Independent Brain-computer Interface Research Articles

Spatial Decoding for Gaze Independent Brain-Computer Interface Based on Covert Visual Attention Shift Using Electroencephalography.

The gaze-independent brain-computer interface (BCI) device is used to re-establish interaction for individuals who have abnormal eye movement. It may be possible to control the BCI by shifting your attention spatially. However, spatial attention is rarely employed to increase the effectiveness of target detection and is typically used to provide a simple "yes" or "no" response to the target recognition inquiry. To improve the effectiveness of detecting target, it is crucial to take advantage of the possible advantages of spatial attention. N2-posterior-contralateral (N2pc) component reflects correlates of visual spatial attention and is used to determine target position. In this study, a long-short-term memory (LSTM) network is used to answer "yes/no" questions by decoding covert spatial attention based on N2pc characteristics using EEG signals. The proposed LSTM-based model's average decoding accuracy is 92.79%. The target detection efficiency was successfully increased by about 4% when compared to conventional machine learning algorithms. The proposed model is tested on the independent dataset to validate its performance. The results of this work show that N2pc characteristics can be employed in gaze-independent BCIs for tracking covert attention shifts, which may help persons with poor eye mobility to connect with their environment.

Multiple Scale Convolutional Few-Shot Learning Networks for Online P300-Based Brain–Computer Interface and Its Application to Patients With Disorder of Consciousness

P300 brain-computer interfaces (BCIs) have significant potential for detecting and assessing residual consciousness in patients with disorders of consciousness (DoC) but are limited by insufficient data collected from them. In this study, a multiple scale convolutional few-shot learning network (MSCNN-FSL) was proposed to detect and recognize small sample P300 signals. A multiple scale convolutional neural network (MSCNN) was developed to learn different scale features from different scales of receptive fields to obtain more information from electroencephalograms (EEG). Then, a prototypical network with cosine distance was introduced as a classifier to classify and small sample P300 signals. The MSCNN-FSL was evaluated in two independent online BCI experiments. In the first P300 speller experiment, the presented network achieves good character recognition performance with average accuracies of 98.02%±1.70%. In the second experiment, eight healthy controls achieved photo recognition performance with average accuracies of 98.75%±1.49% and three of the twelve DoC patients achieved more than 64% online accuracies with significance. Our results indicated that the proposed MSCNN-FSL could correctly assess the three patients who may have residual consciousness but were misdiagnosed by the Coma Recovery Scale-Revised (CRS-R). Clinical evaluation after two months did prove our BCI assessment results for the three patients.

Performance Variation of a Somatosensory BCI Based on Imagined Sensation: A Large Population Study.

A proportion of users cannot achieve adequate brain-computer interface (BCI) control. The diversity of BCI modalities provides a way to solve this emerging issue. Here, we investigate the accuracy of a somatosensory BCI based on sensory imagery (SI). During the SI tasks, subjects were instructed to imagine a tactile sensation and to maintain the attention on the corresponding hand, as if there was tactile stimulus on the skin of the wrist. The performance across 106 healthy subjects in left- and right-hand SI discrimination was 78.9±13.2%. In 70.7% of the subjects the performance was above 70%. The SI task induced a contralateral cortical activation, and high-density EEG source localization showed that the real tactile stimulation and imagined tactile stimulation shared similar cortical activations within the somatosensory cortex. The somatosensory BCI based on SI provides a new signal modality for independent BCI development. Moreover, a combination of SI and other BCI modalities, such as motor imagery, may provide new avenues for further improving BCI usage and applicability, especially in those subjects unable to attain adequate BCI control with conventional BCI modalities.

