Steady-state Visual Evoked Potential Research Articles

Poster Session I: Noninvasive neuromodulation of subcortical visual pathways with transcranial focused ultrasound.

Transcranial ultrasound stimulation (TUS) is an emerging tool to noninvasively modulate neural activity in deep brain areas. In preparation for our first in-human TUS studies, we targeted TUS to the lateral geniculate nucleus (visual thalamus) in a large mammal (sheep). Full-field light flash stimuli were presented with or without concomitant TUS in randomly interleaved trials. Similar to what has previously observed by Fry et al (Nature 1959) in cats, EEG visual-evoked potentials (VEPs) were reversibly suppressed by TUS to the LGN. No changes in VEPs were observed in sheep who received sham-TUS to a control site in the basal ganglia, ruling out potential transducer auditory-somatosensory confounds. Magnetic resonance acoustic radiation force imaging (MR-ARFI), a technique to measure the ultrasound focus in situ, showed a focal volume of microscopic displacement at the expected target. Excitingly, MR-ARFI predicted the suppressive effect on VEPs in individual subjects, suggesting that MR-ARFI can be used to confirm TUS targeting and estimate neurophysiological impact. We are now translating this paradigm into human, targeting TUS to the LGN while participants perform a contrast detection task with EEG recording of steady-state VEPs. MR-ARFI will be measured to evaluate targeting and estimate TUS dosage in each participant. This work provides the foundation for a dissection of the roles of different subcortical nuclei in different aspects of human vision.

A data expansion technique based on training and testing sample to boost the detection of SSVEPs for brain-computer interfaces

Objective. Currently, steady-state visual evoked potentials (SSVEPs)-based brain-computer interfaces (BCIs) have achieved the highest interaction accuracy and speed among all BCI paradigms. However, its decoding efficacy depends deeply on the number of training samples, and the system performance would have a dramatic drop when the training dataset decreased to a small size. To date, no study has been reported to incorporate the unsupervised learning information from testing trails into the construction of supervised classification model, which is a potential way to mitigate the overfitting effect of limited samples. Approach. This study proposed a novel method for SSVEPs detection, i.e. cyclic shift trials (CSTs), which could combine unsupervised learning information from test trials and supervised learning information from train trials. Furthermore, since SSVEPs are time-locked and phase-locked to the onset of specific flashes, CST could also expand training samples on the basis of its regularity and periodicity. In order to verify the effectiveness of CST, we designed an online SSVEP-BCI system, and tested this system combined CST with two common classification algorithms, i.e. extended canonical correlation analysis and ensemble task-related component analysis. Main results. CST could significantly enhance the signal to noise ratios of SSVEPs and improve the performance of systems especially for the condition of few training samples and short stimulus time. The online information transfer rate could reach up to 236.19 bits min−1 using 36 s calibration time of only one training sample for each category. Significance. The proposed CST method can take full advantages of supervised learning information from training samples and unsupervised learning information of testing samples. Furthermore, it is a data expansion technique, which can enhance the SSVEP characteristics and reduce dependence on sample size. Above all, CST is a promising method to improve the performance of SSVEP-based BCI without any additional experimental burden.

Almost free of calibration for SSVEP-based brain-computer interfaces

Objective. Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) is a promising technology that can achieve high information transfer rate (ITR) with supervised algorithms such as ensemble task-related component analysis (eTRCA) and task-discriminant component analysis (TDCA). However, training individual models requires a tedious and time-consuming calibration process, which hinders the real-life use of SSVEP-BCIs. A recent data augmentation method, called source aliasing matrix estimation (SAME), can generate new EEG samples from a few calibration trials. But SAME does not exploit the information across stimuli as well as only reduces the number of calibration trials per command, so it still has some limitations. Approach. This study proposes an extended version of SAME, called multi-stimulus SAME (msSAME), which exploits the similarity of the aliasing matrix across frequencies to enhance the performance of SSVEP-BCI with insufficient calibration trials. We also propose a semi-supervised approach based on msSAME that can further reduce the number of SSVEP frequencies needed for calibration. We evaluate our method on two public datasets, Benchmark and BETA, and an online experiment. Main results. The results show that msSAME outperforms SAME for both eTRCA and TDCA on the public datasets. Moreover, the semi-supervised msSAME-based method achieves comparable performance to the fully calibrated methods and outperforms the conventional free-calibrated methods. Remarkably, our method only needs 24 s to calibrate 40 targets in the online experiment and achieves an average ITR of 213.8 bits min−1 with a peak of 242.6 bits min−1. Significance. This study significantly reduces the calibration effort for individual SSVEP-BCIs, which is beneficial for developing practical plug-and-play SSVEP-BCIs.

