Steady-state Visual Evoked Potential Brain-computer Interface Research Articles

A MultiModal Vigilance (MMV) dataset during RSVP and SSVEP brain-computer interface tasks

Vigilance represents an ability to sustain prolonged attention and plays a crucial role in ensuring the reliability and optimal performance of various tasks. In this report, we describe a MultiModal Vigilance (MMV) dataset comprising seven physiological signals acquired during two Brain-Computer Interface (BCI) tasks. The BCI tasks encompass a rapid serial visual presentation (RSVP)-based target image retrieval task and a steady-state visual evoked potential (SSVEP)-based cursor-control task. The MMV dataset includes four sessions of seven physiological signals for 18 subjects, which encompasses electroencephalogram(EEG), electrooculogram (EOG), electrocardiogram (ECG), photoplethysmogram (PPG), electrodermal activity (EDA), electromyogram (EMG), and eye movement. The MMV dataset provides data from four stages: 1) raw data, 2) pre-processed data, 3) trial data, and 4) feature data that can be directly used for vigilance estimation. We believe this dataset will achieve flexible reuse and meet the various needs of researchers. And this dataset will greatly contribute to advancing research on physiological signal-based vigilance research and estimation.

Efficient dual-frequency SSVEP brain-computer interface system exploiting interocular visual resource disparities

Here, we present a novel dual-frequency steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) system with a unique human–computer interaction (HCI) feature that utilizes distinct visual resource disparities between the two eyes. By leveraging subtle differences in electroencephalography (EEG) responses due to binocular disparity, our system effectively distinguishes identical frequency targets. We conducted a two-phase validation: initial offline experiments confirmed EEG differences from interocular disparity, followed by global optimization to refine the SSVEP coding and spatial arrangement, allowing for 40 distinct targets within 20 frequency combinationes. A tailored binocular-task-related component analysis (bTRCA) algorithm was developed, achieving a maximum individual information transmission rate (ITR) of 338.32 bits/min offline and an average of 224.96 bits/min, with the online version reaching an average ITR of 234.13 bits/min. These results indicate that this system is a leading candidate for dual-frequency SSVEP BCIs, opening new prospects for HCI applications.

Dual-Alpha: a large EEG study for dual-frequency SSVEP brain-computer interface.

The domain of brain-computer interface (BCI) technology has experienced significant expansion in recent years. However, the field continues to face a pivotal challenge due to the dearth of high-quality datasets. This lack of robust datasets serves as a bottleneck, constraining the progression of algorithmic innovations and, by extension, the maturation of the BCI field. This study details the acquisition and compilation of electroencephalogram data across 3 distinct dual-frequency steady-state visual evoked potential (SSVEP) paradigms, encompassing over 100 participants. Each experimental condition featured 40 individual targets with 5 repetitions per target, culminating in a comprehensive dataset consisting of 21,000 trials of dual-frequency SSVEP recordings. We performed an exhaustive validation of the dataset through signal-to-noise ratio analyses and task-related component analysis, thereby substantiating its reliability and effectiveness for classification tasks. The extensive dataset presented is set to be a catalyst for the accelerated development of BCI technologies. Its significance extends beyond the BCI sphere and holds considerable promise for propelling research in psychology and neuroscience. The dataset is particularly invaluable for discerning the complex dynamics of binocular visual resource distribution.

Development of an Adaptive Artifact Subspace Reconstruction Based on Hebbian/Anti-Hebbian Learning Networks for Enhancing BCI Performance.

Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c = 1-10 for activity-specific BCI applications and a moderate range of for the benchmark dataset and general BCI applications.

BCI Control of a Robotic arm based on SSVEP with Moving Stimuli for Reach and grasp Tasks.