Toward the Development of Versatile Brain–Computer Interfaces

Recent advances in artificial intelligence demand an automated framework for the development of versatile brain–computer interface (BCI) systems. In this article, we proposed a novel automated framework that reveals the importance of multidomain features with feature selection to increase the performance of a learning algorithm for motor imagery electroencephalogram task classification on the utility of signal decomposition methods. A framework is explored by investigating several combinations of signal decomposition methods with feature selection techniques. Thus, this article also provides a comprehensive comparison among the aforementioned modalities and validates them with several performance measures, robust ranking, and statistical analysis (Wilcoxon and Friedman) on public benchmark databases. Among all the combinations, the variational mode decomposition, multidomain features obtained with linear regression, and the cascade-forward neural network provide better classification accuracy results for both subject-dependent and independent BCI systems in comparison with other state-of-the-art methods. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Impact Statement</i>—The brain–computer interface (BCI) is a revolutionary device that utilizes cognitive function explicitly for the interaction of external devices without any motor intervention. BCI systems based on motor imagery have shown efficacy for stroke patient treatment, but poor performance, nonflexible characteristics, and lengthy training sessions have limited their use in clinical practice. The proposed automated framework overcomes these limitations. With the significant improvement of up to 26.1% and 26.4% in comparison with the available literature, the proposed automated framework could offer help to BCI device developers to develop flexible BCI devices and provide interaction for motor-disabled users.

Single stimulus location for two inputs: A combined brain-computer interface based on Steady-State Visual Evoked Potential (SSVEP).

Brain-computer interfaces (BCI) help severely paralyzed people communicate with the outside world. One type of BCI depends on eye movements and has high information transfer (ITR) but is tiring for users and not applicable to people with eye dyskinesia. Conversely, independent BCIs enable attention shifts across visual stimuli without eye movement, but at the cost of a lower ITR. Steady-state visual evoked potential (SSVEP) is an oscillatory brain response and typically used as BCI signal sources because of high signal-to-noise ratio (SNR). Considering the effect of attentional modulation on the SSVEP, we proposed the novel concept of one-to-two BCI to optimize existing problems, wherein the target and other stimuli shared the same location. Specifically, two spatially overlapping stimuli were displayed in the center-of-view field, as in the independent BCI, and participants were required to divide their attention between the right and left visual fields, as in the dependent BCI. Using three different design schemes in two experiments, we aimed to provide a new framework for BCI design by exploring the feasibility of a combined BCI that can realize a single stimulus location for two inputs. The results strongly demonstrated that, even when the targets and distractors overlapped spatially, the former evoked stronger SSVEP responses. Notably, the BCI scheme based on the object-based attention could achieve a recognition rate as high as 83.2% and an ITR of 12.5 bits per minute. The feasibility of a one-to-two BCI design, which simplified the keyboard layout, reduced the attention shift, and relieved user fatigue, was established.

A performance based feature selection technique for subject independent MI based BCI.

Significant research has been conducted in the field of brain computer interface (BCI) algorithm development, however, many of the resulting algorithms are both complex, and specific to a particular user as the most successful methodology can vary between individuals and sessions. The objective of this study was to develop a simple yet effective method of feature selection to improve the accuracy of a subject independent BCI algorithm and streamline the process of BCI algorithm development. Over the past several years, several high precision features have been suggested by researchers to classify different motor imagery tasks. This research applies fourteen of these features as a feature pool that can be used as a reference for future researchers. Additionally, we look for the most efficient feature or feature set with four different classifiers that best differentiates several motor imagery tasks. In this work we have successfully employed a feature fusion method to obtain the best sub-set of features. We have proposed a novel computer aided feature selection method to determine the best set of features for distinguishing between motor imagery tasks in lieu of the manual feature selection that has been performed in past studies. The features selected by this method were then fed into a Linear Discriminant Analysis, K-nearest neighbor, decision tree, or support vector machine classifier for classification to determine the overall performance. The methods used were a novel performance based additive feature fusion algorithm working in conjunction with machine learning in order to classify the motor imagery signals into particular states. The data used for this study was collected from BCI competition III dataset IVa. The result of this algorithm was a classification accuracy of 99% for a subject independent algorithm with less computation cost compared to traditional methods, in addition to multiple feature/classifier combinations that outperform current subject independent methods. The conclusion of this study and its significance is that it developed a viable methodology for simple, efficient feature selection and BCI algorithm development, which leads to an overall increase in algorithm classification accuracy.

Does A Subject Independent Dynamic Stopping Model for P300 Speller Work on Different Flash Durations and Inter Stimulus Intervals?

