Steady-state Visual Evoked Potential Based Brain-computer Interfaces Research Articles

Enhancing cross-subject transfer performance for SSVEP identification using small data-based transferability evaluation

Cross-subject steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) have attracted increasing attention in recent years due to their potential to reduce calibration efforts and improve usability. This study proposed an efficient cross-subject transfer method for SSVEP-BCI, which significantly reduced the calibration effort and enhanced system performance. By employing a minimal dataset obtained from the target subject, specifically, a single trial for each frequency, we calculated the similarity between the SSVEPs of the target subject and the source subjects. Based on the similarities, a transferability evaluation model was constructed, which was then used to assign weights to the source subjects. The features calculated from the source subjects were then weighted and summed to obtain the frequency recognition results. The proposed cross-subject transfer method was evaluated in two pipelines based on the ensemble task-related component analysis (eTRCA) and task-discriminant component analysis (TDCA), respectively. The proposed method in eTRCA-pipeline initially achieving accuracies of 96.98 ± 3.66 % and 88.97 ± 14.66 % in our dataset and the Benchmark dataset, respectively, at a data length of 1 s. After fusing the features of eTRCA with those of the extended canonical correlation analysis (eCCA), the method exhibited enhanced performance, elevating accuracies to 98.23 ± 2.68 % and 92.83 ± 10.75 %. Similarly, in TDCA-pipeline, accuracies initially reached 97.47 ± 3.88 % and 93.53 ± 10.22 % in our dataset and the Benchmark dataset, respectively, and after fusing TDCA features with eCCA features, they remarkably increased to 98.13 ± 2.89 % and 95.00 ± 8.43 %, respectively. This study offers a promising solution for the fast and effective calibration of SSVEP-BCI, paving the way for more practical and accessible BCI systems.

Comparison of recognition methods for an asynchronous (un-cued) BCI system: an investigation with 40-class SSVEP dataset.

Steady-state visual evoked potential (SSVEP)-based brain-computer Interface (BCI) has demonstrated the potential to manage multi-command targets to achieve high-speed communication. Recent studies on multi-class SSVEP-based BCI have focused on synchronous systems, which rely on predefined time and task indicators; thus, these systems that use passive approaches may be less suitable for practical applications. Asynchronous systems recognize the user's intention (whether or not the user is willing to use systems) from brain activity; then, after recognizing the user's willingness, they begin to operate by switching swiftly for real-time control. Consequently, various methodologies have been proposed to capture the user's intention. However, in-depth investigation of recognition methods in asynchronous BCI system is lacking. Thus, in this work, three recognition methods (power spectral density analysis, canonical correlation analysis (CCA), and support vector machine (SVM)) used widely in asynchronous SSVEP BCI systems were explored to compare their performance. Further, we categorized asynchronous systems into two approaches (1-stage and 2-stage) based upon the recognition process's design, and compared their performance. To do so, a 40-class SSVEP dataset collected from 40 subjects was introduced. Finally, we found that the CCA-based method in the 2-stage approach demonstrated statistically significantly higher performance with a sensitivity of 97.62±02.06%, specificity of 76.50±23.50%, and accuracy of 75.59±10.09%. Thus, it is expected that the 2-stage approach together with CCA-based recognition and FB-CCA classification have good potential to be implemented in practical asynchronous SSVEP BCI systems.

Deep transfer learning-based SSVEP frequency domain decoding method

ObjectiveImproving the decoding accuracy and information transfer rate (ITR) of a steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) system and narrowing the inter-subject variance are key to the application of the SSVEP-BCI system. To this end, we proposed a deep transfer learning-based SSVEP frequency domain decoding method to improve the decoding performance. MethodsInput data representations with rich spatial and frequency domain features were extracted using filter bank and zero-padding-based fast Fourier transform techniques. A concise and efficient 3-dimensional convolutional neural network (3DCNN) model was designed for feature extraction and decoding of the input data. A transfer learning strategy was proposed to further improve the decoding accuracy and narrow the inter-subject variance. ResultsOur proposed 3DCNN achieves 89.35 % average classification accuracy and 173.02 bits/min ITR on the benchmark dataset with 1 s signal length. On our laboratory dataset, the average classification accuracy and ITR of 3DCNN reach 88.75 % and 120.33 bits/min, respectively, when the signal length is 0.6 s. ConclusionsIn this study, we experimentally confirmed the effectiveness and superiority of our proposed method for SSVEP decoding, which provides a promising decoding method for the application of SSVEP-BCI.

