers, in a closed-loop paradigm using EMA data recorded in real time. A short calibration period was used to compensate for differences in sensor positions and articulatory differences between new speakers and the reference speaker. We found that real-time synthesis of vowels and consonants was possible with good intelligibility. In conclusion, these results open to future speech BCI applications using such articulatory-based speech synthesizer. PMID:27880768

  9. 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.

  10. 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.

  11. 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.

  12. Auditory evacuation beacons

    NARCIS (Netherlands)

    Wijngaarden, S.J. van; Bronkhorst, A.W.; Boer, L.C.

    2005-01-01

    Auditory evacuation beacons can be used to guide people to safe exits, even when vision is totally obscured by smoke. Conventional beacons make use of modulated noise signals. Controlled evacuation experiments show that such signals require explicit instructions and are often misunderstood. A new si

  13. Virtual Auditory Displays

    Science.gov (United States)

    2000-01-01

    timbre , intensity, distance, room modeling, radio communication Virtual Environments Handbook Chapter 4 Virtual Auditory Displays Russell D... musical note “A” as a pure sinusoid, there will be 440 condensations and rarefactions per second. The distance between two adjacent condensations or...and complexity are pitch, loudness, and timbre respectively. This distinction between physical and perceptual measures of sound properties is an

  14. 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.

  15. Efficient active feedback scheme of image multi-class classification%结合主动反馈的图像多分类框架

    Institute of Scientific and Technical Information of China (English)

    刘君; 王银辉; 李黎; 张宇

    2011-01-01

    为了解决图像语义分类中的训练数据不对称、小样本训练和噪声数据这3个难题,提出结合主动反馈的图像多分类框架.该框架将主动选择的策略应用到图像的多分类中,通过主动的选择出不确定的图片给用户手动标记,扩大训练图片集,提高分类的精度.为了验证该框架的有效性,提出一种有效的结合主动选择的图像多分类算法,即结合投票的DDAG SVM(decision directed acyclic graph support vector machine)算法.该算法提出了新的主动选择策略,即结合投票和旁移机制的主动选择策略.实验结果表明,该算法能有效应用到图像多分类中,比DDAGSVM和采用普通主动选择策略的DDAGSVM具有更高的分类的精度.%In order to solve three difficulties of image classification, including asymmetry of training data, small sample issue and noise sample problem, efficient active feedback scheme of image multi-class classification which introduce active selecting technique into image multi-class classification is proposed. By actively selecting doubtful images for users to label, more training samples can be gotten and more accurate classification can be done. In order to validate the scheme, an image multi-class classification algorithm combining with active selecting is fulfilled, which is named as DDAG SVM (decision directed acyclic graph support vector machine) with voting.Experiments show that the algorithm has more accuracy than DDAG SVM and DDAG SVM with normal selecting strategy, so it is efficient and the proposed scheme is also good for image multi-class classification.

  16. 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)

  17. 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.

  18. Animal models for auditory streaming.

    Science.gov (United States)

    Itatani, Naoya; Klump, Georg M

    2017-02-19

    Sounds in the natural environment need to be assigned to acoustic sources to evaluate complex auditory scenes. Separating sources will affect the analysis of auditory features of sounds. As the benefits of assigning sounds to specific sources accrue to all species communicating acoustically, the ability for auditory scene analysis is widespread among different animals. Animal studies allow for a deeper insight into the neuronal mechanisms underlying auditory scene analysis. Here, we will review the paradigms applied in the study of auditory scene analysis and streaming of sequential sounds in animal models. We will compare the psychophysical results from the animal studies to the evidence obtained in human psychophysics of auditory streaming, i.e. in a task commonly used for measuring the capability for auditory scene analysis. Furthermore, the neuronal correlates of auditory streaming will be reviewed in different animal models and the observations of the neurons' response measures will be related to perception. The across-species comparison will reveal whether similar demands in the analysis of acoustic scenes have resulted in similar perceptual and neuronal processing mechanisms in the wide range of species being capable of auditory scene analysis.This article is part of the themed issue 'Auditory and visual scene analysis'.

  19. Brain computation is organized via power-of-two-based permutation logic

    Directory of Open Access Journals (Sweden)

    Kun Xie

    2016-11-01

    computational algorithm is indeed organized by the power-of-two-based permutation logic. This simple mathematical logic can account for brain computation across the entire evolutionary spectrum, ranging from the simplest neural networks to the most complex.

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

    Science.gov (United States)

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

    2017-02-04

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

  1. 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

  2. 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

  3. 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

  4. 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.

  5. A co-adaptive brain-computer interface for end users with severe motor impairment.

    Directory of Open Access Journals (Sweden)

    Josef Faller

    Full Text Available Co-adaptive training paradigms for event-related desynchronization (ERD based brain-computer interfaces (BCI have proven effective for healthy users. As of yet, it is not clear whether co-adaptive training paradigms can also benefit users with severe motor impairment. The primary goal of our paper was to evaluate a novel cue-guided, co-adaptive BCI training paradigm with severely impaired volunteers. The co-adaptive BCI supports a non-control state, which is an important step toward intuitive, self-paced control. A secondary aim was to have the same participants operate a specifically designed self-paced BCI training paradigm based on the auto-calibrated classifier. The co-adaptive BCI analyzed the electroencephalogram from three bipolar derivations (C3, Cz, and C4 online, while the 22 end users alternately performed right hand movement imagery (MI, left hand MI and relax with eyes open (non-control state. After less than five minutes, the BCI auto-calibrated and proceeded to provide visual feedback for the MI task that could be classified better against the non-control state. The BCI continued to regularly recalibrate. In every calibration step, the system performed trial-based outlier rejection and trained a linear discriminant analysis classifier based on one auto-selected logarithmic band-power feature. In 24 minutes of training, the co-adaptive BCI worked significantly (p = 0.01 better than chance for 18 of 22 end users. The self-paced BCI training paradigm worked significantly (p = 0.01 better than chance in 11 of 20 end users. The presented co-adaptive BCI complements existing approaches in that it supports a non-control state, requires very little setup time, requires no BCI expert and works online based on only two electrodes. The preliminary results from the self-paced BCI paradigm compare favorably to previous studies and the collected data will allow to further improve self-paced BCI systems for disabled users.

  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. 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

  8. 支持向量机多分类问题研究%Research on the Multi-class Classification Problem of Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    肖晓; 张敏

    2014-01-01

    The support vector machine (SVM)is a typical two-class classification methods and how to extend it to multi-class classification has been increasingly a hotspot in the research of scholars.Experiments with the standard datasets were carried retrospectively on the existing methods of SVM multiclass classification,such as one against one method,one against rest meth-od and directed acyclic graph,showed their respective merits and defects.The results indicate that directed acyclic graph is naturally suitable for the multi-class classification of large-scale data with ideal training speed,which has a certain reference value.%支持向量机是典型的两类分类方法,如何将其推广到多分类问题是学者们正在研究的一个热点。对比分析几种常用的多类方法的优缺点,利用标准数据集对多类支持向量机的速度和精度两方面进行试验分析。研究表明,对于大规模的多类分类问题,有向无环图简单易行,具有理想的训练速度与精度,具有一定的参考价值。

  9. Single-laboratory validation study of rapid analysis method for multi-class veterinary drugs in milk, fish and shellfish by LC-MS/MS.

