Online Brain-computer Interface Research Articles

A Four-Class Phase-Coded SSVEP BCI at 60Hz Using Refresh Rate.

A four-class brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEPs) was developed by presenting phase-coded 60Hz stimulations on a 240Hz LCD monitor. The task-related component analysis (TRCA) algorithm was used to detect SSVEPs with individual training data. In the BCI experiment with 10 subjects, the system achieved high classification accuracy of 94.50±6.70% and 92.71±7.56% in offline and online BCI experiments, resulting in information transfer rates (ITR) of 19.95±4.36 and 18.81±4.74 bpm, respectively. The behavioral tests on visual comfortableness and perception of flickering reveal that the proposed BCI system is very comfortable to use without any perception of flicker.

Fused Fuzzy Petri Nets: A Shared Control Method for Brain–Computer Interface Systems

It is hard to grasp objects based on brain–computer interface (BCI) by brain-actuated robot arm and hand due to its high degree of freedom. Shared control strategy and hybrid BCI are research trends to solve this control problem of brain-actuated discrete event system. We propose a new shared control method based on fused fuzzy Petri nets (PNs) for combining the robot automatic control (AC) and the brain-actuated control. This method takes the advantages of both fuzzy control and PNs such as easy modeling, robustness, and effectiveness. Both MATLAB simulation test and Barrett robot hand practical experiments show that the proposed method performs much better than AC or BCI control independently. In the online BCI practical experiment, the user successfully control the Barrett robot hand to grasp object avoiding obstacle in whole ten random scenes by our shared control method and hybrid BCI. Compared with BCI control, the user needs not to synchronously work according to the specific paradigm in the whole process. Meanwhile, our method improves safety and robustness by comparing with AC.

Brain–Computer Interface-Based Stochastic Navigation and Control of a Semiautonomous Mobile Robot in Indoor Environments

In this paper, a brain–computer interface (BCI)-based navigation and control strategy is developed for a mobile robot in indoor environments. It combines the simultaneous localization and mapping to achieve the navigation and positioning for a mobile robot in indoor environments, where the RGB landmarks are regarded as the environmental features learned by the FastSLAM algorithm. The online BCI, based on steady-state visually evoked potentials, exploits multivariate synchronization index classification algorithm to analyze the human electroencephalograph (EEG) signals so that the human intention can be recognized accurately, and then the EEG-based motion commands are produced for the mobile robot. Probability potential field approach based on the probability density function of 2-D normal distribution is connected with the brain signals to generate a collision-free trajectory for the mobile robot. The entire system is semiautonomous, since the robot’s low level behaviors are autonomous and the stochastic navigation is executed by the BCI, and it is verified by the extensive experiments involving five volunteers. All the participants can successfully tele-operate the mobile robot, and the experimental results have verified the effectiveness of the proposed approach.

A research for single trial detection of error related negativity

Error related negativity (ERN) is generated in frontal and central cortical regions when individuals perceive errors. Because ERN has low signal-to-noise ratio and large individual difference, it is difficult for single trial ERN recognition. In current study, the optimized electroencephalograph (EEG) channels were selected based on the brain topography of ERN activity and ERN offline recognition rate, and the optimized EEG time segments were selected based on the ERN offline recognition rate, then the low frequency time domain and high frequency time-frequency domain features were analyzed based on wavelet transform, after which the ERN single detection algorithm was proposed based on the above procedures. Finally, we achieved average recognition rate of 72.0% ± 9.6% in 10 subjects by using the sample points feature in 0~3.9 Hz and the power and variance features in 3.9~15.6 Hz from the EEG segments of 200~600 ms on the selected 6 channels. Our work has the potential to help the error command real-time correction technique in the application of online brain-computer interface system.

Developing a Three- to Six-State EEG-Based Brain-Computer Interface for a Virtual Robotic Manipulator Control.

We develop an electroencephalography (EEG)-based noninvasive brain-computer interface (BCI) system having short training time (15 min) that can be applied for high-performance control of robotic prosthetic systems. A signal processing system for detecting user's mental intent from EEG data based on up to six-state BCI paradigm is developed and used. We examine the performance of the developed system on experimental data collected from 12 healthy participants and analyzed offline. Out of 12 participants 3 achieve an accuracy of six-state communication in 80%-90% range, while 2 participants do not achieve a satisfactory accuracy. We further implement an online BCI system for control of a virtual 3 degree-of-freedom (dof) prosthetic manipulator and test it with our three best participants. Two participants are able to successfully complete 100% of the test tasks, demonstrating on average the accuracy rate of 80% and requiring 5-10 s to execute a manipulator move. One participant failed to demonstrate a satisfactory performance in online trials. We show that our offline EEG BCI system can correctly identify different motor imageries in EEG data with high accuracy and our online BCI system can be used for control of a virtual 3 dof prosthetic manipulator. Our results prepare foundation for further development of higher performance EEG BCI-based robotic assistive systems and demonstrate that EEG-based BCI may be feasible for robotic control by paralyzed and immobilized individuals.