Enhancing multi-class SSVEP classification performance using regularized covariance estimators, spatiotemporal beamforming, and short-time EEG trials

One of the main challenges in BCI technologies is the problem of poor information transfer rate (ITR) of BCI systems and its effect on reducing the commands users can give. At the state of the art, the Linearly Constrained Minimum Variance (LCMV) beamformer is one of the most effective strategies that can provide satisfactory performance in the analysis of steady-state visual evoked potentials (SSVEP). When only a short-period SSVEP signal is available, a poor estimate of the covariance matrix used in the original LCMV method is obtained. Therefore, the main drawback of the conventional LCMV beamformer and its spatiotemporal form (LCMVst) is that they require a long-period SSVEP signal to achieve a reliable estimate for the beamformer weights. To cope with the mentioned problems, this paper presents two types of regularized estimators to achieve a robust estimate for the covariance matrix when only a short-period SSVEP signal is available. These two types, which are user-independent, include the convex combination (CC) and general linear combination (GLC) methods. In the next step, in order to provide suitable beamformer weights, the CC and GLC covariance estimators are combined with the original LCMVst beamformer. Furthermore, two new subject-based electrode selection algorithms, i.e., sequential forward electrode selection (SFES) algorithm and common spatial pattern (CSP) based technique, are applied to pick out the best collection of electrodes and to maximize the classification accuracy. The introduced methods were assessed using a four-class SSVEP dataset recorded on 20 subjects. Experimental results indicate that the proposed estimators optimally classify SSVEPs by estimating proper beamformer weights. The proposed LCMVst-CC and LCMVst-GLC beamformers improved the average classification accuracy by about 27.75% in comparison to the original LCMV-based beamformer at the 0.25-second segment length. Using SSVEP stimulations, we show that our proposed beamformers offer a better classification performance than the conventional LCMV-based spatiotemporal beamformer for all stimulation times.

Compressive sensing applied to SSVEP-based brain–computer interface in the cloud for online control of a virtual wheelchair

People with severe motor disabilities have great challenges in achieving independence, particularly driving or controlling manually robotic wheelchairs. Existing assistive technologies for robotic wheelchair control often fail to address user needs adequately, becoming necessary an appropriate evaluation of usability and user’s satisfaction for these systems. In fact, user experience and satisfaction are crucial in determining effectiveness, usability and acceptance of these technologies. The aim is to enhance the quality of life of individuals with severe motor disabilities, increasing their independence to perform some daily activities, such as locomotion. This study proposes a cloud Steady-State Visual Evoked Potential (SSVEP)-based Brain–Computer Interface (BCI) system to evaluate its performance through the user experience in commanding a virtual electric-powered wheelchair in a training simulator. Moreover, this study compares traditional wheelchair command modes, such as input interfaces using JoyStick (JS) and Eye Tracker (ET), through standardized questionnaires to measure motivation, ease of use, workload, discomfort, sense of presence and other relevant parameters. The analysis of user experience reveals high levels of motivation, enjoyment, and usability when using the cloud SSVEP-based BCI proposed here. Although our BCI demands higher mental work and discomforts than both ET and JS, its potential for improving the user’s performance is demonstrated here. The findings of this research provide valuable insights for researchers and practitioners in the field of assistive technologies, enabling the development of more personalized systems based on the user’s experience. The main contributions of this study lie in a cloud BCI approach and a comprehensive evaluation methodology to evaluate the user’s experience.