    Science.gov (United States)

    Nakajima, Takayuki; Nagano, Chieko; Kanda, Maki; Hayashi, Hiroshi; Hashimoto, Tsuneo; Kanai, Setsuko; Matsushima, Yoko; Tateishi, Yukinari; Sasamoto, Takeo; Takano, Ichiro

    2013-01-01

    A method of rapid analysis of multi-class residual veterinary drugs in milk, fish and shellfish was validated in accordance with Japanese guidelines for the validation of analytical methods for residual agricultural chemicals in food. Using LC-MS/MS, 43 multi-class veterinary drugs, including sulfonamides, quinolones, coccidiostats and antiparasites, could be analyzed in one injection. Analytes were extracted from samples with two kinds of solvent, acetonitrile containing 1 vol% formic acid and anhydrous acetonitrile, and salted out with 4.0 g of magnesium sulfate, 1.5 g of trisodium citrate and 2.0 g of sodium chloride. This method was assessed by performing recovery tests in retail milk and 4 kinds of fresh cultured fish and shellfish (salmon, tiger shrimp, red sea bream and bastard halibut) spiked with the 43 target analytes at the levels of 10 and 100 μg/kg. Using this method, 40 out of 43 drugs satisfied the guideline criteria in milk, 37 drugs in salmon, 42 drugs in tiger shrimp, 41 drugs in red sea bream and 39 drugs in bastard halibut.

  10. 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...... parameters highlighting harmonious and balanced qualities while criticizing the noisy and cacophonous qualities of modern urban settings. This paper present a reaffirmation of Schafer’s central methodological claim: that environments can be analyzed through their sound, but offers considerations on the role...... musicalized through electro acoustic equipment installed in shops, shopping streets, transit areas etc. Urban noise no longer acts only as disturbance, but also structure and shape the places and spaces in which urban life enfold. Based on research done in Japanese shopping streets and in Copenhagen the paper...

  11. Behind the Scenes of Auditory Perception

    OpenAIRE

    Shamma, Shihab A.; Micheyl, Christophe

    2010-01-01

    Auditory scenes” often contain contributions from multiple acoustic sources. These are usually heard as separate auditory “streams”, which can be selectively followed over time. How and where these auditory streams are formed in the auditory system is one of the most fascinating questions facing auditory scientists today. Findings published within the last two years indicate that both cortical and sub-cortical processes contribute to the formation of auditory streams, and they raise importan...

  12. 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.

  13. 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.

  14. 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.

  15. Modulation of the inter-hemispheric asymmetry of motor-related brain activity using brain-computer interfaces.

    Science.gov (United States)

    Pereira, Michael; Sobolewski, Aleksander; Millan, Jose Del R

    2015-01-01

    Non-invasive brain stimulation has shown promising results in neurorehabilitation for motor-impaired stroke patients, by rebalancing the relative involvement of each hemisphere in movement generation. Similarly, brain-computer interfaces have been used to successfully facilitate movement-related brain activity spared by the infarct. We propose to merge both approaches by using BCI to train stroke patients to rebalance their motor-related brain activity during motor tasks, through the use of online feedback. In this pilot study, we report results showing that some healthy subjects were able to learn to spontaneously up- and/or down-regulate their ipsilateral brain activity during a single session.

  16. 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

    on a single feature. However, the duration of the window length was not statistically significant (p = 0.5). CONCLUSION: Our results showed the feasibility of detecting a motor task in real time in a less restricted environment compared to environments commonly applied within invasive BCI research......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...

  17. IpsiHand Bravo: an improved EEG-based brain-computer interface for hand motor control rehabilitation.

    Science.gov (United States)

    Holmes, Charles Damian; Wronkiewicz, Mark; Somers, Thane; Liu, Jenny; Russell, Elizabeth; Kim, DoHyun; Rhoades, Colleen; Dunkley, Jason; Bundy, David; Galboa, Elad; Leuthardt, Eric

    2012-01-01

    Stroke and other nervous system injuries can damage or destroy hand motor control and greatly upset daily activities. Brain computer interfaces (BCIs) represent an emerging technology that can bypass damaged nerves to restore basic motor function and provide more effective rehabilitation. A wireless BCI system was implemented to realize these goals using electroencephalographic brain signals, machine learning techniques, and a custom designed orthosis. The IpsiHand Bravo BCI system is designed to reach a large demographic by using non-traditional brain signals and improving on past BCI system pitfalls.

  18. How many people are able to control a P300-based brain-computer interface (BCI)?

    Science.gov (United States)

    Guger, Christoph; Daban, Shahab; Sellers, Eric; Holzner, Clemens; Krausz, Gunther; Carabalona, Roberta; Gramatica, Furio; Edlinger, Guenter

    2009-10-02

    An EEG-based brain-computer system can be used to control external devices such as computers, wheelchairs or Virtual Environments. One of the most important applications is a spelling device to aid severely disabled individuals with communication, for example people disabled by amyotrophic lateral sclerosis (ALS). P300-based BCI systems are optimal for spelling characters with high speed and accuracy, as compared to other BCI paradigms such as motor imagery. In this study, 100 subjects tested a P300-based BCI system to spell a 5-character word with only 5 min of training. EEG data were acquired while the subject looked at a 36-character matrix to spell the word WATER. Two different versions of the P300 speller were used: (i) the row/column speller (RC) that flashes an entire column or row of characters and (ii) a single character speller (SC) that flashes each character individually. The subjects were free to decide which version to test. Nineteen subjects opted to test both versions. The BCI system classifier was trained on the data collected for the word WATER. During the real-time phase of the experiment, the subject spelled the word LUCAS, and was provided with the classifier selection accuracy after each of the five letters. Additionally, subjects filled out a questionnaire about age, sex, education, sleep duration, working duration, cigarette consumption, coffee consumption, and level of disturbance that the flashing characters produced. 72.8% (N=81) of the subjects were able to spell with 100% accuracy in the RC paradigm and 55.3% (N=38) of the subjects spelled with 100% accuracy in the SC paradigm. Less than 3% of the subjects did not spell any character correctly. People who slept less than 8h performed significantly better than other subjects. Sex, education, working duration, and cigarette and coffee consumption were not statistically related to differences in accuracy. The disturbance of the flashing characters was rated with a median score of 1 on a

  19. High-resolution EEG techniques for brain-computer interface applications.

    Science.gov (United States)

    Cincotti, Febo; Mattia, Donatella; Aloise, Fabio; Bufalari, Simona; Astolfi, Laura; De Vico Fallani, Fabrizio; Tocci, Andrea; Bianchi, Luigi; Marciani, Maria Grazia; Gao, Shangkai; Millan, Jose; Babiloni, Fabio

    2008-01-15

    High-resolution electroencephalographic (HREEG) techniques allow estimation of cortical activity based on non-invasive scalp potential measurements, using appropriate models of volume conduction and of neuroelectrical sources. In this study we propose an application of this body of technologies, originally developed to obtain functional images of the brain's electrical activity, in the context of brain-computer interfaces (BCI). Our working hypothesis predicted that, since HREEG pre-processing removes spatial correlation introduced by current conduction in the head structures, by providing the BCI with waveforms that are mostly due to the unmixed activity of a small cortical region, a more reliable classification would be obtained, at least when the activity to detect has a limited generator, which is the case in motor related tasks. HREEG techniques employed in this study rely on (i) individual head models derived from anatomical magnetic resonance images, (ii) distributed source model, composed of a layer of current dipoles, geometrically constrained to the cortical mantle, (iii) depth-weighted minimum L(2)-norm constraint and Tikhonov regularization for linear inverse problem solution and (iv) estimation of electrical activity in cortical regions of interest corresponding to relevant Brodmann areas. Six subjects were trained to learn self modulation of sensorimotor EEG rhythms, related to the imagination of limb movements. Off-line EEG data was used to estimate waveforms of cortical activity (cortical current density, CCD) on selected regions of interest. CCD waveforms were fed into the BCI computational pipeline as an alternative to raw EEG signals; spectral features are evaluated through statistical tests (r(2) analysis), to quantify their reliability for BCI control. These results are compared, within subjects, to analogous results obtained without HREEG techniques. The processing procedure was designed in such a way that computations could be split into a

  20. 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

  1. 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.