Embodied VR environment facilitates motor imagery brain–computer interface training

Motor imagery (MI) is the predominant control paradigm for brain–computer interfaces (BCIs). After sufficient effort is invested to the training, the accuracy of commands mediated by mental imagery of bodily movements grows to a satisfactory level. However, many issues with the MI-BCIs persist; e.g., low bit transfer rate, BCI illiteracy, sub-optimal training procedure. Especially the training process for the MI-BCIs requires improvements. Currently, the training has an inappropriate form, resulting in a high mental and temporal demand on the users (weeks of training are required for the control). This study aims at addressing the issues with the MI-BCI training. To support the learning process, an embodied training environment was created. Participants were placed into a virtual reality environment observed from a first-person view of a human-like avatar, and their rehearsal of MI actions was reflected by the corresponding movements performed by the avatar. Leveraging extension of the sense of ownership, agency, and self-location towards a non-body object (principles known from the rubber hand illusion and the body transfer illusions) has already been proven to help in producing stronger EEG correlates of MI. These principles were used to facilitate the MI-BCI training process for the first time. Performance of 30 healthy participants after two sessions of training was measured using an on-line BCI scenario. The group trained using our embodied VR environment gained significantly higher average accuracy for BCI actions (58.3%) than the control group, trained with a standard MI-BCI training protocol (52.9%).

A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance.

Motor imagery–based brain–computer interface (BCI) using electroencephalography (EEG) has demonstrated promising applications by directly decoding users' movement related mental intention. The selection of control signals, e.g., the channel configuration and decoding algorithm, plays a vital role in the online performance and progressing of BCI control. While several offline analyses report the effect of these factors on BCI accuracy for a single session—performance increases asymptotically by increasing the number of channels, saturates, and then decreases—no online study, to the best of our knowledge, has yet been performed to compare for a single session or across training. The purpose of the current study is to assess, in a group of forty-five subjects, the effect of channel number and decoding method on the progression of BCI performance across multiple training sessions and the corresponding neurophysiological changes. The 45 subjects were divided into three groups using Laplacian Filtering (LAP/S) with nine channels, Common Spatial Pattern (CSP/L) with 40 channels and CSP (CSP/S) with nine channels for online decoding. At the first training session, subjects using CSP/L displayed no significant difference compared to CSP/S but a higher average BCI performance over those using LAP/S. Despite the average performance when using the LAP/S method was initially lower, but LAP/S displayed improvement over first three sessions, whereas the other two groups did not. Additionally, analysis of the recorded EEG during BCI control indicates that the LAP/S produces control signals that are more strongly correlated with the target location and a higher R-square value was shown at the fifth session. In the present study, we found that subjects' average online BCI performance using a large EEG montage does not show significantly better performance after the first session than a smaller montage comprised of a common subset of these electrodes. The LAP/S method with a small EEG montage allowed the subjects to improve their skills across sessions, but no improvement was shown for the CSP method.

An Adaptive Calibration Framework for mVEP-Based Brain-Computer Interface.

Electroencephalogram signals and the states of subjects are nonstationary. To track changing states effectively, an adaptive calibration framework is proposed for the brain-computer interface (BCI) with the motion-onset visual evoked potential (mVEP) as the control signal. The core of this framework is to update the training set adaptively for classifier training. The updating procedure consists of two operations, that is, adding new samples to the training set and removing old samples from the training set. In the proposed framework, a support vector machine (SVM) and fuzzy C-mean clustering (fCM) are combined to select the reliable samples for the training set from the blocks close to the current blocks to be classified. Because of the complementary information provided by SVM and fCM, they can guarantee the reliability of information fed into classifier training. The removing procedure will aim to remove those old samples recorded a relatively long time before current new blocks. These two operations could yield a new training set, which could be used to calibrate the classifier to track the changing state of the subjects. Experimental results demonstrate that the adaptive calibration framework is effective and efficient and it could improve the performance of online BCI systems.