Event-related potential (ERP)-based brain- computer interfacing (BCI) is an effective communication method. However, calibration itself can be unintuitive and tedious for users. The no-calibration Subject Independent Brain Computer Interface (SIBCI) is a popular solution to the lengthy calibration. Researches have proved the subject independent model is efficient in some P300 spellers, but it is still need to be explored whether the subject independent model works when the flash durations (FDs) and the inter stimulus intervals (ISIs) are changed in a P300 speller. This study introduces a subject independent dynamical stopping model (SIDSM), which based on a subject independent model to dynamically stop the data collection process. The performance of the SIDSM is studied by modifying the FDs and ISIs in online experiments for 8 subjects. Results showed the SIDSM has an average accuracy of 92.45% for different settings. This research proved that the SIDSM is very robust to different stimulus parameters as good performance is observed across all experimental sessions.

Brain Computer Interface Using Modulation of Auditory Steady-State Response with Help of Stochastic Resonance.

This paper proposes an eye-movement independent brain computer interface based on the modulations of auditory steady-state response (ASSR-BCI) to amplitude-modulated (AM) tones elicited by paying selective attention to one of the two AM tones. Moreover, the proposed ASSR-BCI exploits a stochastic resonance effect to improve the signal separation and attained the mean classification accuracy of 77 % across nine normal subjects under a noise-added condition with sound pressures 60 dB for the two tones and 30 dB for the noise added to the two AM tones. Results from information transfer rate and its inter-individual difference suggest that it may be adequate to set an inter-trial interval at 2∽3 s for a trial time length. It is consequently feasible to develop a practical eye-movement-independent BCI available in eyes-closed state by optimizing the parameters such as the trial time length and electrode sites each user.

Features Selection and Pattern Classification of Electroencephalography Motor Imagery Tasks of Right Hand

This study presentsa Brain Computer Interface (BCI) approach to detect the motor intents of the disabled people with right hand amputation. Electroencephalography (EEG) Motor Imagery (MI)-based Brain Computer Interface (BCI) systems have been recently used to improve the quality of life of disabled people. However, to naturally trigger particular applications (i.e., upper limb prostheses), independent BCIs appeal further paradigms to involve realistic motor imagery tasks. This study proposes an approach to classifying imagined hand gesture tasks, including the water glass gesture and the index pointer gesture of the right hand using OPENBCI as a consumer-grade EEG acquisition device. For three subjects, the data recorded by OPENBCI were sampled with a sampling rate of 250 Hz. The Minimum Redundancy Maximum Relevance (MRMR) technique was implemented as a feature selection method along with the Support Vector Machine (SVM) algorithm for classification. By obtaining a maximum classification accuracy of 91.7%, the results showed the feasibility of such Brain Computer Interface systems to detect different motor imagery tasks for the right hand. Consequently, upper limb prostheses could be manipulated using the intended motor imagery tasks.

Characterizing Computer Access Using a One-Channel EEG Wireless Sensor.

This work studies the feasibility of using mental attention to access a computer. Brain activity was measured with an electrode placed at the Fp1 position and the reference on the left ear; seven normally developed people and three subjects with cerebral palsy (CP) took part in the experimentation. They were asked to keep their attention high and low for as long as possible during several trials. We recorded attention levels and power bands conveyed by the sensor, but only the first was used for feedback purposes. All of the information was statistically analyzed to find the most significant parameters and a classifier based on linear discriminant analysis (LDA) was also set up. In addition, 60% of the participants were potential users of this technology with an accuracy of over 70%. Including power bands in the classifier did not improve the accuracy in discriminating between the two attentional states. For most people, the best results were obtained by using only the attention indicator in classification. Tiredness was higher in the group with disabilities (2.7 in a scale of 3) than in the other (1.5 in the same scale); and modulating the attention to access a communication board requires that it does not contain many pictograms (between 4 and 7) on screen and has a scanning period of a relatively high 10 s. The information transfer rate (ITR) is similar to the one obtained by other brain computer interfaces (BCI), like those based on sensorimotor rhythms (SMR) or slow cortical potentials (SCP), and makes it suitable as an eye-gaze independent BCI.

Online control of a humanoid robot through hand movement imagination using CSP and ECoG based features.