  2. Word Recognition in Auditory Cortex

    Science.gov (United States)

    DeWitt, Iain D. J.

    2013-01-01

    Although spoken word recognition is more fundamental to human communication than text recognition, knowledge of word-processing in auditory cortex is comparatively impoverished. This dissertation synthesizes current models of auditory cortex, models of cortical pattern recognition, models of single-word reading, results in phonetics and results in…

  3. 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 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.

  4. 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.

  5. A brain-computer interface based on self-regulation of gamma-oscillations in the superior parietal cortex

    Science.gov (United States)

    Grosse-Wentrup, Moritz; Schölkopf, Bernhard

    2014-10-01

    Objective. Brain-computer interface (BCI) systems are often based on motor- and/or sensory processes that are known to be impaired in late stages of amyotrophic lateral sclerosis (ALS). We propose a novel BCI designed for patients in late stages of ALS that only requires high-level cognitive processes to transmit information from the user to the BCI. Approach. We trained subjects via EEG-based neurofeedback to self-regulate the amplitude of gamma-oscillations in the superior parietal cortex (SPC). We argue that parietal gamma-oscillations are likely to be associated with high-level attentional processes, thereby providing a communication channel that does not rely on the integrity of sensory- and/or motor-pathways impaired in late stages of ALS. Main results. Healthy subjects quickly learned to self-regulate gamma-power in the SPC by alternating between states of focused attention and relaxed wakefulness, resulting in an average decoding accuracy of 70.2%. One locked-in ALS patient (ALS-FRS-R score of zero) achieved an average decoding accuracy significantly above chance-level though insufficient for communication (55.8%). Significance. Self-regulation of gamma-power in the SPC is a feasible paradigm for brain-computer interfacing and may be preserved in late stages of ALS. This provides a novel approach to testing whether completely locked-in ALS patients retain the capacity for goal-directed thinking.

  6. Multi-class Classification Algorithm Based on Local Optimization%基于局部优化的多类分类算法

    Institute of Scientific and Technical Information of China (English)

    单瑾; 刘明纲; 罗侃

    2016-01-01

    In order to solve the blindness and imbalance that appeares commonly in the traditional multi-class classification, this paper designs an improved and localized multi-class classification algorithm based on mutual communication entropy and support vector data description (SVDD), which is known as EL-SVDD algorithm. Firstly, this algorithm calculates the mutual communication entropy with many local classes of samples. Secondly, one class is placed inside the ball based on the mutual communication entropy. Finally, according to the samples and mutual communication entropy, it reinterprets theCvalues of SVDD algorithm. Experiment results show that EL-SVDD algorithm not only has the feasibility, but also can effectively and stably improve the accuracy of many types of analysis.%为了解决传统多类分类问题中普遍出现的偏离性与不平衡性,依据互通信熵理论与支持向量数据描述(SVDD)分类原理,设计出一种改进的局部性SVDD多类分类算法,即EL-SVDD算法。此算法首先以局部样本信息为载体,计算出互通信熵参数值;其次在多维度空间球体中以互通信熵参数值分类放置测试样本数据信息;最后综合分析测试样本大小与互通信熵参数值,重新诠释了SVDD算法中的C值。实验表明,EL-SVDD算法不仅具有可行性,而且能够有效和稳定地提高多类分析精度。

  7. 局部学习半监督多类分类机%Local learning semi-supervised multi-class classifier

    Institute of Scientific and Technical Information of China (English)

    吕佳; 邓乃扬; 田英杰; 邵元海; 杨新民

    2013-01-01

    半监督多类分类问题是机器学习和模式识别领域中的一个研究热点,目前大多数多类分类算法是将问题分解成若干个二类分类问题来求解.提出两种类标号表示方法来避免多个二类分类问题的求解,一种是单位圆类标号表示方法,一种是二进制序列类标号表示方法,并利用局部学习在二类分类问题中的良好学习特性,提出基于局部学习的半监督多类分类机.实验结果证明采用了基于局部学习的半监督多类分类机错分率更小,稳定性更高.%Semi-supervised multi-class classification problem opens research focuses in machine learning and pattern recognition, currently it is decomposed into a set of binary classification problems. Two kinds of class label presentation methods that one was class label presentation method of unit disc and the other was that of binary string were proposed for fear that multiple binary classification problems were solved. Besides, local learning has the good feature in semi-supervised binary classification problem. On the basis of it, local learning semi-supervised multi-class classifier was presented in this paper. The effectiveness of the algorithms was confirmed with experiments on benchmark datasets compared to other related algorithms.

  8. 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...

  9. 基于体感电刺激的脑-机接口实验范式初探%A Preliminary Study of Brain-Computer Interface Paradigm Based on Electrical Somatosensory Modality

    Institute of Scientific and Technical Information of China (English)

    蒲江波; 安兴伟; 李杰威; 崔红岩; 明东; 胡勇

    2015-01-01

    Brain-computer interfaces ( BCIs) usually apply the brain signals in responding to various sen-sory inputs in visual or auditory modalities to provide direct communication pathways to external devices. No early study shows the possibility of using electrical stimuli as BCI input.In this study, we adopted the electrical somatosensory stimuli as the BCI input to elicit event-related potential ( ERP) .Three-condition experiment was conducted using visual, auditory and electrical stimuli individually in each condition.We compared the ERP components of each condition as well as the classification accuracy of these three con-ditions.Results show that electrical stimuli could provide relatively high amplitude and stable latencies of ERP components.It also enjoys higher class-discriminative information and classification accuracy than auditory paradigms.Thus, electrical paradigms with different stimuli intensities could be a good choice for BCI applications, which will enlarge the options for BCI purpose.%大脑对各类感觉输入(视、听模态)会产生不同的响应信号,脑-机接口正是利用这一响应信号实现大脑与外部设备间直接的通讯。然而以电刺激作为脑-机接口的输入模态还未有报道。本研究尝试通过使用体感电刺激作为脑-机接口的输入,从而诱发事件相关电位( ERP)。在整个实验中,分别使用视觉、听觉以及电刺激作为诱发因素,针对每种条件下的事件相关电位及其分类准确率开展对比分析。结果显示电刺激所诱发的事件相关电位幅值较高且具有相对稳定的潜伏期,其分类准确率高于听觉刺激范式。也就表明了以不同刺激强度作为参数的电刺激范式作为脑-机接口应用的可行性,这将进一步拓展脑-机接口的应用领域。

  10. 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.

  11. 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.

  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. 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.

  14. 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.

  15. 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.

  16. 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

  17. LETTERS AND COMMENTS: Self-initiation of EEG-based brain computer communication using the heart rate response

    Science.gov (United States)

    Scherer, R.; Müller-Putz, G. R.; Pfurtscheller, G.