Removal of Eye Blink Artifacts From EEG Signals Using Sparsity

Neural activities recorded using electroencephalography (EEG) are mostly contaminated with eye blink (EB) artifact. This results in undesired activation of brain-computer interface (BCI) systems. Hence, removal of EB artifact is an important issue in EEG signal analysis. Of late, several artifact removal methods have been reported in the literature and they are based on independent component analysis (ICA), thresholding, wavelet transformation, etc. These methods are computationally expensive and result in information loss which makes them unsuitable for online BCI system development. To address the above problems, we have investigated sparsity-based EB artifact removal methods. Two sparsity-based techniques namely morphological component analysis (MCA) and K-SVD-based artifact removal method have been evaluated in our work. MCA-based algorithm exploits the morphological characteristics of EEG and EB using predefined Dirac and discrete cosine transform (DCT) dictionaries. Next, in K-SVD-based algorithm an overcomplete dictionary is learned from the EEG data itself and is designed to model EB characteristics. To substantiate the efficacy of the two algorithms, we have carried out our experiments with both synthetic and real EEG data. We observe that the K-SVD algorithm, which uses a learned dictionary, delivers superior performance for suppressing EB artifacts when compared to MCA technique. Finally, the results of both the techniques are compared with the recent state-of-the-art FORCe method. We demonstrate that the proposed sparsity-based algorithms perform equal to the state-of-the-art technique. It is shown that without using any computationally expensive algorithms, only with the use of over-complete dictionaries the proposed sparsity-based algorithms eliminate EB artifacts accurately from the EEG signals.

Online EEG Classification of Covert Speech for Brain-Computer Interfacing.

Brain-computer interfaces (BCIs) for communication can be nonintuitive, often requiring the performance of hand motor imagery or some other conversation-irrelevant task. In this paper, electroencephalography (EEG) was used to develop two intuitive online BCIs based solely on covert speech. The goal of the first BCI was to differentiate between 10[Formula: see text]s of mental repetitions of the word "no" and an equivalent duration of unconstrained rest. The second BCI was designed to discern between 10[Formula: see text]s each of covert repetition of the words "yes" and "no". Twelve participants used these two BCIs to answer yes or no questions. Each participant completed four sessions, comprising two offline training sessions and two online sessions, one for testing each of the BCIs. With a support vector machine and a combination of spectral and time-frequency features, an average accuracy of [Formula: see text] was reached across participants in the online classification of no versus rest, with 10 out of 12 participants surpassing the chance level (60.0% for [Formula: see text]). The online classification of yes versus no yielded an average accuracy of [Formula: see text], with eight participants exceeding the chance level. Task-specific changes in EEG beta and gamma power in language-related brain areas tended to provide discriminatory information. To our knowledge, this is the first report of online EEG classification of covert speech. Our findings support further study of covert speech as a BCI activation task, potentially leading to the development of more intuitive BCIs for communication.

A robust beamforming approach for early detection of readiness potential with application to brain-computer interface systems.

Early detection of intention to move, at self-paced voluntary movements from the activities of neural current sources on the motor cortex, can be an effective approach to brain-computer interface (BCI) systems. Achieving high sensitivity and pre-movement negative latency are important issues for increasing the speed of BCI and other rehabilitation and neurofeedback systems used by disabled and stroke patients and helps enhance their movement abilities. Therefore, developing high-performance extractors or beamformers is a necessary task in this regard. In this paper, for the sake of improving the beamforming performance in well reconstruction of sources of readiness potential, related to hand movement, one kind of surface spatial filter (spherical spline derivative on electrode space) and the linearly constrained minimum variance (LCMV) beamformer are utilized jointly. Moreover, in order to achieve better results, the real head model of each subject was created, using individual head MRI, and was used in beamformer algorithm. Also, few optimizations were done on reconstructed source signal powers to help our template matching classifier with detection of movement onset for five healthy subjects. Our classification results show an average true positive rate (TPR) of 77.1% and 73.1%, false positive rate (FPR) of 28.96% and 28.74% and latency of -512.426 ±396. 7ms and - 360.29 ±252. 16 ms from signals of current sources of motor cortex and sensor space respectively. It can be seen that the proposed method has reliable sensitivity and is faster in prediction of movement onset and more reliable to be used for online BCI in future.