Intention recognition through decoding brain activity could lead to a powerful and independent Brain-Computer-Interface (BCI) allowing for intuitive control of devices like robots. A common strategy for realizing such a system is the motor imagery (MI) BCI using electroencephalography (EEG). Changing to invasive recordings like electrocorticography (ECoG) allows extracting very robust features and easy introduction of an idle state, which might simplify the mental task and allow the subject to focus on the environment. Especially for multi-channel recordings like ECoG, common spatial patterns (CSP) provide a powerful tool for feature optimization and dimensionality reduction. This work focuses on an invasive and independent MI BCI that allows triggering from an idle state, and therefore facilitates tele-operation of a humanoid robot. The task was to lift a can with the robot's hand. One subject participated and reached 95.4 % mean online accuracy after six runs of 40 trials. To our knowledge, this is the first online experiment with a MI BCI using CSPs from ECoG signals.

Long-Term Independent Brain-Computer Interface Home Use Improves Quality of Life of a Patient in the Locked-In State: A Case Study

ObjectiveDespite intense brain-computer interface (BCI) research for >2 decades, BCIs have hardly been established at patients' homes. The current study aimed at demonstrating expert independent BCI home use by a patient in the locked-in state and the effect it has on quality of life. DesignIn this case study, the P300 BCI-controlled application Brain Painting was facilitated and installed at the patient's home. Family and caregivers were trained in setting up the BCI system. After every BCI session, the end user indicated subjective level of control, loss of control, level of exhaustion, satisfaction, frustration, and enjoyment. To monitor BCI home use, evaluation data of every session were automatically sent and stored on a remote server. Satisfaction with the BCI as an assistive device and subjective workload was indicated by the patient. In accordance with the user-centered design, usability of the BCI was evaluated in terms of its effectiveness, efficiency, and satisfaction. The influence of the BCI on quality of life of the end user was assessed. SettingAt the patient's home. ParticipantA 73-year-old patient with amyotrophic lateral sclerosis in the locked-in state. InterventionsNot applicable. Main Outcome MeasureThe BCI has been used by the patient independent of experts for >14 months. The patient painted in about 200 BCI sessions (1–3 times per week) with a mean painting duration of 81.86 minutes (SD=52.15, maximum: 230.41). BCI improved quality of life of the patient. ResultsIn most of the BCI sessions the end user's satisfaction was high (mean=7.4, SD=3.24; range, 0–10). Dissatisfaction occurred mostly because of technical problems at the beginning of the study or varying BCI control. The subjective workload was moderate (mean=40.61; range, 0–100). The end user was highy satisfied with all components of the BCI (mean 4.42–5.0; range, 1–5). A perfect match between the user and the BCI technology was achieved (mean: 4.8; range, 1–5). Brain Painting had a positive impact on the patient's life on all three dimensions: competence (1.5), adaptability (2.17) and self-esteem (1.5); (range: –3 = maximum negative impact; 3 maximum positive impact). The patient had her first public art exhibition in July 2013; future exhibitions are in preparation. ConclusionsIndependent BCI home use is possible with high satisfaction for the end user. The BCI indeed positively influenced quality of life of the patient and supports social inclusion. Results demonstrate that visual P300 BCIs can be valuable for patients in the locked-in state even if other means of communication are still available (eye tracker).

Executed Movement Using EEG Signals through a Naive Bayes Classifier

Recent years have witnessed a rapid development of brain-computer interface (BCI) technology. An independent BCI is a communication system for controlling a device by human intension, e.g., a computer, a wheelchair or a neuroprosthes is, not depending on the brain’s normal output pathways of peripheral nerves and muscles, but on detectable signals that represent responsive or intentional brain activities. This paper presents a comparative study of the usage of the linear discriminant analysis (LDA) and the naive Bayes (NB) classifiers on describing both right- and left-hand movement through electroencephalographic signal (EEG) acquisition. For the analysis, we considered the following input features: the energy of the segments of a band pass-filtered signal with the frequency band in sensorimotor rhythms and the components of the spectral energy obtained through the Welch method. We also used the common spatial pattern (CSP) filter, so as to increase the discriminatory activity among movement classes. By using the database generated by this experiment, we obtained hit rates up to 70%. The results are compatible with previous studies.