An Accumulating Neural Signal Underlying Binocular Rivalry Dynamics.

During binocular rivalry, conflicting images are presented one to each eye and perception alternates stochastically between them. Despite stable percepts between alternations, modeling suggests that neural signals representing the two images change gradually, and that the duration of stable percepts are determined by the time required for these signals to reach a threshold that triggers an alternation. However, direct physiological evidence for such signals has been lacking. Here, we identify a neural signal in the human visual cortex that shows these predicted properties. We measured steady-state visual evoked potentials (SSVEPs) in 84 human participants (62 females, 22 males) who were presented with orthogonal gratings, one to each eye, flickering at different frequencies. Participants indicated their percept while EEG data were collected. The time courses of the SSVEP amplitudes at the two frequencies were then compared across different percept durations, within participants. For all durations, the amplitude of signals corresponding to the suppressed stimulus increased and the amplitude corresponding to the dominant stimulus decreased throughout the percept. Critically, longer percepts were characterized by more gradual increases in the suppressed signal and more gradual decreases of the dominant signal. Changes in signals were similar and rapid at the end of all percepts, presumably reflecting perceptual transitions. These features of the SSVEP time courses are well predicted by a model in which perceptual transitions are produced by the accumulation of noisy signals. Identification of this signal underlying binocular rivalry should allow strong tests of neural models of rivalry, bistable perception, and neural suppression.SIGNIFICANCE STATEMENT During binocular rivalry, two conflicting images are presented to the two eyes and perception alternates between them, with switches occurring at seemingly random times. Rivalry is an important and longstanding model system in neuroscience, used for understanding neural suppression, intrinsic neural dynamics, and even the neural correlates of consciousness. All models of rivalry propose that it depends on gradually changing neural activity that on reaching some threshold triggers the perceptual switches. This manuscript reports the first physiological measurement of neural signals with that set of properties in human participants. The signals, measured with EEG in human observers, closely match the predictions of recent models of rivalry, and should pave the way for much future work.

Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces

Brain–computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEPs) are inexpensive and do not require user training. However, the highly personalized reaction to visual stimulation is an obstacle to the wider application of this technique, as it can be ineffective, tiring, or even harmful at certain frequencies. In our experimental study, we proposed a new approach to the selection of optimal frequencies of photostimulation. By using a custom photostimulation device, we covered a frequency range from 5 to 25 Hz with 1 Hz increments, recording the subjects’ brainwave activity (EEG) and analyzing the signal-to-noise ratio (SNR) changes at the corresponding frequencies. The proposed set of SNR-based coefficients and the discomfort index, determined by the ratio of theta and beta rhythms in the EEG signal, enables the automation of obtaining the recommended stimulation frequencies for use in SSVEP-based BCIs.

Temporal dynamics of normalization reweighting.

For decades, neural suppression in early visual cortex has been thought to be fixed. But recent work has challenged this assumption by showing that suppression can be reweighted based on recent history; when pairs of stimuli are repeatedly presented together, suppression between them strengthens. Here we investigate the temporal dynamics of this process using a steady-state visual evoked potential (SSVEP) paradigm that provides a time-resolved, direct index of suppression between pairs of stimuli flickering at different frequencies (5 and 7Hz). Our initial analysis of an existing electroencephalography (EEG) dataset (N = 100) indicated that suppression increases substantially during the first 2-5 seconds of stimulus presentation (with some variation across stimulation frequency). We then collected new EEG data (N = 100) replicating this finding for both monocular and dichoptic mask arrangements in a preregistered study designed to measure reweighting. A third experiment (N = 20) used source-localized magnetoencephalography and found that these effects are apparent in primary visual cortex (V1), consistent with results from neurophysiological work. Because long-standing theories propose inhibition/excitation differences in autism, we also compared reweighting between individuals with high versus low autistic traits, and with and without an autism diagnosis, across our three datasets (total N = 220). We find no compelling differences in reweighting that are associated with autism. Our results support the normalization reweighting model and indicate that for prolonged stimulation, increases in suppression occur on the order of 2-5 seconds after stimulus onset.

