Phase-locked Time-shift Data Augmentation Method for SSVEP Brain-Computer Interfaces.

Steady state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has gained a lot of attention due to its robustness and high information transfer rate (ITR). However, transitioning well-controlled laboratory-oriented BCI demonstrations to real-world applications poses severe challenges for this exciting field. For instance, conducting BCI experiments usually requires skilled technicians to abrade the area of skin underneath each electrode and apply an electrolytic gel or paste to acquire high-quality SSVEPs from hair-covered areas. Our previous proof-of-concept study has proposed an alternative approach that employed electroencephalographic signals collected from easily accessible non-hair-bearing areas including neck, behind the ears, and face to realize an SSVEP-based BCI. The study results showed that, with proper electrode placements and advanced signal-processing algorithms, the SSVEPs measured from non-hair-bearing areas in off-line SSVEP experiments could achieve comparable SNR to that obtained from the hair-bearing occipital areas. This study extended the previous work to systematically investigate the costs and benefits of non-hair SSVEPs. Furthermore, this study developed and evaluated an online BCI system based solely on non-hair EEG signals. A 12-target identification task was employed to quantitatively assess the performance of the online SSVEP-based BCI system. All subjects successfully completed the tasks using non-hair SSVEPs with 84.08 ± 15.60% averaged accuracy and 30.21 ± 10.61 bits/min averaged ITR. The empirical results of this study demonstrated the practicality of implementing an SSVEP-based BCI based on signals from non-hair-bearing areas, significantly improving the feasibility and practicality of real-world BCIs.

This paper presents an electroencephalogram (EEG) based brain computer interface (BCI) design as a control mechanism for the Open Vibe Space shooter game. BCI using Steady State Visual Evoked Potential (SSVEP) is designed. The SSVEP is generated in response to a flickering visual stimulus. The setup uses different flickering frequencies which are displayed on a Liquid Crystal Display (LCD) monitor to elicit SSVEP responses. The EEG signals are recorded over the visual cortex of the brain. These signals are pre-processed to remove undesired information. To extract features, the power information contained in the corresponding frequency bands are calculated. The feature set is fed as an input to the classifier to identify the target class. This study tried to compare the performance of different classification algorithms in terms of accuracy, information transfer rate and the response time. Based on the outcome, support vector machine classifier with radial basis function recorded the best average accuracy of 90%.

Objective. Brain–computer interface (BCI) technologies have been intensely studied to provide alternative communication tools entirely independent of neuromuscular activities. Current BCI technologies use electroencephalogram (EEG) acquisition methods that require unpleasant gel injections, impractical preparations and clean-up procedures. The next generation of BCI technologies requires practical, user-friendly, nonintrusive EEG platforms in order to facilitate the application of laboratory work in real-world settings. Approach. A capacitive electrode that does not require an electrolytic gel or direct electrode–scalp contact is a potential alternative to the conventional wet electrode in future BCI systems. We have proposed a new capacitive EEG electrode that contains a conductive polymer-sensing surface, which enhances electrode performance. This paper presents results from five subjects who exhibited visual or auditory steady-state responses according to BCI using these new capacitive electrodes. The steady-state visual evoked potential (SSVEP) spelling system and the auditory steady-state response (ASSR) binary decision system were employed. Main results. Offline tests demonstrated BCI performance high enough to be used in a BCI system (accuracy: 95.2%, ITR: 19.91 bpm for SSVEP BCI (6 s), accuracy: 82.6%, ITR: 1.48 bpm for ASSR BCI (14 s)) with the analysis time being slightly longer than that when wet electrodes were employed with the same BCI system (accuracy: 91.2%, ITR: 25.79 bpm for SSVEP BCI (4 s), accuracy: 81.3%, ITR: 1.57 bpm for ASSR BCI (12 s)). Subjects performed online BCI under the SSVEP paradigm in copy spelling mode and under the ASSR paradigm in selective attention mode with a mean information transfer rate (ITR) of 17.78 ± 2.08 and 0.7 ± 0.24 bpm, respectively. Significance. The results of these experiments demonstrate the feasibility of using our capacitive EEG electrode in BCI systems. This capacitive electrode may become a flexible and non-intrusive tool fit for various applications in the next generation of BCI technologies.

