Steady-state Visual Evoked Potential Research Articles

Effects of altered-reality training on interocular disinhibition in amblyopia.

Training of viewing an altered-reality environment dichoptically has been found to reactivate human adult ocular dominance plasticity, allowing improvement of vision for amblyopia. One suspected mechanism for this training effect is ocular dominance rebalancing through interocular disinhibition. Here, we investigated whether the training modulated the neural responses reflecting interocular inhibition. Thirteen patients with amblyopia and 11 healthy controls participated in this study. Before and after six daily altered-reality training sessions, participants watched flickering video stimuli with their steady-state visually evoked potential (SSVEP) signals recorded simultaneously. We assessed the amplitude of SSVEP response at intermodulation frequencies, which was a potential neural indicator of interocular suppression. The results showed that training weakened the intermodulation response only in the amblyopic group, which was in agreement with the hypothesis that the training reduced interocular suppression specific to amblyopia. Moreover, even one month after the training ended, we could still observe this neural training effect. These findings provide preliminary neural evidence in support of the disinhibition account for treating amblyopia. We also explain these results with the ocular opponency model, which, to our knowledge, is the first time for this binocular rivalry model to be used in explaining long-term ocular dominance plasticity.

An online hybrid BCI combining SSVEP and EOG-based eye movements.

Hybrid brain-computer interface (hBCI) refers to a system composed of a single-modality BCI and another system. In this paper, we propose an online hybrid BCI combining steady-state visual evoked potential (SSVEP) and eye movements to improve the performance of BCI systems. Twenty buttons corresponding to 20 characters are evenly distributed in the five regions of the GUI and flash at the same time to arouse SSVEP. At the end of the flash, the buttons in the four regions move in different directions, and the subject continues to stare at the target with eyes to generate the corresponding eye movements. The CCA method and FBCCA method were used to detect SSVEP, and the electrooculography (EOG) waveform was used to detect eye movements. Based on the EOG features, this paper proposes a decision-making method based on SSVEP and EOG, which can further improve the performance of the hybrid BCI system. Ten healthy students took part in our experiment, and the average accuracy and information transfer rate of the system were 94.75% and 108.63 bits/min, respectively.

Brain-Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control.

Brain-computer interfaces (BCIs) are widely utilized in control applications for people with severe physical disabilities. Several researchers have aimed to develop practical brain-controlled wheelchairs. An existing electroencephalogram (EEG)-based BCI based on steady-state visually evoked potential (SSVEP) was developed for device control. This study utilized a quick-response (QR) code visual stimulus pattern for a robust existing system. Four commands were generated using the proposed visual stimulation pattern with four flickering frequencies. Moreover, we employed a relative power spectrum density (PSD) method for the SSVEP feature extraction and compared it with an absolute PSD method. We designed experiments to verify the efficiency of the proposed system. The results revealed that the proposed SSVEP method and algorithm yielded an average classification accuracy of approximately 92% in real-time processing. For the wheelchair simulated via independent-based control, the proposed BCI control required approximately five-fold more time than the keyboard control for real-time control. The proposed SSVEP method using a QR code pattern can be used for BCI-based wheelchair control. However, it suffers from visual fatigue owing to long-time continuous control. We will verify and enhance the proposed system for wheelchair control in people with severe physical disabilities.

Power and distribution of evoked gamma oscillations in brain aging and cognitive performance.

Gamma oscillations (≈25-100 Hz) are believed to play an essential role in cognition, and aberrant gamma oscillations occur in brain aging and neurodegeneration. This study examined age-related changes in visually evoked gamma oscillations at two different time points 5 years apart and tested the hypothesis that the power of gamma oscillations correlated to cognitive skills. The cohort consists of elderly males belonging to the Metropolit 1953 Danish Male Birth Cohort (first visit, N=124; second visit, N=88) over a 5-year period from 63 to 68 years of age. Cognitive functions were assessed using a neuropsychological test battery measuring global cognition, intelligence, memory, and processing speed. The power of steady-state visual evoked potentials (SSVEP) was measured at 8 Hz (alpha) and 36 Hz (gamma) frequencies using EEG scalp electrodes. Over the 5-year period cognitive performance remained relatively stable while the power of visually evoked gamma oscillations shifted from posterior to anterior brain regions with increasing age. A higher-than-average cognitive score was correlated with higher gamma power in parieto-occipital areas and lower in frontocentral areas, i.e., preserved distribution of the evoked activity. Our data reveal that the distribution of visually evoked gamma activity becomes distributed with age. Preserved posterior-occipital gamma power in participants with a high level of cognitive performance is consistent with a close association between the ability to produce gamma oscillations and cognition. The data may contribute to our understanding of the mechanisms that link evoked gamma activity and cognition in the aging brain.