A high-frequency SSVEP-BCI system based on a 360 Hz refresh rate.

Objective. Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) often struggle to balance user experience and system performance. To address this challenge, this study employed stimuli in the 55-62.8 Hz frequency range to implement a 40-target BCI speller that offered both high-performance and user-friendliness.Approach. This study proposed a method that presents stable multi-target stimuli on a monitor with a 360 Hz refresh rate. Real-time generation of stimulus matrix and stimulus rendering was used to ensure stable presentation while reducing the computational load. The 40 targets were encoded using the joint frequency and phase modulation method, offline and online BCI experiments were conducted on 16 subjects using the task discriminant component analysis algorithm for feature extraction and classification.Main results. The online BCI system achieved an average accuracy of 88.87% ± 3.05% and an information transfer rate of 51.83 ± 2.77 bits min-1under the low flickering perception condition.Significance. These findings suggest the feasibility and significant practical value of the proposed high-frequency SSVEP BCI system in advancing the visual BCI technology.

A Canonical Correlation Analysis-Based Transfer Learning Framework for Enhancing the Performance of SSVEP-Based BCIs.

A steady-state visual evoked potential (SSVEP)- based brain-computer interface (BCI) can either achieve high classification accuracy in the case of sufficient training data or suppress the training stage at the cost of low accuracy. Although some researches attempted to conquer the dilemma between performance and practicality, a highly effective approach has not yet been established. In this paper, we propose a canonical correlation analysis (CCA)-based transfer learning framework for improving the performance of an SSVEP BCI and reducing its calibration effort. Three spatial filters are optimized by a CCA algorithm with intra- and inter-subject EEG data (IISCCA), two template signals are estimated separately with the EEG data from the target subject and a set of source subjects and six coefficients are yielded by correlation analysis between a testing signal and each of the two templates after they are filtered by each of the three spatial filters. The feature signal used for classification is extracted by the sum of squared coefficients multiplied by their signs and the frequency of the testing signal is recognized by template matching. To reduce the individual discrepancy between subjects, an accuracy-based subject selection (ASS) algorithm is developed for screening those source subjects whose EEG data are more similar to those of the target subject. The proposed ASS-IISCCA integrates both subject-specific models and subject-independent information for the frequency recognition of SSVEP signals. The performance of ASS-IISCCA was evaluated on a benchmark data set with 35 subjects and compared with the state-of-the-art algorithm task-related component analysis (TRCA). The results show that ASS-IISCCA can significantly improve the performance of SSVEP BCIs with a small number of training trials from a new user, thus helping to facilitate their applications in real world.

Comparison of Deep Learning and Traditional Machine Learning Classification Performance in a SSVEP Based Brain Computer Interface

Brain-computer interfaces (BCIs) offer a very high potential to help those who cannot use their organs properly. In the literature, many electroencephalogram based BCIs exist. Steady state visual evoked potential (SSVEP) based BCIs provide relatively higher accuracy values which make them very popular in BCI research. Recently, deep learning (DL) based methods have been used in electroencephalogram classification problems and they had superior performance over traditional machine learning (ML) methods, which require feature extraction step. This study aimed at comparing the performance of DL and traditional ML based classification performance in terms of stimuli duration, number of channels, and number of trials in an SSVEP based BCI experiment. In the traditional approach canonical correlation analysis method was used for the feature extraction and then three well-known classifiers were used for classification. In DL-based classification, spatio-spectral decomposition (SSD) method was integrated as a preprocessing step to extract oscillatory signals in the frequency band of interest with a convolutional neural network structure. Obtained offline classification results show that proposed DL approach could generate better accuracy values than traditional ML-based methods for short time segments (< 1 s). Besides, use of SSD as a preprocessing step increased the accuracy of DL classification. Superior performance of proposed SSD based DL approach over the traditional ML methods in short trials shows the feasibility of this approach in future BCI designs. Similar approach can be used in other fields where there are oscillatory activity in the recorded signals.