Brain-computer interface (BCI) provides a novel technology for patients and healthy human subjects to control a robotic arm. Currently, BCI control of a robotic arm to complete the reaching and grasping tasks in an unstructured environment is still challenging because the current BCI technology does not meet the requirement of manipulating a multi-degree robotic arm accurately and robustly. BCI based on steady-state visual evoked potential (SSVEP) could output a high information transfer rate; however, the conventional SSVEP paradigm failed to control a robotic arm to move continuously and accurately because the users have to switch their gaze between the flickering stimuli and the target frequently. This study proposed a novel SSVEP paradigm in which the flickering stimuli were attached to the robotic arm's gripper and moved with it. First, an offline experiment was designed to investigate the effects of moving flickering stimuli on the SSVEP's responses and decoding accuracy. After that, contrast experiments were conducted, and twelve subjects were recruited to participate in a robotic arm control experiment using both the paradigm one (P1, with moving flickering stimuli) and the paradigm two (P2, conventional fixed flickering stimuli) using a block randomization design to balance their sequences. Double blinks were used to trigger the grasping action asynchronously whenever the subjects were confident that the position of the robotic arm's gripper was accurate enough. Experimental results showed that the paradigm P1 with moving flickering stimuli provided a much better control performance than the conventional paradigm P2 in completing a reaching and grasping task in an unstructured environment. Subjects' subjective feedback scored by a NASA-TLX mental workload scale also corroborated the BCI control performance. The results of this study suggest that the proposed control interface based on SSVEP BCI provides a better solution for robotic arm control to complete the accurate reaching and grasping tasks.

Effect of Local Network Characteristics on the Performance of the SSVEP Brain-Computer Interface

ObjectiveFor decades, a great deal of interest in investigating brain network functional connective features has arisen in brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs). Traditional decoding algorithms, for example, canonical correlation analysis (CCA), only consider the inherent properties of each channel in terms of feature extraction for the single channel electroencephalogram (EEG) signal, with inadequate features that cannot fully utilize the information transmitted by the brain. Material and methodsThis paper proposes a fused feature extraction method, CCA-DTF, which combines CCA with a direct transfer function (DTF) to construct local brain network features with seven leads in the occipital region. First, the features extracted by the CCA algorithm were combined with these features extracted by DTF to analyze the EEG data from 20 subjects. Then, two methods, support vector machine (SVM) and random forest (RF), were used in constructing the classifiers for the four tasks classification of the SSVEP-BCI. ResultsThe experimental results showed that incorporating local network features (extracted from DTF or Pearson correlation coefficient) can effectively improve the classification average accuracy and the information transfer rate (ITR) of SSVEP, not only for SVM but also for the ensemble method RF. In particular, CCA-DTF plus SVM obtained a 94.52% classification average accuracy and a 49.23 bits/min ITR in a time window of 2 seconds. The performance was 5.57% and 8.01 bits/min higher than those of traditional CCA plus SVM, respectively. ConclusionThe proposed feature extraction method based on local network features is robust for improving the performance of SSVEP-BCI significantly, which has a perspective of being used in neural rehabilitation engineering field.

A Binocular Vision SSVEP Brain-Computer Interface Paradigm for Dual-Frequency Modulation.

This study presents a novel brain-computer interface paradigm of dual-frequency modulated steady-state visual evoked potential (SSVEP), aiming to suppress the unpredictable intermodulation components in current applications. This paradigm is especially suitable for training-free scenarios. This study built a dual-frequency binocular vision SSVEP brain-computer interface system using circularly polarized light technology. Two experiments, including a 6-target offline experiment and a 40-target online experiment, were taken with this system. Meanwhile, an improved algorithm filter bank dual-frequency canonical correlation analysis (FBDCCA) was presented for the dual-frequency SSVEP paradigm. Energy analysis was conducted for 9 subjects in the 6-target dual-frequency offline experiment, among which the signal-to-noise ratio of target frequency components have increased by 2 dB compared to the one of unpredictable intermodulation components. Subsequently, the online experiment with 40 targets was conducted with 12 subjects. With this new dual-frequency paradigm, the online training-free experiment's average information transmission rate (ITR) reached 104.56 ± 15.74 bits/min, which was almost twice as fast as the current best dual-frequency paradigm. And the average information transfer rate for offline training analysis of this new paradigm was 180.87 ± 17.88 bits/min. These results demonstrate that this new dual-frequency SSVEP brain-computer interface paradigm can suppress the unpredictable intermodulation harmonics and generate higher quality responses while completing dual-frequency encoding. Moreover, its performance shows high ITR in applications both with and without training. It is thus believed that this paradigm is competent for achieving large target numbers in brain-computer interface systems and has more possible practices.