Application of a single-flicker online SSVEP BCI for spatial navigation

A promising approach for brain-computer interfaces (BCIs) employs the steady-state visual evoked potential (SSVEP) for extracting control information. Main advantages of these SSVEP BCIs are a simple and low-cost setup, little effort to adjust the system parameters to the user and comparatively high information transfer rates (ITR). However, traditional frequency-coded SSVEP BCIs require the user to gaze directly at the selected flicker stimulus, which is liable to cause fatigue or even photic epileptic seizures. The spatially coded SSVEP BCI we present in this article addresses this issue. It uses a single flicker stimulus that appears always in the extrafoveal field of view, yet it allows the user to control four control channels. We demonstrate the embedding of this novel SSVEP stimulation paradigm in the user interface of an online BCI for navigating a 2-dimensional computer game. Offline analysis of the training data reveals an average classification accuracy of 96.9±1.64%, corresponding to an information transfer rate of 30.1±1.8 bits/min. In online mode, the average classification accuracy reached 87.9±11.4%, which resulted in an ITR of 23.8±6.75 bits/min. We did not observe a strong relation between a subject’s offline and online performance. Analysis of the online performance over time shows that users can reliably control the new BCI paradigm with stable performance over at least 30 minutes of continuous operation.

Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis.

This study proposes and evaluates a novel data-driven spatial filtering approach for enhancing steady-state visual evoked potentials (SSVEPs) detection toward a high-speed brain-computer interface (BCI) speller. Task-related component analysis (TRCA), which can enhance reproducibility of SSVEPs across multiple trials, was employed to improve the signal-to-noise ratio (SNR) of SSVEP signals by removing background electroencephalographic (EEG) activities. An ensemble method was further developed to integrate TRCA filters corresponding to multiple stimulation frequencies. This study conducted a comparison of BCI performance between the proposed TRCA-based method and an extended canonical correlation analysis (CCA)-based method using a 40-class SSVEP dataset recorded from 12 subjects. An online BCI speller was further implemented using a cue-guided target selection task with 20 subjects and a free-spelling task with 10 of the subjects. The offline comparison results indicate that the proposed TRCA-based approach can significantly improve the classification accuracy compared with the extended CCA-based method. Furthermore, the online BCI speller achieved averaged information transfer rates (ITRs) of 325.33 ± 38.17 bits/min with the cue-guided task and 198.67 ± 50.48 bits/min with the free-spelling task. This study validated the efficiency of the proposed TRCA-based method in implementing a high-speed SSVEP-based BCI. The high-speed SSVEP-based BCIs using the TRCA method have great potential for various applications in communication and control.

Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees.

ObjectiveUsing traditional approaches, a brain-computer interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g., by subject-to-subject transfer of a pre-trained classifier or unsupervised adaptive classification methods which learn from scratch and adapt over time. While such heuristics work well in practice, none of them can provide theoretical guarantees. Our objective is to modify an event-related potential (ERP) paradigm to work in unison with the machine learning decoder, and thus to achieve a reliable unsupervised calibrationless decoding with a guarantee to recover the true class means.MethodWe introduce learning from label proportions (LLP) to the BCI community as a new unsupervised, and easy-to-implement classification approach for ERP-based BCIs. The LLP estimates the mean target and non-target responses based on known proportions of these two classes in different groups of the data. We present a visual ERP speller to meet the requirements of LLP. For evaluation, we ran simulations on artificially created data sets and conducted an online BCI study with 13 subjects performing a copy-spelling task.ResultsTheoretical considerations show that LLP is guaranteed to minimize the loss function similar to a corresponding supervised classifier. LLP performed well in simulations and in the online application, where 84.5% of characters were spelled correctly on average without prior calibration.SignificanceThe continuously adapting LLP classifier is the first unsupervised decoder for ERP BCIs guaranteed to find the optimal decoder. This makes it an ideal solution to avoid tedious calibration sessions. Additionally, LLP works on complementary principles compared to existing unsupervised methods, opening the door for their further enhancement when combined with LLP.

Maximizing Information Transfer in SSVEP-Based Brain-Computer Interfaces.

Compared to the different brain signals used in brain-computer interface (BCI) paradigms, the s teady-state visually evoked potential (SSVEP) features a high signal to noise ratio, enabling reliable and fast classification of neural activity patterns without extensive training requirements. In this paper, we present methods to further increase the information transfer rates (ITRs) of SSVEP-based BCIs. Starting with stimulus parameter optimizations methods, we develop an improved approach for the use of Canonical correlation analysis and analyze properties of the SSVEP when the user fixates a target and during transitions between targets. These transitions show a negative effect on the system's ITR which we trace back to delays and dead times of the SSVEP. Using two classifier types adapted to continuous and transient SSVEPs and two control modes (fast feedback and fast input), we present a simulated online BCI implementation which addresses the challenges introduced by transient SSVEPs. The resulting system reaches an average ITR of 181Bits/min and peak ITR values of up to 295Bits/min for individual users.