To enhance the information transfer rate (ITR) of a steady-state visual evoked potential (SSVEP)-based speller, more characters with flickering symbols should be used. Increasing the number of symbols might reduce the classification accuracy. A hybrid brain-computer interface (BCI) improves the overall performance of a BCI system by taking advantage of two or more control signals. In a simultaneous hybrid BCI, various modalities work with each other simultaneously, which enhances the ITR. In our proposed speller, simultaneous combination of electromyogram (EMG) and SSVEP was applied to increase the ITR. To achieve 36 characters, only nine stimulus symbols were used. Each symbol allowed the selection of four characters based on four states of muscle activity. The SSVEP detected which symbol the subject was focusing on and the EMG determined the target character out of the four characters dedicated to that symbol. The frequency rate for character encoding was applied in the EMG modality and latency was considered in the SSVEP modality. Online experiments were carried out on 10 healthy subjects. The average ITR of this hybrid system was 96.1 bit/min with an accuracy of 91.2%. The speller speed was 20.9 char/min. Different subjects had various latency values. We used an average latency of 0.2 s across all subjects. Evaluation of each modality showed that the SSVEP classification accuracy varied for different subjects, ranging from 80% to 100%, while the EMG classification accuracy was approximately 100% for all subjects. Our proposed hybrid BCI speller showed improved system speed compared with state-of-the-art systems based on SSVEP or SSVEP-EMG, and can provide a user-friendly, practical system for speller applications.

Electroencephalogram (EEG) based brain-computer interfaces (BCIs) for wheelchair control have great value for those with devastating neuromuscular disorders. Although there have been many attempts to implement EEG-based wheelchair control systems by P300, steady state visual evoked potential (SSVEP), and motor imagery (MI) related event-related desynchronization/synchronization (ERD/ERS), the number of simultaneous control commands in those BCI systems is strictly limited, and those BCI control do not work for a non-negligible portion of users due to the problem of BCI Illiteracy.In this paper, we develop a multimodal BCI based wheelchair control system, the user could employ subject-optimized mental strategies to produce multiple commands to control the wheelchair, which include ERD/ERS, SSVEP, and simultaneous ERD/ERS and SSVEP. It could not only help address ”BCI illiteracy”, but also provide simultaneous control commands for complex control. Experiment results demonstrate the proposed system is effective and flexible in practical application.KeywordsBCIwheelchair controlmultimodalBCI Illiteracy

Brain-computer interface (BCI) systems translate the human neurophysiological activities into commands through EEG analysis. Improving the BCI performances leads to faster and easier use and less fatigue. In this study, we proposed a new prepossessing approach to increase the robustness of a steady-state visual evoked potential (SSVEP) based BCI. Inspiring from the known properties of the SSVEP frequency components, the goal was to enhance the signal quality by making it more convenient to be interpreted by the decision-making step. We first investigated the potential to detect the deteriorating periods based on the physiological properties of the SSVEP. The proposed system localizes the intervals which can obscure the SSVEP frequencies by a new algorithm founded on the processing and the analysis of the instantaneous phase. The piecewise linear regression allows a sampler comprehension of the phase signal. Then, these intervals are filtered by the moving average filter to enhance the SSVEP quality. Finally, the decision making is made by the canonical correlation analysis (CCA) algorithm. The results of experiments, using real EEG signals from five subjects, show that the proposed approach significantly increases the performances in terms of accuracy and information transfer rate by about 7.3% and 3.85 bits/min, respectively, in case of 2 s segment length. On the other hand, the spatial filtering methods of the literature weaken the system performances.

Cognitive assessment is of growing importance, with the general population getting older and a rapidly growing incidence of dementia, which is a major public health issue. Treatment of dementia must, to be most effective, start early in the disease process. Thus, early detection of cognitive decline is important. Cognitive decline may be detected using fully-automated computerized assessment. Such systems will provide inexpensive and widely available screenings of cognitive ability. The aim of this pilot study is to develop a real time steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) for neurological cognitive assessment. It is intended for use by patients who suffer from diseases impairing their motor skills, but are still able to control their gaze. Results are based on 11 healthy test subjects. The system performance have an average accuracy of 100% - 0%. The test subjects achieved an information transfer rate (ITR) of 14.64 bits/min - 7.63 bits/min and a subject test performance of 47.22% - 34.10%. This study suggests that BCI may be applicable in practice as a computerized cognitive assessment tool. However, many improvements are required for the system to be fully valid and of clinical use.