Fifty-selective SSVEP-BCI Speller with CCA

In this study, we used canonical correlation analysis (CCA), frequency component correlation method (FCCM) and ensemble model to develop a steady state visual evoked potential brain-computer interface (SSVEP-BCI) with fifty-selective. For the proposed fifty-selective SSVEP-BCI with only CCA, it was not possible to obtain sufficient SSVEP induction. In a previous study where a similar problem occurred, the maximum accuracy was 79.53%, and the information transfer rate (ITR) was 45.16 bits/min. Therefore, we proposed FCCM and ensemble model in CCA to improve the accuracy even when the SSVEP induction was not sufficient. We used the proposed method to achieve the highest accuracy of 93.23% and the highest ITR of 58.88 bits/min. The system also achieved an average accuracy of 71.01% and an average ITR of 40.79 bits/min, demonstrating the usefulness of the system. Also, the maximum accuracy and ITR in additional experiments were 98.53% and 65.41 bits/min.

A Novel SSVEP Brain-Computer Interface System Based on Simultaneous Modulation of Luminance and Motion.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have received significant attention owing to their high information transfer rate (ITR) and low training requirements. Previous SSVEP-based BCIs mostly adopt the stationary visual flickers where only a few studies have explored the effect of moving visual flickers on the SSVEP-BCI. In this study, a novel stimulus encoding method based on the simultaneous modulation of luminance and motion was proposed. We adopted the sampled sinusoidal stimulation method to encode the frequencies and phases of stimulus targets. In addition to luminance modulation, at the same time, visual flickers also moved horizontally towards right and left at different frequencies (i.e., 0, 0.2, 0.4, and 0.6 Hz) following a sinusoidal function. Accordingly, a nine-target SSVEP-BCI was built to evaluate the influence of motion modulation on the BCI performance. Filter bank canonical correlation analysis (FBCCA) approach was adopted to identify the stimulus targets. Offline experimental results of 17 subjects revealed that the system performance decreased with the increase of superimposed horizontal periodic motion frequency. Our online experimental results showed that the subjects achieved 85.00 ± 6.77 % and 83.15 ± 9.88 % accuracy for the superimposed horizontal periodic motion frequencies of 0 and 0.2 Hz, respectively. These results verified the feasibility of the proposed systems. In addition, the system with 0.2 Hz horizontal motion frequency provided the best visual experience for subjects. These results indicated that moving visual stimulus can provide an alternative option for SSVEP-BCIs. Furthermore, the proposed paradigm is expected to develop a more comfortable BCI system.