Cross-subject fusion based on time-weighting canonical correlation analysis in SSVEP-BCIs

Brain–computer interface technology provides new possibilities for medical rehabilitation and human–computer interaction. The steady-state visual evoked potential based brain–computer interface (SSVEP-BCI) is the preferred solution for controlling equipment because of its ease of operation, low training requirements, and high information transfer rate (ITR). However, the low recognition accuracy of SSVEP in short time limits its further improvement in ITR. To address this issue, this paper proposes time-weighting canonical correlation analysis (TWCCA), a time-domain enhancement CCA-based method to improve the recognition accuracy of SSVEP in short recognition time. The TWCCA method evaluates the SSVEP components of EEG signals at different time periods and performs a time-dimension weighted CCA on the original signal. The method integrates the features of all stimulus targets based on CCA and is insensitive to the specific number of targets, so it does not require a large amount of calibration data. To further shorten the calibration time for a certain user, this paper also proposes a cross-subject fusion method to calibrate the parameters of the target user using the acquired data from other users. The effectiveness of the proposed method is evaluated using EEG signals from the occipital and ear regions. The experiment results show that the TWCCA method significantly improves the average recognition accuracy by 3.86% and the cross-subject fusion method shortens the calibration time by 78.5% without compromising recognition accuracy. The proposed combination of cross-subject fusion method and TWCCA (CF-TWCCA) increases recognition accuracy by 13.16% on average (p<0.001).

EldBETA: A Large Eldercare-oriented Benchmark Database of SSVEP-BCI for the Aging Population

Global population aging poses an unprecedented challenge and calls for a rising effort in eldercare and healthcare. Steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) boasts its high transfer rate and shows great promise in real-world applications to support aging. Public database is critically important for designing the SSVEP-BCI systems. However, the SSVEP-BCI database tailored for the elder is scarce in existing studies. Therefore, in this study, we present a large eldercare-oriented BEnchmark database of SSVEP-BCI for The Aging population (eldBETA). The eldBETA database consisted of the 64-channel electroencephalogram (EEG) from 100 elder participants, each of whom performed seven blocks of 9-target SSVEP-BCI task. The quality and characteristics of the eldBETA database were validated by a series of analyses followed by a classification analysis of thirteen frequency recognition methods. We expect that the eldBETA database would provide a substrate for the design and optimization of the BCI systems intended for the elders. The eldBETA database is open-access for research and can be downloaded from the website https://doi.org/10.6084/m9.figshare.18032669.

The effect of stimulus number on the recognition accuracy and information transfer rate of SSVEP–BCI in augmented reality