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

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

Attempts towards the first brain-computer interface system in INAYA Medical College

In a Brain-Computer Interface (BCI), the user only concentrates on different mental tasks which activate different functional areas in the brain. This work demonstrates the development of a first BCI system in INAYA Medical College based on electroencephalography pattern recognition. A BCI system recorded the activity of the brain and classified it into two different classes: up and down movements. Both offline and online BCI systems were handled. The offline approach was tested using data set provided by University of Tubingen, while the online approach was designed using real-time EEG data that recorded using five scalp electrodes including the reference electrode. 'Blind Source Separation' by independent component analysis was used to remove the artefacts contaminated signal to get artefact-free signal from which certain features were extracted by applying various techniques in both time and frequency domains. The features were fed to five different types of classifiers where classification accuracy reached 90%.

Attempts towards the first brain-computer interface system in INAYA Medical College

In a Brain-Computer Interface (BCI), the user only concentrates on different mental tasks which activate different functional areas in the brain. This work demonstrates the development of a first BCI system in INAYA Medical College based on electroencephalography pattern recognition. A BCI system recorded the activity of the brain and classified it into two different classes: up and down movements. Both offline and online BCI systems were handled. The offline approach was tested using data set provided by University of Tubingen, while the online approach was designed using real-time EEG data that recorded using five scalp electrodes including the reference electrode. 'Blind Source Separation' by independent component analysis was used to remove the artefacts contaminated signal to get artefact-free signal from which certain features were extracted by applying various techniques in both time and frequency domains. The features were fed to five different types of classifiers where classification accuracy reached 90%.

Optimized Bi-Objective EEG Channel Selection and Cross-Subject Generalization With Brain–Computer Interfaces

Electroencephalography (EEG) signal processing to decode motor imagery (MI) involves high-dimensional features, which increases the computational complexity. To reduce this computational burden due to the large number of channels, an iterative multiobjective optimization for channel selection (IMOCS) is proposed in this paper. For a given MI classification task, the proposed method initializes a reference candidate solution and subsequently finds a set of the most relevant channels in an iterative manner by exploiting both the anatomical and functional relevance of EEG channels. The proposed approach is evaluated on the Wadsworth dataset for the right fist versus left fist MI tasks, while considering the cross-validation accuracy as the performance evaluation criteria. Furthermore, 12 other dimension reduction and channel selection algorithms are used for benchmarking. The proposed approach (IMOCS) achieved an average classification accuracy of about 80% when evaluated using 35 best-performing subjects. One-way analysis of variance revealed the statistical significance of the proposed approach with at least 7% improvement over other benchmarking algorithms. Furthermore, a cross-subject generalization of channel selection on untrained subjects shows that the subject-independent channels perform as good as using all channels achieving an average classification accuracy of 61%. These results are promising for the online brain–computer interface (BCI) paradigm that requires low computational complexity and also for reducing the preparation time while conducting multiple session BCI experiments for a larger pool of subjects.

Reducing training requirements through evolutionary based dimension reduction and subject transfer

Training Brain Computer Interface (BCI) systems to understand the intention of a subject through Electroencephalogram (EEG) data currently requires multiple training sessions with a subject in order to develop the necessary expertise to distinguish signals for different tasks. Conventionally the task of training the subject is done by introducing a training and calibration stage during which some feedback is presented to the subject. This training session can take several hours which is not appropriate for on-line EEG-based BCI systems. An alternative approach is to use previous recording sessions of the same person or some other subjects that performed the same tasks (subject transfer) for training the classifiers. The main aim of this study is to generate a methodology that allows the use of data from other subjects while reducing the dimensions of the data. The study investigates several possibilities for reducing the necessary training and calibration period in subjects and the classifiers and addresses the impact of i) evolutionary subject transfer and ii) adapting previously trained methods (retraining) using other subjects data. Our results suggest reduction to 40% of target subject data is sufficient for training the classifier. Our results also indicate the superiority of the approaches that incorporated evolutionary subject transfer and highlights the feasibility of adapting a system trained on other subjects.