뇌-컴퓨터 인터랙션 오탐 저감을 위한 뇌파-턱 근전도 통합 신호 제어 프로토콜 개발

Objective: The purpose of this study was to investigate whether electromyogram (EMG) induced by jaw-clenching can be accurately observed in steady-state visually evoked potential (SSVEP) signals at occipital lobes as well as temporal lobes. We also investigated a feasibility that the fused SSVEP data with the jaw-clenching EMG can be classified as a function of task types characterized by SSVEPs with and without the short and long clenching noise. Based on this hypothesis, we proposed a novel command protocol to effectively and efficiently reduce false alarms caused by using SSVEP brain-computer interface (BCI).<BR><BR>Background: Much attention has been paid to various brain-computer interfaces for neural rehabilitation and alternative communication channels for disabled people with severe neurological disorders. However, despite the interests in BCI, false alarms caused by interference effects between command flickers with different frequencies or internal and external noises have been still problematic and have yet to be investigated. Even though protocols to correct the false alarms based on error-related potential (ErrP) or eye blinks have been proposed, it has still limitation due to errors inherent in involuntary reactions of humans. Thus, it is necessary to develop a protocol to address the issue and propose a method insusceptible to the involuntary reactions.<BR><BR>Method: Nine undergraduate students (3 female) voluntarily participated in the experiment. They were asked to divide their attention between the two squares randomly oscillating with 8.57 and 10Hz and perform three types of tasks characterized by SSVEP only, short clenching, and long clenching conditions. They carried out the three tasks with a semi-counterbalanced and random order and rest interval between the tasks was set to 10 minutes to minimize order and carryover effects. It took about 12 minutes in completing the experimental tasks for each participant. We used five machine learning algorithms of binary classification to compare the performance in classifying the brainwave signals characterized by the three tasks.<BR><BR>Results: As a result of the classification comparison, random forest showed the highest classification performance of more than 0.85 (AUC) as follows: 0.88 (short clenching vs. SSVEP), 0.87 (long clenching vs. SSVEP), 0.85 (long clenching vs. short clenching condition) in the test data set. Notably, the findings implicated a clear classification in the long cand short clenching condition even at the frequency band of more than 20Hz. For the participants whose highest weight factor was shown at the occipital lobe, it can be possible to reduce false alarms in the SSVEP BCI with only single electrode at the occipital lobe.<BR><BR>Conclusion: The findings found in this study implicate that the proposed protocol can be used to effectively and efficiently correct false alarms in event-related potential (ERP), event-related desynchronization/synchronization (ERD/ERS) BCI as well as SSVEP BCI. On top of that, classifying the subtle changes in the jaw clenching types enables BCI developers to create additional commands to modulate their BCI system. The results can be greatly improved if weight factors in the binary classification are modulated as a function of individual characteristics.<BR><BR>Application: The findings obtained in this study can be utilized in developing asynchronous BCI very robust to false alarms and expected to provide a future direction into hybrid BCI system.

A Zero-Padding Frequency Domain Convolutional Neural Network for SSVEP Classification

The brain-computer interface (BCI) of steady-state visual evoked potential (SSVEP) is one of the fundamental ways of human-computer communication. The main challenge is that there may be a nonlinear relationship between different SSVEP in other states. For improving the performance of SSVEP BCI, a novel CNN algorithm model is proposed in this study. Based on the discrete Fourier transform to calculate the signal's power spectral density (PSD), we perform zero-padding in the signal's time domain to improve its performance on the PSD and make it more refined. In this way, the frequency point interval in the PSD of the SSVEP is consistent with the minimum gap between the stimulation frequency. Combining the nonlinear transformation capabilities of CNN in deep learning, a zero-padding frequency domain convolutional neural network (ZPFDCNN) model is proposed. Extensive experiments based on the SSVEP dataset validate the effectiveness of our method. The study verifies that the proposed ZPFDCNN method can improve the effectiveness of the SSVEP-based high-speed BCI ITR. It has massive potential in the application of BCI.

A classification algorithm of an SSVEP brain-Computer interface based on CCA fusion wavelet coefficients