This study proposes a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) independent of amplitude-frequency and phase calibrations. Six stepping delay flickering sequences (SDFSs) at 32-Hz flickering frequency were used to implement a six-command BCI system. EEG signals recorded from Oz position were first filtered within 29-35 Hz, segmented based on trigger events of SDFSs to obtain SDFS epochs, and then stored separately in epoch registers. An epoch-average process suppressed the inter-SDFS interference. For each detection point, the latest six SDFS epochs in each epoch register were averaged and the normalized power of averaged responses was calculated. The visual target that induced the maximum normalized power was identified as the visual target. Eight subjects were recruited in this study. All subjects were requested to produce the "563241" command sequence four times. The averaged accuracy, command transfer interval, and information transfer rate (mean ± std.) values for all eight subjects were 97.38 ± 5.97%, 3.56 ± 0.68 s, and 42.46 ± 11.17 bits/min, respectively. The proposed system requires no calibration in either the amplitude-frequency characteristic or the reference phase of SSVEP which may provide an efficient and reliable channel for the neuromuscular disabled to communicate with external environments.

In this paper, to develop a hybrid brain-computer interface based on Steady State Visual Evoked Potential(SSVEP) and P300, we validate a SSVEP brain-computer interface (BCI) with the hybrid stimuli of SSVEP and P300, and analyze the effect of P300 visual stimuli on the classification accuracy of the SSVEP BCI. The hybrid stimuli include the SSVEP stimuli, which consist of twochessboardimagespresented on a LCD screen and flashing at particular frequencies, respectively, and the P300 stimuli, which include nine characters and locate between the SSVEP stimuli. We extract the fundamental frequencies and second harmonics of the frequencies of the SSVEP stimulifrom EEG data as features, andapply a Fisher's linear discriminant analysis to build the classifier. The experimental results from three subjects show that the SSVEP potentials can be elicited well under the hybrid stimuli of SSVEP and P300, and the P300 stimuli under the hybrid condition have no significant effect on the SSVEP BCI. This work laysa foundationfor developing a hybrid BCIbased on SSVEP and P300.

This paper presents the major challenges and solutions for the Brain Computer Interfaces (BCIs), which are based on Steady State Visual Evoked Potentials (SSVEPs). A BCI utilizes the information transmitted by the brain; there are several methods available for analyzing these brain activities. One of the major challenges in BCI is to remove the noise successfully. Averaging along with Wavelets has been proposed in this paper for de-noising the brain activities. Moreover, three different approaches have been investigated to estimate the frequency spectrum of the brain signals. In addition to the well known Fourier Transform (FT) technique, for spectral estimation, there are many parametric and non parametric techniques for computing the frequency content of a signal. This paper presents a comparison between Fast Fourier Transform (FFT), MUltiple SIgnal Classification (MUSIC) and Linear Predictive Coding (LPC). The effectiveness of different techniques has been studied and the simulation results have shown that MUSIC outperforms the other approaches.

The performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has been drastically improved in the past few years. In conventional SSVEP-based BCIs, the speed of a selection is fixed towards high performance based on preliminary offline analysis. However, due to inter-trial variability, the optimal selection time to achieve sufficient accuracy is different for each trial. To optimize the performance of SSVEP-based BCIs, this study proposed a dynamic stopping method that can adaptively determine a selection time in each trial by applying a threshold to the probability of detecting a target. A 12-class SSVEP dataset recorded from 10 subjects was used to evaluate the performance of the proposed method. Compared to the conventional method with a fixed selection time towards the highest accuracy, the proposed method could significantly reduce the averaged selection time (0.84±0.39 s vs. 1.44±0.63 s, p<;0.05) with comparable accuracy (99.44±1.57 % vs. 99.55±1.22 %). As a result, the simulated online information transfer rate (ITR) with the dynamic stopping method achieved a significant improvement compared to the conventional method (125.30±21.55 bits/min vs. 92.75±23.77 bits/min). These results suggest that the proposed dynamic stopping method is effective for improving the performance of SSVEP-based BCI systems.