    2007-12-01

    Self-initiation, that is the ability of a brain-computer interface (BCI) user to autonomously switch on and off the system, is a very important issue. In this work we analyze whether the respiratory heart rate response, induced by brisk inspiration, can be used as an additional communication channel. After only 20 min of feedback training, ten healthy subjects were able to self-initiate and operate a 4-class steady-state visual evoked potential-based (SSVEP) BCI by using one bipolar ECG and one bipolar EEG channel only. Threshold detection was used to measure a beat-to-beat heart rate increase. Despite this simple method, during a 30 min evaluation period on average only 2.9 non-intentional switches (heart rate changes) were detected.

  18. A comparison of regression techniques for a two-dimensional sensorimotor rhythm-based brain-computer interface

    Science.gov (United States)

    Fruitet, Joan; McFarland, Dennis J.; Wolpaw, Jonathan R.

    2010-02-01

    People can learn to control electroencephalogram (EEG) features consisting of sensorimotor-rhythm amplitudes and use this control to move a cursor in one, two or three dimensions to a target on a video screen. This study evaluated several possible alternative models for translating these EEG features into two-dimensional cursor movement by building an offline simulation using data collected during online performance. In offline comparisons, support-vector regression (SVM) with a radial basis kernel produced somewhat better performance than simple multiple regression, the LASSO or a linear SVM. These results indicate that proper choice of a translation algorithm is an important factor in optimizing brain-computer interface (BCI) performance, and provide new insight into algorithm choice for multidimensional movement control.

  19. 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.

  20. Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment

    Science.gov (United States)

    Lalor, E. C.; Kelly, S. P.; Finucane, C.; Burke, R.; Smith, R.; Reilly, R. B.; McDarby, G.

    2005-12-01

    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.

  1. A review of past and future near-infrared spectroscopy brain computer interface research at the PRISM lab.

    Science.gov (United States)

    Schudlo, Larissa C; Weyand, Sabine; Chau, Tom

    2014-01-01

    Single-trial classification of near-infrared spectroscopy (NIRS) signals for brain-computer interface (BCI) applications has recently gained much attention. This paper reviews research in this area conducted at the PRISM lab (University of Toronto) to date, as well as directions for future work. Thus far, research has included classification of hemodynamic changes induced by the performance of various mental tasks in both offline and online settings, as well as offline classification of cortical changes evoked by different affective states. The majority of NIRS-BCI work has only involved able-bodied individuals. However, preliminary work involving individuals from target BCI-user populations is also underway. In addition to further testing with users with severe disabilities, ongoing and future research will focus on enhancing classification accuracies, communication speed and user experience.

  2. 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.

  3. Improving brain computer interface research through user involvement - The transformative potential of integrating civil society organisations in research projects

    Science.gov (United States)

    Wakunuma, Kutoma; Rainey, Stephen; Hansen, Christian

    2017-01-01

    Research on Brain Computer Interfaces (BCI) often aims to provide solutions for vulnerable populations, such as individuals with diseases, conditions or disabilities that keep them from using traditional interfaces. Such research thereby contributes to the public good. This contribution to the public good corresponds to a broader drive of research and funding policy that focuses on promoting beneficial societal impact. One way of achieving this is to engage with the public. In practical terms this can be done by integrating civil society organisations (CSOs) in research. The open question at the heart of this paper is whether and how such CSO integration can transform the research and contribute to the public good. To answer this question the paper describes five detailed qualitative case studies of research projects including CSOs. The paper finds that transformative impact of CSO integration is possible but by no means assured. It provides recommendations on how transformative impact can be promoted. PMID:28207882

  4. 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.

  5. Design and implementation of a P300-based brain-computer interface for controlling an internet browser.

    Science.gov (United States)

    Mugler, Emily M; Ruf, Carolin A; Halder, Sebastian; Bensch, Michael; Kubler, Andrea

    2010-12-01

    An electroencephalographic (EEG) brain-computer interface (BCI) internet browser was designed and evaluated with 10 healthy volunteers and three individuals with advanced amyotrophic lateral sclerosis (ALS), all of whom were given tasks to execute on the internet using the browser. Participants with ALS achieved an average accuracy of 73% and a subsequent information transfer rate (ITR) of 8.6 bits/min and healthy participants with no prior BCI experience over 90% accuracy and an ITR of 14.4 bits/min. We define additional criteria for unrestricted internet access for evaluation of the presented and future internet browsers, and we provide a review of the existing browsers in the literature. The P300-based browser provides unrestricted access and enables free web surfing for individuals with paralysis.

  6. EEG-Based Classification of Motor Imagery Tasks Using Fractal Dimension and Neural Network for Brain-Computer Interface

    Science.gov (United States)

    Phothisonothai, Montri; Nakagawa, Masahiro

    In this study, we propose a method of classifying a spontaneous electroencephalogram (EEG) approach to a brain-computer interface. Ten subjects, aged 21-32 years, volunteered to imagine left-and right- hand movements. An independent component analysis based on a fixed-point algorithm is used to eliminate the activities found in the EEG signals. We use a fractal dimension value to reveal the embedded potential responses in the human brain. The different fractal dimension values between the relaxing and imaging periods are computed. Featured data is classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Two conventional methods, namely, the use of the autoregressive (AR) model and the band power estimation (BPE) as features, and the linear discriminant analysis (LDA) as a classifier, are selected for comparison in this study. Experimental results show that the proposed method is more effective than the conventional methods.

  7. 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.

  8. TOPICAL REVIEW: A survey of signal processing algorithms in brain computer interfaces based on electrical brain signals

    Science.gov (United States)

    Bashashati, Ali; Fatourechi, Mehrdad; Ward, Rabab K.; Birch, Gary E.

    2007-06-01

    Brain computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention?

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

    Science.gov (United States)

    Wang, Jue; Yu, Tianyou

    2017-01-01

    The hybrid brain computer interface (BCI) based on motor imagery (MI) and P300 has been a preferred strategy aiming to improve the detection performance through combining the features of each. However, current methods used for combining these two modalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to optimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a dual spectral norm regularizer is applied to the combined features. Under this framework, the hybrid features of MI and P300 can be learned, selected, and combined together directly. Experimental results on the data set of hybrid BCI based on MI and P300 are provided to illustrate competitive performance of the proposed method against other conventional methods. This provides an evidence that the method used here contributes to the discrimination performance of the brain state in hybrid BCI. PMID:28316617

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

    Directory of Open Access Journals (Sweden)

    Jinyi Long

    2017-01-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

  12. 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.

  13. Control of a brain-computer interface using stereotactic depth electrodes in and adjacent to the hippocampus

    Science.gov (United States)

    Krusienski, D. J.; Shih, J. J.

    2011-04-01

    A brain-computer interface (BCI) is a device that enables severely disabled people to communicate and interact with their environments using their brain waves. Most research investigating BCI in humans has used scalp-recorded electroencephalography or intracranial electrocorticography. The use of brain signals obtained directly from stereotactic depth electrodes to control a BCI has not previously been explored. In this study, event-related potentials (ERPs) recorded from bilateral stereotactic depth electrodes implanted in and adjacent to the hippocampus were used to control a P300 Speller paradigm. The ERPs were preprocessed and used to train a linear classifier to subsequently predict the intended target letters. The classifier was able to predict the intended target character at or near 100% accuracy using fewer than 15 stimulation sequences in the two subjects tested. Our results demonstrate that ERPs from hippocampal and hippocampal adjacent depth electrodes can be used to reliably control the P300 Speller BCI paradigm.

  14. 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.

  15. 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

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

    Science.gov (United States)

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

    2013-08-01

    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.

  17. Auditory Processing Disorder (For Parents)

    Science.gov (United States)

    ... CAPD often have trouble maintaining attention, although health, motivation, and attitude also can play a role. Auditory ... programs. Several computer-assisted programs are geared toward children with APD. They mainly help the brain do ...