BackgroundIn the study of brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs), how to improve the classification accuracies of BCIs has always been the focus of researchers. Canonical correlation analysis (CCA) is widely used in BCI systems of SSVEPs because of its rapidity and scalability. However, the classical CCA algorithm always encounters the difficulty of low accuracy in a short time. New methodFor targetless stimuli, this paper proposes a fusion algorithm (CCA-CWT-SVM) that is combined with CCA, a continuous wavelet transform, and a support vector machine (SVM) to improve the low classification accuracies when a single feature extraction method is used. ResultsThis fusion algorithm achieves high accuracies and information transfer rates (ITRs) in the SSVEP paradigm with few targets. Comparison with existing methods and conclusionsThrough the study of 400 groups of experimental data from 10 subjects, the results show that CCA-CWT-SVM has a classification accuracy of 91.76% within 2 s and an ITR of 48.92 bits/min, which are 10.88% and 13.18 bits/min higher than those of the standard CCA. Compared with a mainstream EEG decoding algorithm, filter bank canonical correlation analysis (FBCCA), the classification accuracy and ITR of the CCA-CWT-SVM algorithm also improved (4.45% and 5.69 bit/min, respectively). Using a dataset from Tsinghua University (THU), we also showed that the fusion algorithm is better than the classical algorithms. The CCA-CWT-SVM algorithm obtained an 89.1% accuracy and a 39.91 bit/min ITR in a time window of 2 s. The results were significantly improved compared with those of CCA and the FBCCA (CCA: 79.44% and 28.23 bits/min, FBCCA: 84.03% and 33.4 bits/min). Hence, this work provides an experimental basis for designing an SSVEP-based BCI system with a high task classification accuracy in some crucial biomedical applications.

The Human—Unmanned Aerial Vehicle System Based on SSVEP—Brain Computer Interface

The brain–computer interface (BCI) is a mechanism for extracting information from the brain, with this information used for various applications. This study proposes a method to control an unmanned aerial vehicle (UAV) flying through a BCI system using the steady-state visual evoked potential (SSVEP) approach. The UAV’s screen emits three frequencies for visual stimulation: 15, 23, and 31 Hz for the UAV’s left-turn, forward-flight, and right-turn functions. Due to the requirement of immediate response to the UAV flight, this paper proposes a method to improve the accuracy rate and reduce the time required to correct instruction errors in the resolution of brainwave signals received by UAVs. This study tested ten subjects and verified that the proposed method has a 10% improvement inaccuracy. While the traditional method can take 8 s to correct an error, the proposed method requires only 1 s, making it more suitable for practical applications in UAVs. Furthermore, such a BCI application for UAV systems can achieve the same experience of using the remote control for physically challenged patients.

Single-Channel Selection for Detecting Steady-State Visual Evoked Potentials in a Brain-Computer Interface Speller

Purpose: Brain-Computer Interface (BCI) provides a secondary communication pathway for patients with neuromuscular diseases such as amyotrophic lateral sclerosis (ALS) or brainstem stroke in which they are almost incapacitated to move or talk. BCI enacts neural oscillations to generate a command signal for machines to operate desired tasks instead of patients. Steady-State Visual Evoked Potential (SSVEP) is the brain response to a visual stimulus, with the same frequency as its eliciting signal (or its harmonics), that has been widely used in BCI environments. In order to provide a more convenient situation for BCI users, we aim to find the best single-channel EEG, which results in the highest accuracy for detecting SSVEP. Materials and Methods: We developed a Deep Convolutional Neural Network with single-channel EEG as input to classify a 40-class SSVEP; each class represents a stimulus, which has been acquired from 35 subjects. We used 3.5 s windows of the data (Trials of 3.5 seconds length for each class) to train our model and leave-one-subject-out cross-validation for the testing. Results: The proposed method resulted in the average classification accuracy of 74.30%±20.85 and Information Transfer Rate (ITR) of 57.51 bpm which outperforms the previous single-channel SSVEP BCIs in terms of ITR. Also, the O1 channel achieved the best performance criteria among the channels in the occipital and parietal lobes, which seems reasonable according to previous researches for finding the location of neurons, responsible for visual tasks in the brain. Conclusion: In this study, we dedicated our efforts to reduce the number of EEG channels to a single channel while proposing a deep learning strategy for an SSVEP-based BCI speller to make it more feasible for patients whose lives are dependent on such systems. The overall results, although not ideal, open a new promising window toward a feasible BCI system.