A portable SSVEP-BCI system for rehabilitation exoskeleton in augmented reality environment

In order to strengthen the participation of stroke patients in rehabilitation training and weaken the dependence of steady-state visually evoked potential (SSVEP)-based brain-computer interface (BCI) on external stimuli equipment, we build an augmented-reality-based brain-computer interface (AR-BCI) system applied to rehabilitation exoskeleton. The system uses HoloLens to design a 4-category BCI, and adopts the sequential logic decoding method to control sixteen rehabilitation movements. In offline experiments, the performance of AR-BCI is compared with computer screen-based brain-computer interface (CS-BCI), and the effects of data length, number and position of electrodes on the performance of BCI are studied. Then, the instruction classification accuracy of AR-BCI and movement accuracy of exoskeleton are evaluated in online rehabilitation training. The average recognition accuracy of AR-BCI is 90.2% in offline experiments, which is smaller gap with CS-BCI. The recognition accuracy still reaches more than 90% when only Oz and O2 electrodes are used. The online results show that the instruction classification accuracy of AR-BCI is 88.9% and the averaged information transfer rates (ITR) is 30.01 bits min−1 under the data length of 2.5 s. The movement accuracy of exoskeleton is 91.12% with the ITR of 31.63 bits min−1, which is 2.2% higher than instruction recognition accuracy of AR-BCI. These results show that AR-BCI provides a high-performance and more friendly human–computer interaction method, and greatly improves the application potential of wearable BCI.

A high-performance SSVEP-based BCI using imperceptible flickers

Objective. Existing steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) struggle to balance user experience and system performance. This study proposed an individualized space and phase modulation method to code imperceptible flickers at 60 Hz towards a user-friendly SSVEP-based BCI with high performance. Approach. The individualized customization of visual stimulation took the subject-to-subject variability in cortex geometry into account. An annulus global-stimulation was divided into local-stimulations of eight annular sectors and presented to subjects separately. The local-stimulation SSVEPs were superimposed to simulate global-stimulation SSVEPs with 47 space and phase coding combinations. A four-class phase-coded BCI diagram was used to evaluate the simulated classification performance. The performance ranking of all simulated global-stimulation SSVEPs were obtained and three performance levels (optimal, medium, worst) of individualized modulation groups were searched for each subject. The standard-modulation group conforming to the V1 ‘cruciform’ geometry and the non-modulation group were involved as controls. A four-target phase-coded BCI system with SSVEPs at 60 Hz was implemented with the five modulation groups and questionnaires were used to evaluate user experience. Main results. The proposed individualized space and phase modulation method effectively modulated the SSVEP intensity without affecting the user experience. The online BCI system using the 60 Hz stimuli achieved mean information transfer rates of 52.8 ± 1.9 bits min−1, 16.8 ± 2.4 bits min−1, and 42.4 ± 3.0 bits min−1 with individualized optimal-modulation, individualized worst-modulation, and non-modulation groups, respectively. Significance. Structural and functional characteristics of the human visual cortex were exploited to enhance the response intensity of SSVEPs at 60 Hz, resulting in a high-performance BCI system with good user experience. This study has important theoretical significance and application value for promoting the development of the visual BCI technology.

Stimulus-Stimulus Transfer Based on Time-Frequency-Joint Representation in SSVEP-Based BCIs.

Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) require extensive and costly calibration to achieve high performance. Using transfer learning to re-use existing calibration data from old stimuli is a promising strategy, but finding commonalities in the SSVEP signals across different stimuli remains a challenge. This study presents a new perspective, namely time-frequency-joint representation, in which SSVEP signals corresponding to different stimuli can be synchronized, and thus can emphasize common components. According to this time-frequency-joint representation, an adaptive decomposition technique based on the multi-channel adaptive Fourier decomposition (MAFD) is proposed to adaptively decompose SSVEP signals of different stimuli simultaneously. Then, common components can be identified and transferred across stimuli. A simulation study on public SSVEP datasets demonstrates that the proposed stimulus-stimulus transfer method has the ability to extract and transfer these common components across stimuli. By using calibration data from eight source stimuli, the proposed stimulus-stimulus transfer method can generate SSVEP templates of other 32 target stimuli. It boosts the ITR of the stimulus-stimulus transfer based recognition method from 95.966 bits/min to 123.684 bits/min. By extracting and transfer common components across stimuli in the proposed time-frequency-joint representation, the proposed stimulus-stimulus transfer method produces good classification performance without requiring calibration data of target stimuli. This study provides a synchronization standpoint to analyze and model SSVEP signals. In addition, the proposed stimulus-stimulus method shortens the calibration time and thus improve comfort, which could facilitate real-world applications of SSVEP-based BCIs.

A Study on Electrode Positions Around the Ear for BCI Development using SSVEP

In recent years, with the advent of wearable EEG devices, brain-computer interface (BCI) has become popular not only in the medical field but also for general use. Conventional wearable EEG devices are mainly worn on the head, which induces discomfort when worn for a long time and is unattractive. The area around the ear can be considered as a measurement site that solves these problems and is easy to wear. However, there is still little knowledge about EEG around the ear, and the appropriate measurement site has not been determined. In this study, we investigated the optimal electrode placement and EEG recording method around the right ear for steady-state visual evoked potential (SSVEP), which has a high signal-to-noise ratio among EEGs used in BCI. Electrodes were attached to Pz, Oz, and eight locations around the right ear, and measurements were performed using unipolar and bipolar leads. The cross-correlation coefficients between Pz, Oz, and the electrodes around the right ear were calculated, and two locations with particularly high values were identified. In addition, by calculating the EEG data combination from multiple channels around right ear, some channel combinations were found which can significantly improve the discrimination accuracy of BCI system.

컨볼루션 신경망을 활용한 다중의도 뇌-컴퓨터 인터페이스

The Brain-computer interface (BCI) is a technology that measures and analyzes electroencephalogram (EEG) signals to control robots or machines according to the user"s intention. The BCI is a beneficial technology that helps people who can not move their body because of paralysis or amputation so that they can control various electronic devices or express their intentions just by thinking. However, previous BCI technologies have a limitation that they can not have been unable to predict multiple intentions. To use the BCI system in daily life, BCI should be able to predict various intentions such as movement, communication, and robot control. In this study, we developed a BCI technology that can predict multiple intentions simultaneously using a convolutional neural network (CNN). We evaluated the proposed BCI model with EEG signals measured from 10 subjects participants during completing Sensory-Motor Rhythm (SMR) and Steady-State Visual Evoked Potential (SSVEP) tasks. The evaluation showed that the proposed BCI model could predict multiple intentions. The average accuracy averaged by all subjects was 62.8±13.7%. This result implies that BCI users can simultaneously perform various tasks, such as typing and moving. We expect that our study will promote the development of the practical applications of BCI. in daily life.