  18. Evaluation of Diagnostic Tests Using Information Theory for Multi-Class Diagnostic Problems and its Application for the Detection of Occlusal Caries Lesions

    Science.gov (United States)

    Arslan, Umut; Karaağaoğlu, Ergun; Özkan, Gökhan; Kanlı, Aydan

    2014-01-01

    Background: Several methods are available to evaluate the performance of the tests when the purpose of the diagnostic test is to discriminate between two possible disease states. However multi-class diagnostic problems frequently appear in many areas of medical science. Hence, there is a need for methods which will enable us to characterize the accuracy of diagnostic tests when there are more than two possible disease states. Aims: To show that two information theory measures, information content (IC) and proportional reduction in diagnostic uncertainty (PRDU), can be used for the evaluation of the performance of diagnostic tests for multi-class diagnostic problems that may appear in different areas of medical science. Study Design: Diagnostic accuracy study. Methods: Sixty freshly extracted permanent human molar and pre-molar teeth suspected to have occlusal caries lesions were selected for the study and were assessed by two experienced examiners. Each examiner performed two evaluations. Histological examination was used as the gold standard. The scores of the histological examination were defined as sound (n=11), enamel caries (n=22) and dentin caries (n=27). Diagnostic performance of i) visual inspection, ii) radiography, iii) laser fluorescence (LF) and iv) micro-computed tomography (M-CT) caries detection methods was evaluated by calculating IC and PRDU. Results: Micro-computed tomography examination was the best method among the diagnostic techniques for the diagnosis of occlusal caries in terms of both IC and PRDU. M-CT examination supplied the maximum diagnostic information about the diagnosis of occlusal caries in the first (IC: 1.056; p<0.05), (PRDU: 70.5%) and second evaluation (IC: 1.105; p<0.05), (PRDU: 73.8%) for the first examiner. M-CT examination was the best method among the diagnostic techniques for the second examiner in both the first (IC:1.105; p<0.05), (PRDU:73.8%) and second evaluation (IC:1.061; p<0.05), (PRDU:70.8%). IC and PRDU were

  19. 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.

  20. Unsupervised Multi-class Co-segmentation via Joint-Cut over L1-Manifold Hyper-Graph of Discriminative Image Regions.

    Science.gov (United States)

    Ma, Jizhou; Li, Shuai; Qin, Hong; Hao, Aimin

    2016-11-22

    This paper systematically advocates a robust and efficient unsupervised multi-class co-segmentation approach by leveraging underlying subspace manifold propagation to exploit the cross-image coherency. It can combat certain image cosegmentation difficulties due to viewpoint change, partial occlusion, complex background, transient illumination, and cluttering texture patterns. Our key idea is to construct a powerful hyper-graph joint-cut framework, which incorporates mid-level image regions based intra-image feature representation and L1- manifold graph based inter-image coherency exploration. For local image region generation, we propose a bi-harmonic distance distribution difference metric to govern the super-pixel clustering in a bottom-up way. It not only affords drastic data reduction but also gives rise to discriminative and structure-meaningful feature representation. As for the inter-image coherency, we leverage multi-type features involved L1-graph to detect the underlying local manifold from cross-image regions. As a result, the implicit supervising information could be encoded into the unsupervised hyper-graph joint-cut framework. We conduct extensive experiments and make comprehensive evaluations with other stateof- the-art methods over various benchmarks, including iCoseg, MSRC, and Oxford flower. All the results demonstrate the superiorities of our method in terms of accuracy, robustness, efficiency, and versatility.

  1. Analytical method for fast screening and confirmation of multi-class veterinary drug residues in fish and shrimp by LC-MS/MS.

    Science.gov (United States)

    Kim, Junghyun; Suh, Joon Hyuk; Cho, Hyun-Deok; Kang, Wonjae; Choi, Yong Seok; Han, Sang Beom

    2016-01-01

    A multi-class, multi-residue analytical method based on LC-MS/MS detection was developed for the screening and confirmation of 28 veterinary drug and metabolite residues in flatfish, shrimp and eel. The chosen veterinary drugs are prohibited or unauthorised compounds in Korea, which were categorised into various chemical classes including nitroimidazoles, benzimidazoles, sulfones, quinolones, macrolides, phenothiazines, pyrethroids and others. To achieve fast and simultaneous extraction of various analytes, a simple and generic liquid extraction procedure using EDTA-ammonium acetate buffer and acetonitrile, without further clean-up steps, was applied to sample preparation. The final extracts were analysed by ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS). The method was validated for each compound in each matrix at three different concentrations (5, 10 and 20 ng g(-1)) in accordance with Codex guidelines (CAC/GL 71-2009). For most compounds, the recoveries were in the range of 60-110%, and precision, expressed as the relative standard deviation (RSD), was in the range of 5-15%. The detection capabilities (CCβs) were below or equal to 5 ng g(-1), which indicates that the developed method is sufficient to detect illegal fishery products containing the target compounds above the residue limit (10 ng g(-1)) of the new regulatory system (Positive List System - PLS).

  2. 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

  3. Conditional associative learning examined in a paralyzed patient with amyotrophic lateral sclerosis using brain-computer interface technology

    Science.gov (United States)

    Iversen, IH; Ghanayim, N; Kübler, A; Neumann, N; Birbaumer, N; Kaiser, J

    2008-01-01

    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 cognitive abilities of

  4. Auditory Hallucinations in Acute Stroke

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    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.

  5. 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.

  6. 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

  7. 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.

  8. 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

  9. 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.

  10. 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.

  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

    2015-09-30

    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.

  12. 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.

  13. A review of brain-computer interface games and an opinion survey from researchers, developers and users.

    Science.gov (United States)

    Ahn, Minkyu; Lee, Mijin; Choi, Jinyoung; Jun, Sung Chan

    2014-08-11

    In recent years, research on Brain-Computer Interface (BCI) technology for healthy users has attracted considerable interest, and BCI games are especially popular. This study reviews the current status of, and describes future directions, in the field of BCI games. To this end, we conducted a literature search and found that BCI control paradigms using electroencephalographic signals (motor imagery, P300, steady state visual evoked potential and passive approach reading mental state) have been the primary focus of research. We also conducted a survey of nearly three hundred participants that included researchers, game developers and users around the world. From this survey, we found that all three groups (researchers, developers and users) agreed on the significant influence and applicability of BCI and BCI games, and they all selected prostheses, rehabilitation and games as the most promising BCI applications. User and developer groups tended to give low priority to passive BCI and the whole head sensor array. Developers gave higher priorities to "the easiness of playing" and the "development platform" as important elements for BCI games and the market. Based on our assessment, we discuss the critical point at which BCI games will be able to progress from their current stage to widespread marketing to consumers. In conclusion, we propose three critical elements important for expansion of the BCI game market: standards, gameplay and appropriate integration.

  14. 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 pinternal binary intention has the potential to be used for implementing more intuitive and user-friendly communication systems for patients with motor disabilities.