Objective. The biggest advantage of steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) lies in its large command set and high information transfer rate (ITR). Almost all current SSVEP–BCIs use a computer screen (CS) to present flickering visual stimuli, which limits its flexible use in actual scenes. Augmented reality (AR) technology provides the ability to superimpose visual stimuli on the real world, and it considerably expands the application scenarios of SSVEP–BCI. However, whether the advantages of SSVEP–BCI can be maintained when moving the visual stimuli to AR glasses is not known. This study investigated the effects of the stimulus number for SSVEP–BCI in an AR context. Approach. We designed SSVEP flickering stimulation interfaces with four different numbers of stimulus targets and put them in AR glasses and a CS to display. Three common recognition algorithms were used to analyze the influence of the stimulus number and stimulation time on the recognition accuracy and ITR of AR–SSVEP and CS–SSVEP. Main results. The amplitude spectrum and signal-to-noise ratio of AR–SSVEP were not significantly different from CS–SSVEP at the fundamental frequency but were significantly lower than CS–SSVEP at the second harmonic. SSVEP recognition accuracy decreased as the stimulus number increased in AR–SSVEP but not in CS–SSVEP. When the stimulus number increased, the maximum ITR of CS–SSVEP also increased, but not for AR–SSVEP. When the stimulus number was 25, the maximum ITR (142.05 bits min−1) was reached at 400 ms. The importance of stimulation time in SSVEP was confirmed. When the stimulation time became longer, the recognition accuracy of both AR–SSVEP and CS–SSVEP increased. The peak value was reached at 3 s. The ITR increased first and then slowly decreased after reaching the peak value. Significance. Our study indicates that the conclusions based on CS–SSVEP cannot be simply applied to AR–SSVEP, and it is not advisable to set too many stimulus targets in the AR display device.

Novel Hybrid Brain-Computer Interface for Virtual Reality Applications Using Steady-State Visual-Evoked Potential-Based Brain-Computer Interface and Electrooculogram-Based Eye Tracking for Increased Information Transfer Rate.

Brain–computer interfaces (BCIs) based on electroencephalogram (EEG) have recently attracted increasing attention in virtual reality (VR) applications as a promising tool for controlling virtual objects or generating commands in a “hands-free” manner. Video-oculography (VOG) has been frequently used as a tool to improve BCI performance by identifying the gaze location on the screen, however, current VOG devices are generally too expensive to be embedded in practical low-cost VR head-mounted display (HMD) systems. In this study, we proposed a novel calibration-free hybrid BCI system combining steady-state visual-evoked potential (SSVEP)-based BCI and electrooculogram (EOG)-based eye tracking to increase the information transfer rate (ITR) of a nine-target SSVEP-based BCI in VR environment. Experiments were repeated on three different frequency configurations of pattern-reversal checkerboard stimuli arranged in a 3 × 3 matrix. When a user was staring at one of the nine visual stimuli, the column containing the target stimulus was first identified based on the user’s horizontal eye movement direction (left, middle, or right) classified using horizontal EOG recorded from a pair of electrodes that can be readily incorporated with any existing VR-HMD systems. Note that the EOG can be recorded using the same amplifier for recording SSVEP, unlike the VOG system. Then, the target visual stimulus was identified among the three visual stimuli vertically arranged in the selected column using the extension of multivariate synchronization index (EMSI) algorithm, one of the widely used SSVEP detection algorithms. In our experiments with 20 participants wearing a commercial VR-HMD system, it was shown that both the accuracy and ITR of the proposed hybrid BCI were significantly increased compared to those of the traditional SSVEP-based BCI in VR environment.

Align and Pool for EEG Headset Domain Adaptation (ALPHA) to Facilitate Dry Electrode Based SSVEP-BCI.

The steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) implemented in dry electrodes is a promising paradigm for alternative and augmentative communication in real-world applications. To improve its performance and reduce the calibration effort for dry-electrode systems, we utilize cross-device transfer learning by exploiting auxiliary individual wet-electrode electroencephalogram (EEG). We proposed a novel transfer learning framework named ALign and Pool for EEG Headset domain Adaptation (ALPHA), which aligns the spatial pattern and the covariance for domain adaptation. To evaluate its efficacy, 75 subjects performed an experiment of 2 sessions involving a 12-target SSVEP-BCI task. ALPHA significantly outperformed a baseline approach (canonical correlation analysis, CCA) and two competing transfer learning approaches (transfer template CCA, ttCCA and least square transformation, LST) in two transfer directions. When transferring from wet to dry EEG headsets, ALPHA significantly outperformed the fully-calibrated approach of task-related component analysis (TRCA). ALPHA advances the frontier of recalibration-free cross-device transfer learning for SSVEP-BCIs and boosts the performance of dry electrode based systems. ALPHA has methodological and practical implications and pushes the boundary of dry electrode based SSVEP-BCI toward real-world applications.