Farklı performans ölçümleri kullanılarak kararlı durum görsel uyarılmış potansiyel kontrollü quadcopter yolunun değerlendirilmesi

This study focuses on controlling a quadcopter system using a steady-state visual evoked potential (SSVEP)-based brain-computer interface system. In the literature, researchers report the accuracy and information transfer rate. However, these measures do not provide sufficient information about the predicted and target path similarity. The drone is expected to follow a certain square-shaped path and return to its starting position. We calculated the final and mean distances as additional outcome measures using several classifiers. The results emphasize the importance of having a balanced confusion matrix in the performance of quadcopter control and provide a more complete picture in the evaluation of the quadcopter’s performance. Focusing on the relationship between classification accuracy and spatial deviation might create a new perspective for BCI-based control systems.

A hybrid BCI combining SSVEP and EOG and its application for continuous wheelchair control

Brain-computer interfaces (BCIs) have emerged as one of the most promising techniques for individuals with motor disabilities to control external devices directly without peripheral pathways. While the discrete control strategy is widely adopted in BCI systems, the continuous control strategy provides intuitive and fluent control of robotic devices. However, the steady-state visual evoked potential (SSVEP)-based BCI is still far from practical applications of continuous control. One of the existing challenges is the long erroneous transition time, i.e., the duration from when the subject switches towards a new target to when the BCI system recognizes this new target. Therefore, this study proposed a hybrid BCI combining SSVEP and electrooculography (EOG). First, the prior probability distribution of the intended target was obtained by the SSVEP detection method. Different from directly picking the one with the maximum probability as the conventional SSVEP-based BCI did, the captured saccade event in the EOG signal served as an additional event to optimize this probability distribution, and the target was then extrapolated and outputted. An offline experiment simulating the target switching process and an online experiment of virtual wheelchair control were designed and conducted. The proposed hybrid BCI significantly outperformed the conventional SSVEP-based BCI in both the offline experiment (continuous accuracy:64.99 ± 3.44 % vs. 55.76 ± 3.67 %, transition time: 1.19 ± 0.073 s vs. 1.65 ± 0.08 s) and the online wheelchair control experiment (success rate: 81.67 ± 5.75 % vs. 16.67 %± 3.76 %; collision times with the dynamic obstacle: 1.50 ± 0.50 vs. 8.25 ± 0.37; minimum distance to dynamic obstacle: 1.41 ± 0.13 m vs. 0.31 ± 0.08 m). The results revealed that by combining the SSVEP and EOG signals, the proposed hybrid BCI can significantly diminish the transition time in SSVEP-based BCI and achieve continuous, fluent control. The present study provides a novel framework for integrating the SSVEP and EOG, and promotes the potential applications of continuous brain-actuated robotic devices.

Combining biosignals with RFID to develop a multimodal-shared control interface

The paper presents an approach to develop assistive devices by combining multimodal biosignals and radio frequency identification (RFID). The brain and eye signals have been used as multimodal biosignals to control the movement of a robot in four directions and help reach near the object following a predefined path. RFID shared control over object identification, and the gripper arm connected at the end effector of the robot performs pick and place operations. Horizontal electrooculography (EOG) has been used for x-directional movement control and electroencephalography (EEG) signal obtained by visual stimulus, called steady-state visual-evoked potential (SSVEP) has been used for y-directional movement control of a robot. The SSVEP signal has also been used to ring an alarm in case of an emergency call by the user. Two parameters classification accuracy (CA) and information transfer rate (ITR) have been calculated for the performance evaluation of the proposed multimodal-shared control model and have shown improved results as compared to previous literature. The results also proved that the proposed model can be used for real-time mobility assistive applications.