  15. The utility metric: a novel method to assess the overall performance of discrete brain-computer interfaces.

    Science.gov (United States)

    Dal Seno, Bernardo; Matteucci, Matteo; Mainardi, Luca T

    2010-02-01

    A relevant issue in a brain-computer interface (BCI) is the capability to efficiently convert user intentions into correct actions, and how to properly measure this efficiency. Usually, the evaluation of a BCI system is approached through the quantification of the classifier performance, which is often measured by means of the information transfer rate (ITR). A shortcoming of this approach is that the control interface design is neglected, and hence a poor description of the overall performance is obtained for real systems. To overcome this limitation, we propose a novel metric based on the computation of BCI Utility. The new metric can accurately predict the overall performance of a BCI system, as it takes into account both the classifier and the control interface characteristics. It is therefore suitable for design purposes, where we have to select the best options among different components and different parameters setup. In the paper, we compute Utility in two scenarios, a P300 speller and a P300 speller with an error correction system (ECS), for different values of accuracy of the classifier and recall of the ECS. Monte Carlo simulations confirm that Utility predicts the performance of a BCI better than ITR.

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

    Science.gov (United States)

    Lajoie, Guillaume; Kalaska, John F.; Fairhall, Adrienne L.; Fetz, Eberhard E.

    2017-01-01

    Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP) rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity. PMID:28151957

  17. Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier.

    Science.gov (United States)

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

    2016-02-01

    There is general agreement in the brain-computer interface (BCI) community that although non-linear classifiers can provide better results in some cases, linear classifiers are preferable. Particularly, as non-linear classifiers often involve a number of parameters that must be carefully chosen. However, new non-linear classifiers were developed over the last decade. One of them is the random forest (RF) classifier. Although popular in other fields of science, RFs are not common in BCI research. In this work, we address three open questions regarding RFs in sensorimotor rhythm (SMR) BCIs: parametrization, online applicability, and performance compared to regularized linear discriminant analysis (LDA). We found that the performance of RF is constant over a large range of parameter values. We demonstrate - for the first time - that RFs are applicable online in SMR-BCIs. Further, we show in an offline BCI simulation that RFs statistically significantly outperform regularized LDA by about 3%. These results confirm that RFs are practical and convenient non-linear classifiers for SMR-BCIs. Taking into account further properties of RFs, such as independence from feature distributions, maximum margin behavior, multiclass and advanced data mining capabilities, we argue that RFs should be taken into consideration for future BCIs.

  18. [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.

  19. Design and simulation of virtual telephone keypad control based on brain computer interface (BCI with very high transfer rates

    Directory of Open Access Journals (Sweden)

    Rehab B. Ashari

    2011-03-01

    Full Text Available Brain Computer Interface (BCI is a communication and control mechanism, which does not rely on any kind of muscular response to send a message to the external world. This technique is used to help the paralyzed people with spinal cord injury to have the ability to communicate with the external world. In this paper we emphasize to increase the BCI System bit rate for controlling a virtual telephone keypad. To achieve the proposed algorithm, a simulated virtual telephone keypad based on Steady State Visual Evoked Potential (SSVEP BCI system is developed. Dynamic programming technique with specifically modified Longest Common Subsequence (LCS algorithm is used. By comparing the paralyzed user selection with the recent, and then the rest, of the stored records in the file of the telephone, the user can save the rest of his choices for controlling the keypad and thence improving the overall performance of the BCI system. This axiomatic approach, which is used in searching the web pages for increasing the performance of the searching, is urgent to be used for the paralyzed people rather than the normal user.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. Progress and prospects in neurorehabilitation: clinical applications of stem cells and brain-computer interface for spinal cord lesions.

    Science.gov (United States)

    Gongora, Mariana; Peressutti, Caroline; Machado, Sergio; Teixeira, Silmar; Velasques, Bruna; Ribeiro, Pedro

    2013-04-01

    Spinal cord injury (SCI) is a disease that affects millions of people worldwide, causing a temporary or permanent impairment of neuromotor functions. Mostly associated to traumatic lesions, but also to other forms of disease, the appropriate treatment is still unsure. In this review, several ongoing studies are presented that aim to provide methods of prevention that ensure quality of life, and rehabilitation trends to patients who suffer from this injury. Stem cell research, highlighted in this review, seeks to reduce damage caused to the tissue, as also provide spinal cord regeneration through the application of several types of stem cells. On the other hand, research using brain-computer interface (BCI) technology proposes the development of interfaces based on the interaction of neural networks with artificial tools to restore motor control and full mobility of the injured area. PubMed, MEDLINE and SciELO data basis analyses were performed to identify studies published from 2000 to date, which describe the link between SCI with stem cells and BCI technology.

  11. 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.

  12. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans

    Science.gov (United States)

    Wolpaw, Jonathan R.; McFarland, Dennis J.

    2004-12-01

    Brain-computer interfaces (BCIs) can provide communication and control to people who are totally paralyzed. BCIs can use noninvasive or invasive methods for recording the brain signals that convey the user's commands. Whereas noninvasive BCIs are already in use for simple applications, it has been widely assumed that only invasive BCIs, which use electrodes implanted in the brain, can provide multidimensional movement control of a robotic arm or a neuroprosthesis. We now show that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys. In movement time, precision, and accuracy, the results are comparable to those with invasive BCIs. The adaptive algorithm used in this noninvasive BCI identifies and focuses on the electroencephalographic features that the person is best able to control and encourages further improvement in that control. The results suggest that people with severe motor disabilities could use brain signals to operate a robotic arm or a neuroprosthesis without needing to have electrodes implanted in their brains. brain-machine interface | electroencephalography

  13. A Brain-Computer Interface (BCI) system to use arbitrary Windows applications by directly controlling mouse and keyboard.

    Science.gov (United States)

    Spuler, Martin

    2015-08-01

    A Brain-Computer Interface (BCI) allows to control a computer by brain activity only, without the need for muscle control. In this paper, we present an EEG-based BCI system based on code-modulated visual evoked potentials (c-VEPs) that enables the user to work with arbitrary Windows applications. Other BCI systems, like the P300 speller or BCI-based browsers, allow control of one dedicated application designed for use with a BCI. In contrast, the system presented in this paper does not consist of one dedicated application, but enables the user to control mouse cursor and keyboard input on the level of the operating system, thereby making it possible to use arbitrary applications. As the c-VEP BCI method was shown to enable very fast communication speeds (writing more than 20 error-free characters per minute), the presented system is the next step in replacing the traditional mouse and keyboard and enabling complete brain-based control of a computer.

  14. 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.

  15. Rehabilitation of hand in subacute tetraplegic patients based on brain computer interface and functional electrical stimulation: a randomised pilot study

    Science.gov (United States)

    Osuagwu, Bethel C. A.; Wallace, Leslie; Fraser, Mathew; Vuckovic, Aleksandra

    2016-12-01

    Objective. To compare neurological and functional outcomes between two groups of hospitalised patients with subacute tetraplegia. Approach. Seven patients received 20 sessions of brain computer interface (BCI) controlled functional electrical stimulation (FES) while five patients received the same number of sessions of passive FES for both hands. The neurological assessment measures were event related desynchronization (ERD) during movement attempt, Somatosensory evoked potential (SSEP) of the ulnar and median nerve; assessment of hand function involved the range of motion (ROM) of wrist and manual muscle test. Main results. Patients in both groups initially had intense ERD during movement attempt that was not restricted to the sensory-motor cortex. Following the treatment, ERD cortical activity restored towards the activity in able-bodied people in BCI-FES group only, remaining wide-spread in FES group. Likewise, SSEP returned in 3 patients in BCI-FES group, having no changes in FES group. The ROM of the wrist improved in both groups. Muscle strength significantly improved for both hands in BCI-FES group. For FES group, a significant improvement was noticed for right hand flexor muscles only. Significance. Combined BCI-FES therapy results in better neurological recovery and better improvement of muscle strength than FES alone. For spinal cord injured patients, BCI-FES should be considered as a therapeutic tool rather than solely a long-term assistive device for the restoration of a lost function.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-computer interface