A Spectrally-Dense Encoding Method for Designing a High-Speed SSVEP-BCI With 120 Stimuli.

The practical functionality of a brain-computer interface (BCI) is critically affected by the number of stimuli, especially for steady-state visual evoked potential based BCI (SSVEP-BCI), which shows promise for the implementation of a multi-target system for real-world applications. Joint frequency-phase modulation (JFPM) is an effective and widely used method in modulating SSVEPs. However, the ability of JFPM to implement an SSVEP-BCI system with a large number of stimuli, e.g., over 100 stimuli, remains unclear. To address this issue, a spectrally-dense JPFM (sJFPM) method is proposed to encode a broad array of stimuli, which modulates the low- and medium-frequency SSVEPs with a frequency interval of 0.1 Hz and triples the number of stimuli in conventional SSVEP-BCI to 120. To validate the effectiveness of the proposed 120-target BCI system, an offline experiment and a subsequent online experiment testing 18 healthy subjects in total were conducted. The offline experiment verified the feasibility of using sJFPM in designing an SSVEP-BCI system with 120 stimuli. Furthermore, the online experiment demonstrated that the proposed system achieved an average performance of 92.47±1.83% in online accuracy and 213.23±6.60 bits/min in online information transfer rate (ITR), where more than 75% of the subjects attained the accuracy above 90% and the ITR above 200 bits/min. This present study demonstrates the effectiveness of sJFPM in elevating the number of stimuli to more than 100 and extends our understanding of encoding a large number of stimuli by means of finer frequency division.

Implementing a calibration-free SSVEP-based BCI system with 160 targets

Objective. Steady-state visual evoked potential (SSVEP) is an essential paradigm of electroencephalogram based brain–computer interface (BCI). Previous studies in the BCI research field mostly focused on enhancing classification accuracy and reducing stimuli duration. This study, however, concentrated on increasing the number of available targets in the BCI systems without calibration. Approach. Motivated by the idea of multiple frequency sequential coding, we developed a calibration-free SSVEP–BCI system implementing 160 targets by four continuous sinusoidal stimuli that lasted four seconds in total. Taking advantage of the benchmark dataset of SSVEP–BCI, this study optimized an arrangement of stimuli sequences, maximizing the response distance between different stimuli. We proposed an effective classification algorithm based on filter bank canonical correlation analysis. To evaluate the performance of this system, we conducted offline and online experiments using cue-guided selection tasks. Eight subjects participated in the offline experiments, and 12 subjects participated in the online experiments with real-time feedbacks. Main results. Offline experiments indicated the feasibility of the stimulation selection and detection algorithms. Furthermore, the online system achieved an average accuracy of 87.16 ± 11.46% and an information transfer rate of 78.84 ± 15.59 bits min−1. Specifically, seven of 12 subjects accomplished online experiments with accuracy higher than 90%. This study proposed an intact solution of applying numerous targets to SSVEP-based BCIs. Results of experiments confirmed the utility and efficiency of the system. Significance. This study firstly provides a calibration-free SSVEP–BCI speller system that enables more than 100 commands. This system could significantly expand the application scenario of SSVEP-based BCI. Meanwhile, the design criterion can hopefully enhance the overall performance of the BCI system. The demo video can be found in the supplementary material available online at stacks.iop.org/JNE/18/046094/mmedia.

Improving the Performance of Individually Calibrated SSVEP-BCI by Task- Discriminant Component Analysis.