A high-speed hybrid brain-computer interface with more than 200 targets

Objective. Brain-computer interfaces (BCIs) have recently made significant strides in expanding their instruction set, which has attracted wide attention from researchers. The number of targets and commands is a key indicator of how well BCIs can decode the brain’s intentions. No studies have reported a BCI system with over 200 targets. Approach. This study developed the first high-speed BCI system with up to 216 targets that were encoded by a combination of electroencephalography features, including P300, motion visual evoked potential (mVEP), and steady-state visual evoked potential (SSVEP). Specifically, the hybrid BCI paradigm used the time-frequency division multiple access strategy to elaborately tag targets with P300 and mVEP of different time windows, along with SSVEP of different frequencies. The hybrid features were then decoded by task-discriminant component analysis and linear discriminant analysis. Ten subjects participated in the offline and online cued-guided spelling experiments. Other ten subjects took part in online free-spelling experiments. Main results. The offline results showed that the mVEP and P300 components were prominent in the central, parietal, and occipital regions, while the most distinct SSVEP feature was in the occipital region. The online cued-guided spelling and free-spelling results showed that the proposed BCI system achieved an average accuracy of 85.37% ± 7.49% and 86.00% ± 5.98% for the 216-target classification, resulting in an average information transfer rate (ITR) of 302.83 ± 39.20 bits min−1 and 204.47 ± 37.56 bits min−1, respectively. Notably, the peak ITR could reach up to 367.83 bits min−1. Significance. This study developed the first high-speed BCI system with more than 200 targets, which holds promise for extending BCI’s application scenarios.

Attentional modulation as a mechanism for enhanced facial emotion discrimination: The case of action video game players

Action video game players (AVGPs) outperform nonvideo game players (NVGPs) on a wide variety of attentional tasks, mediating benefits to perceptual and cognitive decision processes. A key issue in the literature is the extent to which such benefits transfer beyond cognition. Using steady-state visual evoked potentials (SSVEP) as a neural measure of attentional resource allocation, we investigated whether the attentional benefit of AVGPs generalizes to the processing of rapidly presented facial emotions. AVGPs (n = 36) and NVGPs (n = 32) performed a novel, attention-demanding emotion discrimination task, requiring the identification of a target emotion in one of two laterally presented streams of emotional faces. The emotional faces flickered at either 2.0 Hz or 2.5 Hz. AVGPs outperformed NVGPs at detecting the target emotions regardless of the type of emotion. Correspondingly, attentional modulation of the SSVEP at parieto-occipital recording sites was larger in AVGPs compared with NVGPs. This difference appeared to be driven by a larger response to attended information, as opposed to a reduced response to irrelevant distractor information. Exploratory analyses confirmed that this novel paradigm elicited the expected pattern of event-related potentials associated with target detection and error processing. These components did not, however, differ between groups. Overall, the results indicate enhanced discrimination of facial emotions in AVGPs arising from enhanced attentional processing of emotional information. This presents evidence for the attentional advantage of AVGPs to extend beyond perceptual and cognitive processes.

An active and passive upper limb rehabilitation training system based on a hybrid brain–computer interface

Movement function rehabilitation of patients with craniocerebral injuries is an important issue facing neurorehabilitation science. The use of brain–computer interface technology in rehabilitation training systems can allow patients to actively participate in the rehabilitation training process and use the brain’s neuroplasticity to enhance the effects from rehabilitation training. At present, the brain–computer interface-based rehabilitation training system still has problems such as insufficient active participation of patients, resulting in slowed motor neural circuit repair or low action execution accuracy. In response to the above problems, this paper designed an active and passive upper limb rehabilitation training system based on a hybrid brain–computer interface of steady-state visual evoked potentials (SSVEP) and movement-related cortical potentials (MRCPs). The system includes six parts: task setting and training guidance module, EEG signal acquisition module, EEG signal preprocessing and recognition module, rehabilitation training module, training completion evaluation module, and communication module. The system drives the rehabilitation robot to complete the training actions by identifying the participant’s SSVEP and evaluates the completion of the rehabilitation training based on the patient’s movement intention recognition results. In this study, 12 participants were recruited. In the online test, the system achieved an average action execution accuracy of 99.3%. The movement intention detection based on MRCPs reached an average accuracy of 82.7%. The participants’ average completion rate was 0.91. The experimental results show that the system can achieve a high rate of execution accuracy. In addition, it can evaluate the active participation level of patients in rehabilitation training based on the movement intention detection results, accelerate the reconstruction of motor neural circuits, improve the effects of training, and provide more effective ways of thinking for the study of upper limb rehabilitation training systems for patients with craniocerebral injuries.

The effects of acupuncture on clinical efficacy and steady-state visual evoked potentials in insomnia patients with emotional disorders: A randomized single-blind sham-controlled trial.