    Directory of Open Access Journals (Sweden)

    Brittany Mei Young

    2014-07-01

    Full Text Available This study aims to examine the changes in task-related brain activity induced by rehabilitative therapy using brain-computer interface (BCI technologies and whether these changes are relevant to functional gains achieved through the use of these therapies. Stroke patients with persistent upper-extremity motor deficits received interventional rehabilitation therapy using a closed-loop neurofeedback BCI device (n=8 or no therapy (n=6. Behavioral assessments using the Stroke Impact Scale, the Action Research Arm Test, and the Nine-Hole Peg Test as well as task-based fMRI scans were conducted before, during, after, and one month after therapy administration or at analogous intervals in the absence of therapy. Laterality Index (LI during finger tapping of each hand were calculated for each time point and assessed for correlation with behavioral outcomes. Brain activity during finger tapping of each hand shifted over the course of BCI therapy but not in the absence of therapy to greater involvement of the non-lesioned hemisphere (and lesser involvement of the stroke-lesioned hemisphere as measured by LI. Moreover, changes from baseline LI values during finger tapping of the impaired hand were correlated with gains in both objective and subjective behavioral measures. These findings suggest that the administration of interventional BCI therapy can induce differential changes in brain activity patterns between the lesioned and nonlesioned hemisphere and that these brain changes are associated with changes in specific motor functions.

  1. 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.

  2. EEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis

    Science.gov (United States)

    Mak, Joseph N.; McFarland, Dennis J.; Vaughan, Theresa M.; McCane, Lynn M.; Tsui, Phillippa Z.; Zeitlin, Debra J.; Sellers, Eric W.; Wolpaw, Jonathan R.

    2012-04-01

    The purpose of this study was to identify electroencephalography (EEG) features that correlate with P300-based brain-computer interface (P300 BCI) performance in people with amyotrophic lateral sclerosis (ALS). Twenty people with ALS used a P300 BCI spelling application in copy-spelling mode. Three types of EEG features were found to be good predictors of P300 BCI performance: (1) the root-mean-square amplitude and (2) the negative peak amplitude of the event-related potential to target stimuli (target ERP) at Fz, Cz, P3, Pz, and P4; and (3) EEG theta frequency (4.5-8 Hz) power at Fz, Cz, P3, Pz, P4, PO7, PO8 and Oz. A statistical prediction model that used a subset of these features accounted for >60% of the variance in copy-spelling performance (p < 0.001, mean R2 = 0.6175). The correlations reflected between-subject, rather than within-subject, effects. The results enhance understanding of performance differences among P300 BCI users. The predictors found in this study might help in: (1) identifying suitable candidates for long-term P300 BCI operation; (2) assessing performance online. Further work on within-subject effects needs to be done to establish whether P300 BCI user performance could be improved by optimizing one or more of these EEG features.

  3. Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving

    Science.gov (United States)

    Zander, Thorsten O.; Andreessen, Lena M.; Berg, Angela; Bleuel, Maurice; Pawlitzki, Juliane; Zawallich, Lars; Krol, Laurens R.; Gramann, Klaus

    2017-01-01

    We tested the applicability and signal quality of a 16 channel dry electroencephalography (EEG) system in a laboratory environment and in a car under controlled, realistic conditions. The aim of our investigation was an estimation how well a passive Brain-Computer Interface (pBCI) can work in an autonomous driving scenario. The evaluation considered speed and accuracy of self-applicability by an untrained person, quality of recorded EEG data, shifts of electrode positions on the head after driving-related movements, usability, and complexity of the system as such and wearing comfort over time. An experiment was conducted inside and outside of a stationary vehicle with running engine, air-conditioning, and muted radio. Signal quality was sufficient for standard EEG analysis in the time and frequency domain as well as for the use in pBCIs. While the influence of vehicle-induced interferences to data quality was insignificant, driving-related movements led to strong shifts in electrode positions. In general, the EEG system used allowed for a fast self-applicability of cap and electrodes. The assessed usability of the system was still acceptable while the wearing comfort decreased strongly over time due to friction and pressure to the head. From these results we conclude that the evaluated system should provide the essential requirements for an application in an autonomous driving context. Nevertheless, further refinement is suggested to reduce shifts of the system due to body movements and increase the headset's usability and wearing comfort.

  4. 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.

  5. 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.

  6. 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.

  7. Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving.

    Science.gov (United States)

    Zander, Thorsten O; Andreessen, Lena M; Berg, Angela; Bleuel, Maurice; Pawlitzki, Juliane; Zawallich, Lars; Krol, Laurens R; Gramann, Klaus

    2017-01-01

    We tested the applicability and signal quality of a 16 channel dry electroencephalography (EEG) system in a laboratory environment and in a car under controlled, realistic conditions. The aim of our investigation was an estimation how well a passive Brain-Computer Interface (pBCI) can work in an autonomous driving scenario. The evaluation considered speed and accuracy of self-applicability by an untrained person, quality of recorded EEG data, shifts of electrode positions on the head after driving-related movements, usability, and complexity of the system as such and wearing comfort over time. An experiment was conducted inside and outside of a stationary vehicle with running engine, air-conditioning, and muted radio. Signal quality was sufficient for standard EEG analysis in the time and frequency domain as well as for the use in pBCIs. While the influence of vehicle-induced interferences to data quality was insignificant, driving-related movements led to strong shifts in electrode positions. In general, the EEG system used allowed for a fast self-applicability of cap and electrodes. The assessed usability of the system was still acceptable while the wearing comfort decreased strongly over time due to friction and pressure to the head. From these results we conclude that the evaluated system should provide the essential requirements for an application in an autonomous driving context. Nevertheless, further refinement is suggested to reduce shifts of the system due to body movements and increase the headset's usability and wearing comfort.

  8. Classification effects of real and imaginary movement selective attention tasks on a P300-based brain-computer interface

    Science.gov (United States)

    Salvaris, Mathew; Sepulveda, Francisco

    2010-10-01

    Brain-computer interfaces (BCIs) rely on various electroencephalography methodologies that allow the user to convey their desired control to the machine. Common approaches include the use of event-related potentials (ERPs) such as the P300 and modulation of the beta and mu rhythms. All of these methods have their benefits and drawbacks. In this paper, three different selective attention tasks were tested in conjunction with a P300-based protocol (i.e. the standard counting of target stimuli as well as the conduction of real and imaginary movements in sync with the target stimuli). The three tasks were performed by a total of 10 participants, with the majority (7 out of 10) of the participants having never before participated in imaginary movement BCI experiments. Channels and methods used were optimized for the P300 ERP and no sensory-motor rhythms were explicitly used. The classifier used was a simple Fisher's linear discriminant. Results were encouraging, showing that on average the imaginary movement achieved a P300 versus No-P300 classification accuracy of 84.53%. In comparison, mental counting, the standard selective attention task used in previous studies, achieved 78.9% and real movement 90.3%. Furthermore, multiple trial classification results were recorded and compared, with real movement reaching 99.5% accuracy after four trials (12.8 s), imaginary movement reaching 99.5% accuracy after five trials (16 s) and counting reaching 98.2% accuracy after ten trials (32 s).

  9. Multi-class determination of personal care products and pharmaceuticals in environmental and wastewater samples by ultra-high performance liquid-chromatography-tandem mass spectrometry.