A crucial problem for the overall performance of steady-state visual evoked potentials (SSVEP)-based brain computer interface (BCIs) is the right choice of the time-window length since a large window results in a higher accuracy but longer detection time, making the system impractical. This paper proposes an adaptive time window length to improve the system performance based on the subject’s online performance. However, since there is no known methods of assessing the online performance in real time, it is also proposed a feedback from the user, through a speller, for the system to know whether the output is correct or not and change or maintain the time-window length accordantly. The system was implemented fully online and tested in 4 subjects. The subjects have attained an average information transfer rate (ITR) of 62.09bit/min and standard deviation of 2.13bit/min with a mean accuracy of 99.00% and standard deviation of 1.15%, which represents an improvement of about 6.50% of the ITR to the fixed time-window length system.Keywordsbrain-computer interface (BCI)steady-state visual evoked potentials (SSVEP)adaptive time-window lengthonline performance assessment

Purpose: Brain-Computer Interface (BCI) Speller systems help people with mobility impairments improve their cognitive and physical abilities. Steady-State Visual Evoked Potential (SSVEP) signals have been used to build high-speed BCI speller systems. SSVEP signals are a subtype of Visual Evoked Potential (VEP), a form of co-frequency, and the harmonics response elicited by a specific frequency stimulus. Noise and artifacts are critical issues for target detection in SSVEP-based BCI systems.&#x0D; Materials and Methods: Thus, it is essential to provide target detection techniques that operate well in the presence of noises. One solution for overcoming the noise impact is to employ approaches that automatically extract the appropriate features for target detection from the training data. Deep Convolutional Neural Network (DCNN) was utilized in this study to automatically extract features from SSVEP data in noisy conditions. Moreover, the BETA database, which contains SSVEP data from 70 individuals collected outside of the electromagnetic shielding room, was used. In this regard, a suitable DCNN structure for target stimulus frequency identification was first designed. The network was pre-trained with part of the data from the BETA database. Finally, at the single-subject level, this pre-trained network was retrained and evaluated.&#x0D; Results: The results showed that after retraining, the accuracy and Information Transfer Rate (ITR) increased (p-value &lt; 0.01) for all participants.&#x0D; Conclusion:.The enhancement in accuracy and ITR are 25.72% and 43.10 bpm, respectively.

The application of the brain-computer interface (BCI) is massively helpful and advantageous for disabled people. Moreover, BCI is an arrangement of software and hardware interface that provides a direct interaction between the human brain and computer devices. Therefore, in this article, A steady state visual evoked potential (SSVEP)-based BCI system is presented to identify SSVEP components from multi-channel electroencephalogram (EEG) data by minimizing background noise using an adaptive spatial filtering method. Here, the proposed adaptive spatial filtering-based SSVEP component extraction (ASFSCE) model improves reproducibility among multiple trails and identifies targets efficiently by optimizing the Eigenvalue problem. Along with that, the proposed ASFSCE model minimizes computational complexity from O(G&lt;sup&gt;2&lt;/sup&gt;) to to get high target identification accuracy with faster execution. Performance results are measured using the SSVEP dataset. In this dataset, 11 subjects are used to perform experiments and 256-channel EEG data is taken. The efficiency of the proposed ASFSCE model is measured in terms of mean target detection accuracy and mean information transfer rate (ITR) in bits per minute. The average detection accuracy and ITR are evaluated by considering 23 trials for each subject. The obtained detection accuracy is 93.47% and ITR is 308.23 bpm.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to realize high-speed communication between the human brain and the external environment. Recently, multiple access (MA) methods in telecommunications have been introduced into the system design of BCIs and showed their potential in improving BCI performance. This study investigated the feasibility of hybrid frequency and phase coding methods in multi-target SSVEP-based BCIs. Specifically, this study compared two hybrid target-coding strategies: (1) mixed frequency and phase coding, and (2) joint frequency and phase coding. In a simulated online BCI experiment using a 40-target BCI speller, BCI performance for both coding approaches were tested with a group of six subjects. At a spelling speed of 40 characters per minute (1.5 seconds per character), both approaches obtained high information transfer rates (ITR) (mixed coding: 172.37±28.67 bits/min, joint coding: 170.94±28.32 bits/min) across subjects. There was no statistically significant difference between the two approaches (p>0.05). These results suggest that the hybrid frequency and phase coding methods are highly efficient for multi-target coding in SSVEP BCIs with a large number of classes, providing a practical solution to implement a high-speed BCI speller.