SSVEP BCI and Eye Tracking Use by Individuals With Late-Stage ALS and Visual Impairments.

Access to communication is critical for individuals with late-stage amyotrophic lateral sclerosis (ALS) and minimal volitional movement, but they sometimes present with concomitant visual or ocular motility impairments that affect their performance with eye tracking or visual brain-computer interface (BCI) systems. In this study, we explored the use of modified eye tracking and steady state visual evoked potential (SSVEP) BCI, in combination with the Shuffle Speller typing interface, for this population. Two participants with late-stage ALS, visual impairments, and minimal volitional movement completed a single-case experimental research design comparing copy-spelling performance with three different typing systems: (1) commercially available eye tracking communication software, (2) Shuffle Speller with modified eye tracking, and (3) Shuffle Speller with SSVEP BCI. Participant 1 was unable to type any correct characters with the commercial system, but achieved accuracies of up to 50% with Shuffle Speller eye tracking and 89% with Shuffle Speller BCI. Participant 2 also had higher maximum accuracies with Shuffle Speller, typing with up to 63% accuracy with eye tracking and 100% accuracy with BCI. However, participants’ typing accuracy for both Shuffle Speller conditions was highly variable, particularly in the BCI condition. Both the Shuffle Speller interface and SSVEP BCI input show promise for improving typing performance for people with late-stage ALS. Further development of innovative BCI systems for this population is needed.

Validation of a brain-computer interface version of the digit symbol substitution test in healthy subjects

Digit symbol substitution test (DSST), which is a valid and sensitive tool to assess human cognitive dysfunction, has been widely used in clinical neuropsychology. Although several versions of DSST are currently available, most of the existing DSST versions rely on examinees' intact motor function. This limits their utility in severely motor-impaired individuals. A brain-computer interface (BCI) version of DSST was implemented in this study. Steady-state visual evoked potential (SSVEP) was adopted to build the BCI. Nine symbols in the proposed SSVEP BCI-based DSST were designed with clearly different shapes for decreasing measurement errors due to misidentified symbols. To reduce practice effect, furthermore, the digit-symbol pairs of each trial were different. A two-target SSVEP BCI was designed to judge whether the digit-symbol probe in the center of the user interface matched one of the nine digit-symbol pairs above the user interface. All 12 examinees were able to perform the tasks using the proposed SSVEP BCI-based DSST with 96.17 ± 4.18% averaged accuracy, which was comparable with that of computerized DSST. Furthermore, for examinees participating in both offline and online experiment, the accuracies of the online and offline experiments were comparable, supporting that the proposed BCI-DSST was reliable for repeatedly evaluating examinees’ cognitive function over time. These results verified that the proposed SSVEP BCI-based DSST was feasible and effective for healthy subjects.

Simultaneous Decoding of Eccentricity and Direction Information for a Single-Flicker SSVEP BCI

The feasibility of a steady-state visual evoked potential (SSVEP) brain–computer interface (BCI) with a single-flicker stimulus for multiple-target decoding has been demonstrated in a number of recent studies. The single-flicker BCIs have mainly employed the direction information for encoding the targets, i.e., different targets are placed at different spatial directions relative to the flicker stimulus. The present study explored whether visual eccentricity information can also be used to encode targets for the purpose of increasing the number of targets in the single-flicker BCIs. A total number of 16 targets were encoded, placed at eight spatial directions, and two eccentricities (2.5° and 5°) relative to a 12 Hz flicker stimulus. Whereas distinct SSVEP topographies were elicited when participants gazed at targets of different directions, targets of different eccentricities were mainly represented by different signal-to-noise ratios (SNRs). Using a canonical correlation analysis-based classification algorithm, simultaneous decoding of both direction and eccentricity information was achieved, with an offline 16-class accuracy of 66.8 ± 16.4% averaged over 12 participants and a best individual accuracy of 90.0%. Our results demonstrate a single-flicker BCI with a substantially increased target number towards practical applications.