A brain-computer interface (BCI) provides a direct communication channel between a brain and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has received increasing attention due to its high information transfer rate, which is accomplished by individual calibration for frequency recognition. Task-related component analysis (TRCA) is a recent and state-of-the-art method for individually calibrated SSVEP-BCIs. However, in TRCA, the spatial filter learned from each stimulus may be redundant and temporal information is not fully utilized. To address this issue, this paper proposes a novel method, i.e., task-discriminant component analysis (TDCA), to further improve the performance of individually calibrated SSVEP-BCI. The performance of TDCA was evaluated by two publicly available benchmark datasets, and the results demonstrated that TDCA outperformed ensemble TRCA and other competing methods by a significant margin. An offline and online experiment testing 12 subjects further validated the effectiveness of TDCA. The present study provides a new perspective for designing decoding methods in individually calibrated SSVEP-BCI and presents insight for its implementation in high-speed brain speller applications.

Frequency Detection of Single Channel Steady State Visual Evoked Potential using Canonical Correlation Analysis

Wave generated into visual cortex of brain, when subject focused his/her attention on visual stimulus flickers at certain frequency. The main challenge with SSVEP Based Brain computer interface (BCI) System is to detect the stimulus frequency from recorded brain signal. Canonical Correlation analysis (CCA) is one of the most popular methods to recognize the frequency of Steady state visual evoked potential (SSVEP). This paper focuses on the study of CCA algorithm to recognize the SSVEP signal frequency. For experiment purpose, a single channel data with flickering frequency in the range of (6Hz-10Hz) is used. The performance of the BCI System is measured in terms of detection accuracy and Information transmission rate (ITR). The maximum accuracy is obtained as 83.90% and ITR is 15.35 at stimulus frequency of 8.2Hz

Enhancing Detection of SSVEPs with Intermodulation Frequencies Using Individual Calibration Data.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to realize high-speed communication between human brain and external devices. Recently, we proposed an intermodulation frequency-based stimulation approach to increase the number of visual stimuli that can be presented on a computer monitor. Although our recent studies have demonstrated that this approach can encode more visual stimuli by only one flickering frequency, the performance of the intermodulation frequency-based SSVEP BCI remains poor and needs further improvement. This study aims to incorporate filter bank analysis and individual SSVEP calibration data into canonical correlation analysis (CCA) to improve the detection of SSVEPs with intermodulation frequencies. Results on classification accuracy and information transfer rate (ITR) suggest that the employment of individual calibration data can significantly improve the performance of the intermodulation frequency-based SSVEP BCI.

Evaluating the Performance of Non-Hair SSVEP-Based BCIs Featuring Template-Based Decoding Methods.

Our previous study has demonstrated the feasibility of employing non-hair-bearing electrodes to build a Steadystate Visual Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) system, relaxing technical barriers in preparation time and offering an ease-of-use apparatus. The signal quality of the SSVEPs and the resultant performance of the non-hair BCI, however, did not close upon those reported in the state-of-the-art BCI studies based on the electroencephalogram (EEG) measured from the occipital regions. Recently, advanced decoding algorithms such as task-related component analysis have made a breakthrough in enhancing the signal quality of the occipital SSVEPs and the performance of SSVEP-based BCIs in a well-controlled laboratory environment. However, it remains unclear if the advanced decoding algorithms can extract highfidelity SSVEPs from the non-hair EEG and enhance the practicality of non-hair BCIs in real-world environments. This study aims to quantitatively evaluate whether, and if so, to what extent the non-hair BCIs can leverage the state-of-art decoding algorithms. Eleven healthy individuals participated in a 5-target SSVEP BCI experiment. A high-density EEG cap recorded SSVEPs from both hair-covered and non-hair-bearing regions. By evaluating and demonstrating the accessibility of nonhair-bearing behind-ear signals, our assessment characterized constraints on data length, trial numbers, channels, and their relationships with the decoding algorithms, providing practical guidelines to optimize SSVEP-based BCI systems in real-life applications.