The aim of this study was to observe the clinical effects and brain electrical potential changes following acupuncture in the treatment of insomnia patients with mood disorders. Ninety patients with insomnia who met the inclusion criteria were randomly divided into the active acupuncture group (AA group, n = 44) and sham acupuncture group (SA group, n = 46) at a ratio of 1:1. The primary outcome was the total score of the Pittsburgh Sleep Quality Index (PSQI), and the secondary outcomes were the total effective rate, Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS) scores, and values of steady-state visual evoked potentials (SSVEP). The two groups received acupuncture or sham acupuncture 10 times (2 weeks). Finally, the total PSQI scores of the AA group and SA group were significantly different (p < 0.05) at 2 weeks (6.11 ± 2.33 vs. 10.37 ± 4.73), 6 weeks (6.27 ± 1.39 vs. 11.93 ± 3.07), 18 weeks (6.32 ± 2.84 vs. 11.78 ± 2.95) and 42 weeks (8.05 ± 3.14 vs. 12.54 ± 2.81). Further analysis found that AA group patients received acupuncture treatment at any age after the same effect (p > 0.05). The SAS and SDS scores of the AA group were also significantly different from those of the SA group at each assessment time point (p < 0.05). The total effective rate of the AA group was 81.82%, while that of the SA group was 30.43% (p < 0.05). There was no significant difference between the AA group and SA group only in the brain potential of the parietal lobe (F4), left temporal lobe (C3) and right temporal lobe (T8) (P > 0.05), but there was a significant difference between other brain regions (P < 0.05). In addition, correlation analysis showed that there was a certain positive correlation between the total PSQI score, SAS score, efficacy level, and SSVEP value in the AA group as follows: C4 and the total PSQI score (r = 0.595, P = 0.041), F3 and SAS score (r = 0.604, P = 0.037), FPz and efficiency level of the frontal lobe (r = 0.581, P = 0.048), and O2 and efficiency level of the occipital lobe (r = 0.704, P = 0.011). Therefore, acupuncture have a good clinical effect on patients with insomnia and emotional disorders and have a significant regulatory effect on abnormally excited brain potentials.

Transfer learning of an ensemble of DNNs for SSVEP BCI spellers without user-specific training

Objective. Steady-state visually evoked potentials (SSVEPs), measured with electroencephalogram (EEG), yield decent information transfer rates (ITRs) in brain-computer interface (BCI) spellers. However, the current high performing SSVEP BCI spellers in the literature require an initial lengthy and tiring user-specific training for each new user for system adaptation, including data collection with EEG experiments, algorithm training and calibration (all are before the actual use of the system). This impedes the widespread use of BCIs. To ensure practicality, we propose a novel target identification method based on an ensemble of deep neural networks (DNNs), which does not require any sort of user-specific training. Approach. We exploit already-existing literature datasets from participants of previously conducted EEG experiments to train a global target identifier DNN first, which is then fine-tuned to each participant. We transfer this ensemble of fine-tuned DNNs to the new user instance, determine the k most representative DNNs according to the participants’ statistical similarities to the new user, and predict the target character through a weighted combination of the ensemble predictions. Main results. The proposed method significantly outperforms all the state-of-the-art alternatives for all stimulation durations in [0.2–1.0] s on two large-scale benchmark and BETA datasets, and achieves impressive 155.51 bits/min and 114.64 bits/min ITRs. Code is available for reproducibility: https://github.com/osmanberke/Ensemble-of-DNNs. Significance. Our Ensemble-DNN method has the potential to promote the practical widespread deployment of BCI spellers in daily lives as we provide the highest performance while enabling the immediate system use without any user-specific training.

Global attentional selection of visual features is not associated with selective modulation of posterior alpha-band activity.