Distinct patterns of spatial attentional modulation of steady-state visual evoked magnetic fields (SSVEFs) in subdivisions of the human early visual cortex.

In recent years, steady-state visual evoked potentials (SSVEPs) became an increasingly valuable tool to investigate neural dynamics of competitive attentional interactions and brain-computer interfaces. This is due to their good signal-to-noise ratio, allowing for single-trial analysis, and their ongoing oscillating nature that enables to analyze temporal dynamics of facilitation and suppression. Given the popularity of SSVEPs, it is surprising that only a few studies looked at the cortical sources of these responses. This is in particular the case when searching for studies that assessed the cortical sources of attentional SSVEP amplitude modulations. To address this issue, we used a typical spatial attention task and recorded neuromagnetic fields (MEG) while presenting frequency-tagged stimuli in the left and right visual fields, respectively. Importantly, we controlled for attentional deployment in a baseline period before the shifting cue. Subjects either attended to a central fixation cross or to two peripheral stimuli simultaneously. Results clearly showed that signal sources and attention effects were restricted to the early visual cortex: V1, V2, hMT+, precuneus, occipital-parietal, and inferior-temporal cortex. When subjects attended to central fixation first, shifting attention to one of the peripheral stimuli resulted in a significant activation increase for the to-be-attended stimulus with no activation decrease for the to-be-ignored stimulus in hMT+ and inferio-temporal cortex, but significant SSVEF decreases from V1 to occipito-parietal cortex. When attention was first deployed to both rings, shifting attention away from one ring basically resulted in a significant activation decrease in all areas for the then-to-be-ignored stimulus.

An investigation of inattentional blindness using gaze and frequency tagging.

Inattentional blindness (IB) occurs when a salient object presented in plain sight goes unnoticed when its appearance is unexpected. Across two experiments, participants completed a classic dynamic IB task while eye movements and steady-state visual evoked potential (SSVEP) responses were continually recorded. This allowed us to measure the modulation of gaze and brain-based indices of attention during IB. While an SSVEP response to all stimuli including the unexpected object was attained, only gaze measures were able to discriminate noticers from nonnoticers. Experiment 1 used a prototypical sustained IB task and found that gaze toward the unexpected object was largely unrelated to noticing that object. Experiment 2 manipulated the contrast of the target and distractor stimuli, and instead observed a tight concordance between gazing at the unexpected object and reporting its presence. This task-based variability in gaze deployment is consistent with the broader literature and cumulatively delineates the challenges faced in translating lab-based IB research from the bench to the bedside. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Spectrum-Enhanced TRCA (SE-TRCA): A novel approach for direction detection in SSVEP-based BCI