Assessment of the applicability of independent brain-computer interfaces in robotic systems

The paper is devoted to assessing the applicability of the use of independent brain-computer interfaces in engineering systems (in particular, in robotics). This technology provides the closest integration between human beings and automated systems for various purposes. It may be of particular interest in solving control problems in robotic systems—in particular, in systems of devices extending human manipulatory capabilities. However, currently there is no universal algorithm for the assessment of the applicability of brain-computer interfaces in various technological systems. This hampers the implementation of this technology. A description of the proposed method of assessing applicability is given that is based on the conceptual and methodological apparatus of digital signal processing, i.e., the discrete Fourier transform, cross-correlation coefficient, and distance between the vectors. The stages of operation of the proposed algorithm can be reduced to a step-by-step comparison of groups of averaged spectra of amplitudes calculated for signals that are measured of the brain’s electrical activity. Experimental results of the practical application of the method that has been developed are presented in graphical form. The method makes it possible to efficiently assess the applicability of the use of an independent brain-computer interface in various technological systems and circumstances.

Eye-gaze independent EEG-based brain–computer interfaces for communication

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.

New stimulation pattern for covert SSVEP-based brain-computer interface

New stimulation pattern for covert SSVEP-based brain-computer interface

Gaze independent brain–computer speller with covert visual search tasks

Objective Brain–computer interface (BCI) provides a mean of communication for the patients that are severely disabled by neuromuscular diseases. The performance of the classical P300 speller, however, declines noticeably in the gaze fixation condition. The speller paradigm presented in this paper aims to release the gaze dependency at the cost of an extra covert visual search task. Methods Clusters of characters were presented sequentially in the near-central vision as stimulation. Participants fixed their gaze on the center, searched and recognized the target character with covert shift of attention. Random position (RP) and fixed position (FP) presentation modes designed with different searching set size (6 for RP, and ⩽2 for FP) were examined. Results Online sessions using 10 stimulus sequences achieved character accuracies of 94.4% and 96.3% for RP and FP mode, respectively. For offline overall evaluation, the peak written symbol rate (WSR) of 1.38 symbols/min was obtained, with corresponding accuracies of 87.8% (RP) and 84.1% (FP). The P300 waveform of RP mode has evident longer latency and larger amplitude. Electrooculogram (EOG) analysis indicated that the performance was independent of gaze shift. Conclusions The proposed speller could be operated effectively and gaze independently by healthy participants. Significance The proposed gaze independent BCI approach promises reasonable communication capability for the profoundly paralyzed patients with head or ocular motor impairments.

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

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.

Visual Spatial Attention Control in an Independent Brain-Computer Interface

This paper presents a novel brain computer interface (BCI) design employing visual evoked potential (VEP) modulations in a paradigm involving no dependency on peripheral muscles or nerves. The system utilizes electrophysiological correlates of visual spatial attention mechanisms, the self-regulation of which is naturally developed through continuous application in everyday life. An interface involving real-time biofeedback is described, demonstrating reduced training time in comparison to existing BCIs based on self-regulation paradigms. Subjects were cued to covertly attend to a sequence of letters superimposed on a flicker stimulus in one visual field while ignoring a similar stimulus of a different flicker frequency in the opposite visual field. Classification of left/right spatial attention is achieved by extracting steady-state visual evoked potentials (SSVEPs) elicited by the stimuli. Six out of eleven physically and neurologically healthy subjects demonstrate reliable control in binary decision-making, achieving at least 75% correct selections in at least one of only five sessions, each of approximately 12-min duration. The highest-performing subject achieved over 90% correct selections in each of four sessions. This independent BCI may provide a new method of real-time interaction for those with little or no peripheral control, with the added advantage of requiring only brief training.

Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication

The steady-state visual evoked potential (SSVEP) has been employed successfully in brain-computer interface (BCI) research, but its use in a design entirely independent of eye movement has until recently not been reported. This paper presents strong evidence suggesting that the SSVEP can be used as an electrophysiological correlate of visual spatial attention that may be harnessed on its own or in conjunction with other correlates to achieve control in an independent BCI. In this study, 64-channel electroencephalography data were recorded from subjects who covertly attended to one of two bilateral flicker stimuli with superimposed letter sequences. Offline classification of left/right spatial attention was attempted by extracting SSVEPs at optimal channels selected for each subject on the basis of the scalp distribution of SSVEP magnitudes. This yielded an average accuracy of approximately 71% across ten subjects (highest 86%) comparable across two separate cases in which flicker frequencies were set within and outside the alpha range respectively. Further, combining SSVEP features with attention-dependent parieto-occipital alpha band modulations resulted in an average accuracy of 79% (highest 87%).