Attending to a single feature, such as color or motion, leads to global modulation of neural processing associated with the representation of the attended features. Alpha-band modulations are hypothesized to be a marker (and even a mechanism) of the modulation of neural processing. By adopting a previously used attentional shifting paradigm, we examined whether alpha-band dynamics are linked to sustained Feature-Based-Attentional (FBA) selection. For this purpose, we presented task-irrelevant flickering random dot kinematograms (RDKs) in the periphery that either did or did not share the to-be-attended color of centrally presented task-relevant RDKs and should thus be subject to global FBA selection. Steady-state visual evoked potentials (SSVEPs) and alpha-band activity associated with these task-irrelevant RDKs were analyzed to quantify FBA modulation. Overall, the SSVEP results replicated previous findings: relative to a pre-cue baseline, SSVEP amplitudes for peripheral RDKs were significantly enhanced when these RDKs shared the to-be-attended color of the central RDKs and were not modulated when they shared the centrally to-be-ignored color. Nevertheless, there were no differences in alpha-band amplitude modulations between signals recorded contralateral to the RDKs sharing the centrally attended color and RDKs sharing the centrally ignored color. Hence, alpha-band modulations seem not to index the sustained global selection of attended over unattended feature values within the same feature dimension.

Enhancing Detection of Multi-Frequency-Modulated SSVEP Using Phase Difference Constrained Canonical Correlation Analysis.

Multi-frequency-modulated visual stimulation scheme has been shown effective for the steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) recently, especially in increasing the visual target number with less stimulus frequencies and mitigating the visual fatigue. However, the existing calibration-free recognition algorithms based on the traditional canonical correlation analysis (CCA) cannot provide the merited performance. To improve the recognition performance, this study proposes a phase difference constrained CCA (pdCCA), which assumes that the multi-frequency-modulated SSVEPs share a common spatial filter over different frequencies and have a specified phase difference. Specifically, during the CCA computation, the phase differences of the spatially filtered SSVEPs are constrained using the temporal concatenation of the sine-cosine reference signals with the pre-defined initial phases. We evaluate the performance of the proposed pdCCA-based method on three representative multi-frequency-modulated visual stimulation paradigms (i.e., based on the multi-frequency sequential coding, the dual-frequency, and the amplitude modulation). The evaluation results on four SSVEP datasets (Dataset Ia, Ib, II, and III) show that the pdCCA-based method can significantly outperform the current CCA method in terms of recognition accuracy. It improves the accuracy by 22.09% in Dataset Ia, 20.86% in Dataset Ib, 8.61% in Dataset II, and 25.85% in Dataset III. The pdCCA-based method, which actively controls the phase difference of the multi-frequency-modulated SSVEPs after spatial filtering, is a new calibration-free method for multi-frequency-modulated SSVEP-based BCIs.

Cross-Subject Transfer Method Based on Domain Generalization for Facilitating Calibration of SSVEP-Based BCIs.

In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), various spatial filtering methods based on individual calibration data have been proposed to alleviate the interference of spontaneous activities in SSVEP signals for enhancing the SSVEP detection performance. However, the time-consuming calibration session would increase the visual fatigue of subjects and reduce the usability of the BCI system. The key idea of this study is to propose a cross-subject transfer method based on domain generalization, which transfers the domain-invariant spatial filters and templates learned from source subjects to the target subject with no access to the EEG data from the target subject. The transferred spatial filters and templates are obtained by maximizing the intra- and inter-subject correlations using the SSVEP data corresponding to the target and its neighboring stimuli. For SSVEP detection of the target subject, four types of correlation coefficients are calculated to construct the feature vector. Experimental results estimated with three SSVEP datasets show that the proposed cross-subject transfer method improves the SSVEP detection performance compared to state-of-art methods. The satisfactory results demonstrate that the proposed method provides an effective transfer learning strategy requiring no tedious data collection process for new users, holding the potential of promoting practical applications of SSVEP-based BCI.

A calibration-free hybrid BCI speller system based on high-frequency SSVEP and sEMG.