    Science.gov (United States)

    Gracia-Lor, Emma; Martínez, Marian; Sancho, Juan V; Peñuela, Gustavo; Hernández, Félix

    2012-09-15

    In this work, a multi-class method for the simultaneous determination of 17 emerging contaminants, including pharmaceuticals and personal care products, has been developed. Target analytes were two anti-inflammatories, a lipid regulator agent, two angiotensin II antagonists, two antiepileptic drugs and a diuretic. Among personal care products, four preservatives and five UV filters were included. The method is based on solid-phase extraction (SPE) using Oasis HLB cartridges followed by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Up to three simultaneous transitions per compound were acquired to assure a reliable identification. A detailed study of the extraction process efficiency and matrix effects was carried out in surface water and effluent wastewater. The use of isotope-labeled internal standards (ILIS) was tested to compensate both potential SPE losses during sample extraction and signal suppression/enhancement observed, especially in EWW. Satisfactory correction in all water samples was only ensured when the own analyte ILIS was used. The use of analogues ILIS was a rather useful approach for correction in the majority of the samples tested when analyte ILIS was unavailable. The method was successfully validated in five different surface water (SW) samples and five effluent wastewater (EWW) samples spiked at two concentration levels (0.05 and 0.5 μg/L in SW; 0.1 and 0.5 μg/L in EWW). The developed method was applied to the analysis of 22 samples (SW and EWW) from the Spanish Mediterranean area and 51 reservoir water samples from Colombia. Personal care products were frequently detected, with the highest concentrations corresponding to benzophenone and benzophenone-4 (samples from Spain), and methylparaben (samples from Colombia). Several pharmaceuticals were detected in the Spanish samples, where irbesartan and valsartan - two Angiotensin II antagonists that are not commonly monitored in the aquatic environment

  10. Multi-class analysis of new psychoactive substances and metabolites in hair by pressurized liquid extraction coupled to HPLC-HRMS.

    Science.gov (United States)

    Montesano, Camilla; Vannutelli, Gabriele; Massa, Maristella; Simeoni, Maria Chiara; Gregori, Adolfo; Ripani, Luigi; Compagnone, Dario; Curini, Roberta; Sergi, Manuel

    2016-07-23

    In this paper, an analytical method has been developed and validated for the analysis of new psychoactive substances (NPS) and metabolites in hair samples. The method was based on pressurized liquid extraction (PLE) followed by solid-phase extraction (SPE) clean-up and high performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS) analysis. To evaluate extraction efficiency and the applicability of the method, hair samples were fortified by soaking in order to obtain a good surrogate for drug users' hair; the amount of incorporated drugs related to their lipophilicity, similarly to in vivo drug incorporation. To the best of our knowledge, this is the first method that allowed for the analysis of both cathinones (5) and synthetic cannabinoids (7) in hair with a single extraction procedure and chromatographic run. A phenethylamine (2C-T-4), 4- fluorophenylpiperazine and methoxetamine were also included showing that PLE coupled to SPE clean-up was suitable for a multi-class analysis of NPS in hair. In addition, the use of PLE significantly reduced hair analysis time: decontamination, incubation, clean-up, and liquid chromatography-mass spectrometry (LC-MS) analysis were carried out in approximately 45 min. The method was fully validated according to Scientific Working Group for Forensic Toxicology (SWGTOX) and Society of Hair Testing (SoHT) guidelines. Limit of quantification (LOQ) values ranged from 8 to 50 pg mg(-1) for cathinones, phenetylamines and piperazines, and from 9 to 40 pg mg(-1) for synthetic cannabinoids (10 pg mg(-1) for methoxetamine). Matrix effects were below 15% for all the analytes, demonstrating the effectiveness of the clean-up step. Inaccuracy was lower than 9% in terms of bias. Copyright © 2016 John Wiley & Sons, Ltd.

  11. Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection

    Science.gov (United States)

    Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

    2017-01-01

    Objectives Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Methods Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Results Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. Conclusion The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying

  12. 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.

  13. 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.

  14. 基于SVM概率输出与证据理论的多分类方法%Multi-class Classification Method Based on SVM Probability Output and Evidence Theory

    Institute of Scientific and Technical Information of China (English)

    权文; 王晓丹; 王坚; 张玉玺

    2012-01-01

    单一技术无法有效解决多类分类问题.为此,提出一种基于一对多支持向量机(SVM)的基本概率分配 输出方法,并与置信最大熵模型的D-S证据组合方法结合,给出基于SVM概率输出和证据理论的多分类模型.在3种UCI标准数据集上的仿真 结果表明,该方法的分类精度优于传统的一对多和一对一硬输出方法,是一种有效的多类分类方法.%One-technology do not solve multi-class classification problem, on the basis of this, a basic probability output distribution method based on One-Against-All(OAA) Support Vector Machine(SVM) is proposed, a multi-class model based on Support Vector Machine(SVM) probability output and evidence theory is put forward by integrating one-against-all multi-class SVM with max-entropy D-S theory, . Simulations results on three datasets of UCI repository show that the method has higher classification precision than hard output method OAA and OAO.

  15. Auditory and audio-visual processing in patients with cochlear, auditory brainstem, and auditory midbrain implants: An EEG study.

    Science.gov (United States)

    Schierholz, Irina; Finke, Mareike; Kral, Andrej; Büchner, Andreas; Rach, Stefan; Lenarz, Thomas; Dengler, Reinhard; Sandmann, Pascale

    2017-04-01

    There is substantial variability in speech recognition ability across patients with cochlear implants (CIs), auditory brainstem implants (ABIs), and auditory midbrain implants (AMIs). To better understand how this variability is related to central processing differences, the current electroencephalography (EEG) study compared hearing abilities and auditory-cortex activation in patients with electrical stimulation at different sites of the auditory pathway. Three different groups of patients with auditory implants (Hannover Medical School; ABI: n = 6, CI: n = 6; AMI: n = 2) performed a speeded response task and a speech recognition test with auditory, visual, and audio-visual stimuli. Behavioral performance and cortical processing of auditory and audio-visual stimuli were compared between groups. ABI and AMI patients showed prolonged response times on auditory and audio-visual stimuli compared with NH listeners and CI patients. This was confirmed by prolonged N1 latencies and reduced N1 amplitudes in ABI and AMI patients. However, patients with central auditory implants showed a remarkable gain in performance when visual and auditory input was combined, in both speech and non-speech conditions, which was reflected by a strong visual modulation of auditory-cortex activation in these individuals. In sum, the results suggest that the behavioral improvement for audio-visual conditions in central auditory implant patients is based on enhanced audio-visual interactions in the auditory cortex. Their findings may provide important implications for the optimization of electrical stimulation and rehabilitation strategies in patients with central auditory prostheses. Hum Brain Mapp 38:2206-2225, 2017. © 2017 Wiley Periodicals, Inc.

  16. Auditory Hallucinations Nomenclature and Classification

    NARCIS (Netherlands)

    Blom, Jan Dirk; Sommer, Iris E. C.

    2010-01-01

    Introduction: The literature on the possible neurobiologic correlates of auditory hallucinations is expanding rapidly. For an adequate understanding and linking of this emerging knowledge, a clear and uniform nomenclature is a prerequisite. The primary purpose of the present article is to provide an

  17. Nigel: A Severe Auditory Dyslexic

    Science.gov (United States)

    Cotterell, Gill

    1976-01-01

    Reported is the case study of a boy with severe auditory dyslexia who received remedial treatment from the age of four and progressed through courses at a technical college and a 3-year apprenticeship course in mechanics by the age of eighteen. (IM)

  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. 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).

  20. 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。