The Steady State Visual Evoked Potential (SSVEP) is a widely used component in BCIs due to its high noise resistance and low equipment requirements. Recently, a novel SSVEP-based paradigm has been introduced for direction detection, in which, unlike the common SSVEP paradigms that use several frequency stimuli, only one flickering stimulus is used and it makes direction detection very challenging. So far, only the CCA method has been used for direction detection using SSVEP component analysis. Since Canonical Correlation Analysis (CCA) has some limitations, a Task-Related Component Analysis (TRCA) based method has been introduced for feature extraction to improve the direction detection performance.Although these methods have been proven efficient, they do not utilize the latent frequency information in the EEG signal. Therefore, the performance of direction detection using SSVEP component analysis is still suboptimal. For further improvement, the TRCA-based algorithm is enhanced by incorporating frequency information and introducing Spectrum-Enhanced TRCA (SE-TRCA). SE-TRCA method can utilize frequency information in conjunction with spatial information by concatenating the EEG signal and its shifted version. Accordingly, the obtained spatio-spectral filters perform as a Finite Impulse Response (FIR) filter.To evaluate the proposed SE-TRCA method, two different sorts of datasets (1) a hybrid BCI dataset (including SSVEP component for direction detection) and (2) a pure benchmark SSVEP dataset (including SSVEP component for frequency detection) have been used. Our experiments showed that the accuracy of direction detection using the proposed SE-TRCA and TRCA approaches compared to CCA-based approach have been increased by 23.35% and 28.24%, respectively. Furthermore, the accuracy of character recognition obtained from integrating P300 and SSVEP components in CCA, TRCA, and SETRCA approaches are 54.01%, 56.02%, and 58.56%, on the hybrid dataset, respectively. The evaluation of the SE-TRCA method on the benchmark SSVEP dataset demonstrates that the SE-TRCA method outperforms both CCA and TRCA, particularly regarding frequency detection accuracy. In this specific dataset, the SE-TRCA method achieved an impressive frequency detection accuracy of 98.19% for a 3-s signal, surpassing the accuracies of TRCA and CCA, which were 97.91% and 90.47%, respectively.These results demonstrated that the TRCA-based approach is more efficient than the CCA approach to extracting spatial filters. Moreover, SE-TRCA, extracting both Spectral and spatial information from the EEG signal, can capture more discriminative features from the SSVEP component and increase the accuracy of classification. The results of this study emphasize the effectiveness of the proposed SE-TRCA approach across different SSVEP paradigms and tasks. These findings provide strong evidence for the method’s ability to generalize well in SSVEP analysis.

Cortical signatures of visual body representation develop in human infancy

Human infants cannot report their experiences, limiting what we can learn about their bodily awareness. However, visual cortical responses to the body, linked to visual awareness and selective attention in adults, can be easily measured in infants and provide a promising marker of bodily awareness in early life. We presented 4- and 8-month-old infants with a flickering (7.5 Hz) video of a hand being stroked and recorded steady-state visual evoked potentials (SSVEPs). In half of the trials, the infants also received tactile stroking synchronously with visual stroking. The 8-month-old, but not the 4-month-old infants, showed a significant enhancement of SSVEP responses when they received tactile stimulation concurrent with the visually observed stroking. Follow-up experiments showed that this enhancement did not occur when the visual hand was presented in an incompatible posture with the infant’s own body or when the visual stimulus was a body-irrelevant video. Our findings provide a novel insight into the development of bodily self-awareness in the first year of life.

Feature-based Attentional Amplitude Modulations of the Steady-state Visual Evoked Potentials Reflect Blood Oxygen Level Dependent Changes in Feature-sensitive Visual Areas.

Recent EEG studies have investigated basic principles of feature-based attention by means of frequency-tagged random dot kinematograms in which different colors are simultaneously presented at different temporal frequencies to elicit steady-state visual evoked potentials (SSVEPs). These experiments consistently showed global facilitation of the to-be-attended random dot kinematogram-a basic principle of feature-based attention. SSVEP source estimation suggested that posterior visual cortex from V1 to area hMT+/V5 is broadly activated by frequency-tagged stimuli. What is presently unknown is whether the feature-based attentional facilitation of SSVEPs is a rather unspecific neural response including all visual areas that follow the "on/off," or whether SSVEP feature-based amplitude enhancements are driven by activity in visual areas most sensitive to a specific feature, such as V4v in the case of color. Here, we leverage multimodal SSVEP-fMRI recordings in human participants and a multidimensional feature-based attention paradigm to investigate this question. Attending to shape produced significantly greater SSVEP-BOLD covariation in primary visual cortex compared with color. SSVEP-BOLD covariation during color selection increased along the visual hierarchy, with greatest values in areas V3 and V4. Importantly, in area hMT+/V5, we found no differences between shape and color selection. Results suggest that SSVEP amplitude enhancements in feature-based attention is not an unspecific enhancement of neural activity in all visual areas following the "on/off." These findings open new avenues to investigating neural dynamics of competitive interactions in specific visual areas sensitive to a certain feature in a more economical way and better temporal resolution compared with fMRI.