Hybrid brain-computer interface (hBCI) systems that combine steady-state visual evoked potential (SSVEP) and surface electromyography (sEMG) signals have attracted attention of researchers due to the advantage of exhibiting significantly improved system performance. However, almost all existing studies adopt low-frequency SSVEP to build hBCI. It produces much more visual fatigue than high-frequency SSVEP. Therefore, the current study attempts to build a hBCI based on high-frequency SSVEP and sEMG. With these two signals, this study designed and realized a 32-target hBCI speller system. Thirty-two targets were separated from the middle into two groups. Each side contained 16 sets of targets with different high-frequency visual stimuli (i.e., 31-34.75 Hz with an interval of 0.25 Hz). sEMG was utilized to choose the group and SSVEP was adopted to identify intra-group targets. The filter bank canonical correlation analysis (FBCCA) and the root mean square value (RMS) methods were used to identify signals. Therefore, the proposed system allowed users to operate it without system calibration. A total of 12 healthy subjects participated in online experiment, with an average accuracy of 93.52 ± 1.66% and the average information transfer rate (ITR) reached 93.50 ± 3.10 bits/min. Furthermore, 12 participants perfectly completed the free-spelling tasks. These results of the experiments indicated feasibility and practicality of the proposed hybrid BCI speller system.

Frequency Domain Filtering Method for SSVEP-EEG Preprocessing

Steady-state visual evoked potential (SSVEP) signal collected from the scalp typically contains other types of electric signals, and it is important to remove these noise components from the actual signal by application of a pre-processing step for accurate analysis. High-pass or bandpass filtering of the SSVEP signal in the time domain is the most common pre-processing method. Because frequency is the most important feature information contained in the SSVEP signal, a technique for frequency-domain filtering of SSVEP was proposed here. In this method, the time-domain signal is extended to multi-dimensional signal by empirical mode decomposition (EMD), where each dimension represents a intrinsic mode function (IMF). The multi-dimensional signal is transformed to a frequency-domain signal by 2-D Fourier transform, the Gaussian high-pass filter function is constructed to perform high-pass filtering, and then the filtered signal is transformed to time domain by 2-D inverse Fourier transform. Finally, the filtered multi-dimensional intrinsic mode function is superimposed and averaged as the frequency-domain filtered signal. Compared with the time-domain filtering method, the experimental results revealed that frequency-domain filtering method effectively removed the baseline drift in signal and effectively suppressed the low-frequency interference component. This method was tested using data from public datasets and the results show that the proposed frequency-domain filtering method can significantly improve the feature recognition performance of canonical correlation analysis (CCA), filter bank canonical correlation analysis (FBCCA), and task-related component analysis (TRCA) methods. Thus, the results suggest that the application of frequency-domain filtering in the pre-processing stage allows improved noise removal. The proposed method extends SSVEP signal filtering from time-domain to frequency-domain, and the results suggest that this analysis tool significantly promotes the practical application of SSVEP systems.

Enhancing Detection of Control State for High-Speed Asynchronous SSVEP-BCIs Using Frequency-Specific Framework.

This study proposed a novel frequency-specific (FS) algorithm framework for enhancing control state detection using short data length toward high-performance asynchronous steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCI). The FS framework sequentially incorporated task-related component analysis (TRCA)-based SSVEP identification and a classifier bank containing multiple FS control state detection classifiers. For an input EEG epoch, the FS framework first identified its potential SSVEP frequency using the TRCA-based method and then recognized its control state using one of the classifiers trained on the features specifically related to the identified frequency. A frequency-unified (FU) framework that conducted control state detection using a unified classifier trained on features related to all candidate frequencies was proposed to compare with the FS framework. Offline evaluation using data lengths within 1 s found that the FS framework achieved excellent performance and significantly outperformed the FU framework. 14-target FS and FU asynchronous systems were separately constructed by incorporating a simple dynamic stopping strategy and validated using a cue-guided selection task in an online experiment. Using averaged data length of 591.63±5.65 ms, the online FS system significantly outperformed the FU system and achieved an information transfer rate, true positive rate, false positive rate, and balanced accuracy of 124.95±12.35 bits/min, 93.16±4.4%, 5.21±5.85%, and 92.89±4.02%, respectively. The FS system was also of higher reliability by accepting more correctly identified SSVEP trials and rejecting more wrongly identified ones. These results suggest that the FS framework has great potential to enhance the control state detection for high-speed asynchronous SSVEP-BCIs.