Design of Wearable Headset with Steady State Visually Evoked Potential-Based Brain Computer Interface

Brain–computer interface (BCI) is a system that allows people to communicate directly with external machines via recognizing brain activities without manual operation. However, for most current BCI systems, conventional electroencephalography (EEG) machines and computers are usually required to acquire EEG signal and translate them into control commands, respectively. The sizes of the above machines are usually large, and this increases the limitation for daily applications. Moreover, conventional EEG electrodes also require conductive gels to improve the EEG signal quality. This causes discomfort and inconvenience of use, while the conductive gels may also encounter the problem of drying out during prolonged measurements. In order to improve the above issues, a wearable headset with steady-state visually evoked potential (SSVEP)-based BCI is proposed in this study. Active dry electrodes were designed and implemented to acquire a good EEG signal quality without conductive gels from the hairy site. The SSVEP BCI algorithm was also implemented into the designed field-programmable gate array (FPGA)-based BCI module to translate SSVEP signals into control commands in real time. Moreover, a commercial tablet was used as the visual stimulus device to provide graphic control icons. The whole system was designed as a wearable device to improve convenience of use in daily life, and it could acquire and translate EEG signal directly in the front-end headset. Finally, the performance of the proposed system was validated, and the results showed that it had excellent performance (information transfer rate = 36.08 bits/min).

Error Correction Regression Framework for Enhancing the Decoding Accuracies of Ear-EEG Brain-Computer Interfaces.

Ear-electroencephalography (EEG) is a promising tool for practical brain-computer interface (BCI) applications because it is more unobtrusive, comfortable, and mobile than a typical scalp-EEG system. However, an ear-EEG has a natural constraint of electrode location (e.g., limited in or around the ear) for acquiring informative brain signals sufficiently. Achieving reliable performance of ear-EEG in specific BCI paradigms that do not utilize brain signals on the temporal lobe around the ear is difficult. For example, steady-state visual evoked potentials (SSVEPs), which are mainly generated in the occipital area, have a significantly attenuated and distorted amplitude in ear-EEG. Therefore, preserving the high level of decoding accuracy is challenging and essential for SSVEP BCI based on ear-EEG. In this paper, we first investigate linear and nonlinear regression methods to increase the decoding accuracy of ear-EEG regarding SSVEP paradigm by utilizing the estimated target EEG signals on the occipital area. Then, we investigate an ensemble method to consider the prediction variability of the regression methods. Finally, we propose an error correction regression (ECR) framework to reduce the prediction errors by adding an additional nonlinear regression process (i.e., kernel ridge regression). We evaluate the ECR framework in terms of single session, session-to-session transfer, and subject-transfer decoding. We also validate the online decoding ability of the proposed framework with a short-time window size. The average accuracies are observed to be 91.11±9.14%, 90.52±8.67%, 86.96±12.13%, and 78.79±12.59%. This paper demonstrates that SSVEP BCI based on ear-EEG can achieve reliable performance with the proposed ECR framework.

A two-step idle-state detection method for SSVEP BCI.

The role of detecting work/idle state in asynchronous Steady-state visual evoked potential (SSVEP) Brain-computer interface (BCI) or a self-paced SSVEP BCI has received increased attention in recent years. This study proposed a tree structure method which identifies the work/idle state based on the frequency recognition to detect work/idle state. Firstly, a frequency recognition estimated with task-related component analysis (TRCA). Then, the work/idle state is classified with step-wise linear discriminant analysis (SWLDA) using the data fusion of TRCA scores and power spectral density (PSD) as features. This method was evaluated by Electroencephalography (EEG) data from fourteen healthy participants with eight frequencies as work states and three idle state conditions. The averaged AUC of this method achieved 0.89 with data lengths of one second, which was significantly higher than that of the conventional power spectrum-based algorithm. The proposed method could identify the work/idle state fast and accurately, making the SSVEP BCI better suited for practical application.