Winning algorithms in BCI Controlled Robot Contest in World Robot Contest 2022: BCI Turing Test

The Turing Test is a method of testing whether a machine has human intelligence. A novel brain–computer interface (BCI) Turing Test was proposed in the BCI Controlled Robot Contest in World Robot Contest 2022. Contestants developed algorithms that can distinguish if an instruction is issued by a human. Participants collaborated with an electroencephalogram-based BCI to play a soccer game in a virtual scenario. Participants were asked to perform steady-state visual evoked potential (SSVEP) tasks or motor imagery (MI) tasks to control the robots or be in an idle state to mimic the system giving instructions on behalf of the participants. Several algorithms proposed in this competition are developed based on the concept that the idle state is a category in multiclass classification problems. This paper details the algorithms of the top five teams with the best performance in the final, lists the popular classification models and algorithms for MI and SSVEP, and discusses the effectiveness of each approach in improving classification performance and reducing the computation time.

Overview of the winning approaches in 2022 World Robot Contest Championship–Asynchronous SSVEP

In recent years, the steady-state visual evoked potential (SSVEP) electroencephalogram paradigm has gained considerable attention owing to its high information transfer rate. Several approaches have been proposed to improve the performance of SSVEP-based brain–computer interface (BCI) systems. In SSVEP-based BCIs, the asynchronous scenario poses a challenge as the subjects stare at the screen without synchronization signals from the system. The algorithm must distinguish whether the subject is being stimulated or not, which presents a significant challenge for accurate classification. In the 2022 World Robot Contest Championship, several effective algorithm frameworks were proposed by participating teams to address this issue in the SSVEP competition. The efficacy of the approaches employed by five teams in the final round is demonstrated in this study, and an overview of their methods is provided. Based on the final score, this paper presents a comparative analysis of five algorithms that propose distinct asynchronous recognition frameworks via diverse statistical methods to differentiate between intentional control state and non-control state based on dynamic window strategies. These algorithms achieve an impressive information transfer rate of 89.833 and a low false positive rate of 0.073. This study provides an overview of the algorithms employed by different teams to address asynchronous scenarios in SSVEP-based BCIs and identifies potential future avenues for research in this area.

Adaptation of Ocular Opponency Neurons Mediates Attention-Induced Ocular Dominance Plasticity.

Previous research has shown that ocular dominance can be biased by prolonged attention to one eye. The ocular-opponency-neuron model of binocular rivalry has been proposed as a candidate account for this phenomenon. Yet direct neural evidence is still lacking. By manipulating the contrast of dichoptic testing gratings, here we measured the steady-state visually evoked potentials (SSVEPs) at the intermodulation frequencies to selectively track the activities of ocular-opponency-neurons before and after the "dichoptic-backward-movie" adaptation. One hour of adaptation caused a shift of perceptual and neural ocular dominance towards the unattended eye. More importantly, we found a decrease in the intermodulation SSVEP response after adaptation, which was significantly greater when high-contrast gratings were presented to the attended eye than when they were presented to the unattended eye. These results strongly support the view that the adaptation of ocular-opponency-neurons contributes to the ocular dominance plasticity induced by prolonged eye-based attention.

Recognition of high-frequency steady-state visual evoked potential for brain-computer interface

Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). It would improve the comfort and safety of the system and has promising applications. However, most of the current advanced SSVEP decoding algorithms were compared and verified on low-frequency SSVEP datasets, and their recognition performance on high-frequency SSVEPs was still unknown. To address the aforementioned issue, electroencephalogram (EEG) data from 20 subjects were collected utilizing a high-frequency SSVEP paradigm. Then, the state-of-the-art SSVEP algorithms were compared, including 2 canonical correlation analysis algorithms, 3 task-related component analysis algorithms, and 1 task discriminant component analysis algorithm. The results indicated that they all could effectively decode high-frequency SSVEPs. Besides, there were differences in the classification performance and algorithms' speed under different conditions. This paper provides a basis for the selection of algorithms for high-frequency SSVEP-BCI, demonstrating its potential utility in developing user-friendly BCI.