Periodic component analysis as a spatial filter for SSVEP-based brain–computer interface

BackgroundTraditional spatial filters used for steady-state visual evoked potential (SSVEP) extraction such as minimum energy combination (MEC) require the estimation of the background electroencephalogram (EEG) noise components. Even though this leads to improved performance in low signal to noise ratio (SNR) conditions, it makes such algorithms slow compared to the standard detection methods like canonical correlation analysis (CCA) due to the additional computational cost. New methodIn this paper, Periodic component analysis (πCA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise. The πCA can separate out components corresponding to a given frequency of interest from the background electroencephalogram (EEG) by capturing the temporal information and does not generalize SSVEP based on rigid templates. ResultsData from ten test subjects were used to evaluate the proposed method and the results demonstrate that the periodic component analysis acts as a reliable spatial filter for SSVEP extraction. Statistical tests were performed to validate the results. Comparison with existing methodsThe experimental results show that πCA provides significant improvement in accuracy compared to standard CCA and MEC in low SNR conditions. ConclusionsThe results demonstrate that πCA provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost. Hence πCA is a reliable and efficient alternative detection algorithm for SSVEP based brain–computer interface (BCI).

Complex sparse spatial filter for decoding mixed frequency and phase coded steady-state visually evoked potentials

BackgroundMixed frequency and phase coding (FPC) can achieve the significant increase of the number of commands in steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI). However, the inconsistent phases of the SSVEP over channels in a trial and the existence of non-contributing channels due to noise effects can decrease accurate detection of stimulus frequency. New methodWe propose a novel command detection method based on a complex sparse spatial filter (CSSF) by solving ℓ1- and ℓ2,1-regularization problems for a mixed-coded SSVEP-BCI. In particular, ℓ2,1-regularization (aka group sparsification) can lead to the rejection of electrodes that are not contributing to the SSVEP detection. ResultsA calibration data based canonical correlation analysis (CCA) and CSSF with ℓ1- and ℓ2,1-regularization cases were demonstrated for a 16-target stimuli with eleven subjects. The results of statistical test suggest that the proposed method with ℓ1- and ℓ2,1-regularization significantly achieved the highest ITR. Comparison with existing methodsThe proposed approaches do not need any reference signals, automatically select prominent channels, and reduce the computational cost compared to the other mixed frequency-phase coding (FPC)-based BCIs. ConclusionsThe experimental results suggested that the proposed method can be usable implementing BCI effectively with reduce visual fatigue.

Period doubling behavior in human steady state visual evoked potentials

Objective. Previous human steady state visual evoked potential (SSVEP) experiments have yielded different results regarding the range of stimulus frequencies in which period doubling (PD) behavior is observed. This study aims at obtaining experimental and statistical data regarding the frequency range of PD generation and also investigates other characteristics of PD. Approach. In two sets of experiments, seven subjects were presented a sinusoidal flickering light stimulus with frequencies varying from 15 to 42 Hz. To observe the short term variations in PD generation, another set of 5 successive experiments were performed on five subjects with 10 min breaks in between. To obtain the SSVEP responses, filtering, signal averaging and power spectral density (PSD) estimation were applied to the recorded electroencephalogram. From the PSD estimates, subharmonic occurrence rates were calculated for each experiment and were used along with ANOVA for interpreting the outcomes of the short term repeatability experiments. Main results. Although fundamental (excitation frequency) and second harmonic components appear in almost all SSVEP spectra, there is considerable inter-subject and intra-subject variability regarding PD occurrence. PD occurs for all stimulus frequencies from 15 to 42 Hz when all subjects are considered together. Furthermore, the statistical analyses of short term repeatability experiments suggest that in the short term, PD generation is consistent when all frequencies are considered together but for a single frequency significant short term differences occur. There also is considerable variation in the ratio of subharmonic amplitude to fundamental amplitude across different frequencies for a given subject. Significance. Important results and statistical data are obtained regarding PD generation. Our results indicate that modeling studies should attempt to generate PD for a broader range of stimulus frequencies. It is argued that SSVEP based brain–computer interface applications would likely benefit from the utilization of subharmonics in classification.