SSVEP-based Brain-computer Interface Research Articles

An SSVEP-based BCI with 112 targets using frequency spatial multiplexing

Objective. Brain–computer interface (BCI) systems with large directly accessible instruction sets are one of the difficulties in BCI research. Research to achieve high target resolution ( ⩾ 100) has not yet entered a rapid development stage, which contradicts the application requirements. Steady-state visual evoked potential (SSVEP) based BCIs have an advantage in terms of the number of targets, but the competitive mechanism between the target stimulus and its neighboring stimuli is a key challenge that prevents the target resolution from being improved significantly. Approach. In this paper, we reverse the competitive mechanism and propose a frequency spatial multiplexing method to produce more targets with limited frequencies. In the proposed paradigm, we replicated each flicker stimulus as a 2 × 2 matrix and arrange the matrices of all frequencies in a tiled fashion to form the interaction interface. With different arrangements, we designed and tested three example paradigms with different layouts. Further we designed a graph neural network that distinguishes between targets of the same frequency by recognizing the different electroencephalography (EEG) response distribution patterns evoked by each target and its neighboring targets. Main results. Extensive experiment studies employing eleven subjects have been performed to verify the validity of the proposed method. The average classification accuracies in the offline validation experiments for the three paradigms are 89.16%, 91.38%, and 87.90%, with information transfer rates (ITR) of 51.66, 53.96, and 50.55 bits/min, respectively. Significance. This study utilized the positional relationship between stimuli and did not circumvent the competing response problem. Therefore, other state-of-the-art methods focusing on enhancing the efficiency of SSVEP detection can be used as a basis for the present method to achieve very promising improvements.

Control of the robotic arm system with an SSVEP-based BCI

Recent studies on brain–computer interfaces (BCIs) implemented in robotic systems have shown that the system’s effectiveness in assisting individuals with movement disorders to enhance their human–computer interaction skills. However, achieving precise and rapid online completion of tasks remains a challenge for manipulators with multiple degrees of freedom (DOFs). In this paper, we explore a time-sharing control strategy for studying motion control of a robotic arm based on steady-state visual evoked potentials. The signals are generated by the joint frequency-phase modulation method, analyzed with the filter-bank canonical correlation analysis algorithm, and identified to control the six-DOF robotic arm for task execution. The shared control strategy not only reduces user’s cognitive fatigue but also enhances system in practical environments. The use of high-frequency stimuli significantly improves user comfort, and hybrid coding increases the universality of the BCI system. Additionally, by setting multiple locations and actions randomly, the robotic arm can adaptively program the optimal path. The online results showed that BCI instructions of the proposed system could be accurately chosen from six options within 6.45 s. Subjects used an average of 12 commands for the robotic arm to achieve the proposed task with an average accuracy of 98.21%. These findings validate the feasibility and effectiveness of applying the system to robotic control. The control strategy proposed in this study exhibits versatility in controlling robots to perform various complex tasks across different domains.

Assessment of visual fatigue in SSVEP-based brain-computer interface: a comprehensive study.

Fatigue deteriorates the performance of a brain-computer interface (BCI) system; thus, reliable detection of fatigue is the first step to counter this problem. The fatigue evaluated by means of electroencephalographic (EEG) signals has been studied in many research projects, but widely different results have been reported. Moreover, there is scant research when considering the fatigue on steady-state visually evoked potential (SSVEP)-based BCI. Therefore, nowadays, fatigue detection is not a completely solved topic. In the current work, the issues found in the literature that led to the differences in the results are identified and saved by performing a new experiment on an SSVEP-based BCI system. The experiment was long enough to produce fatigue in the users, and different SSVEP stimulation ranges were used. Additionally, the EEG features commonly reported in the literature (EEG rhythms powers, SNR, etc.) were calculated as well as newly proposed features (spectral features and Lempel-Ziv complexity). The analysis was carried out on O1, Oz and O2 channels. This work found a tendency of displacement from high-frequency rhythms to low-frequency ones, and thus, better EEG features should present a similar behaviour. Then, the 'relative power' of EEG rhythms, the rates (θ + α)/β, α/β and θ/β, some spectral features (central and mean frequencies, asymmetry and kurtosis coefficients, etc.) and Lempel-Ziv complexity are proposed as reliable EEG features for fatigue detection. Hence, this set of features may be used to construct a more trustworthy fatigue index.

Attention-Focused Triggering Strategy for Dynamic Classification in SSVEP-Based Brain-Computer Interface

Attention-Focused Triggering Strategy for Dynamic Classification in SSVEP-Based Brain-Computer Interface

Dynamic decomposition graph convolutional neural network for SSVEP-based brain–computer interface

The SSVEP-based paradigm serves as a prevalent approach in the realm of brain–computer interface (BCI). However, the processing of multi-channel electroencephalogram (EEG) data introduces challenges due to its non-Euclidean characteristic, necessitating methodologies that account for inter-channel topological relations. In this paper, we introduce the Dynamic Decomposition Graph Convolutional Neural Network (DDGCNN) designed for the classification of SSVEP EEG signals. Our approach incorporates layerwise dynamic graphs to address the oversmoothing issue in Graph Convolutional Networks (GCNs), employing a dense connection mechanism to mitigate the gradient vanishing problem. Furthermore, we enhance the traditional linear transformation inherent in GCNs with graph dynamic fusion, thereby elevating feature extraction and adaptive aggregation capabilities. Our experimental results demonstrate the effectiveness of proposed approach in learning and extracting features from EEG topological structure. The results shown that DDGCNN outperforms other state-of-the-art (SOTA) algorithms reported on two datasets (Dataset 1: 54 subjects, 4 targets, 2 sessions; Dataset 2: 35 subjects, 40 targets). Additionally, we showcase the implementation of DDGCNN in the context of synchronized BCI robotic fish control. This work represents a significant advancement in the field of EEG signal processing for SSVEP-based BCIs. Our proposed method processes SSVEP time domain signals directly as an end-to-end system, making it easy to deploy. The code is available at https://github.com/zshubin/DDGCNN.

Optimizing SSVEP-based BCI training through Adversarial Generative Neural Networks

Brain-computer interfaces (BCIs) based on steady-state visually evoked potential (SSVEP) use brain activity to control external devices, with applications ranging from assistive technologies to gaming. Typically, BCI systems are developed using supervised learning techniques that require labelled brain signals. However, acquiring these labelled signals can be tiring and time-consuming, especially for subjects with disabilities. In this study, we evaluated the performance impact of using synthetic brain signals to train and calibrate an SSVEP-based BCI system. Specifically, we used generative adversarial networks (GANs) to synthesize brain signals with SSVEP information, considering cases with two and four visual stimuli. Four scenarios with different proportions of real vs. synthetic brain signals were evaluated: Scenario 1 (baseline) using only real data and Scenarios 2-4 with 10%, 20% and 30% of real data replaced by synthetic data, respectively. Our results reveal that synthetic data can be used to train the BCI without a performance loss across the tested scenarios when two visual stimuli are used and with an average performance reduction compared to baseline of 7% (Scenario 2), 10,3% (Scenario 3) and 9,3% (Scenario 4) for four stimuli. Furthermore, considering each recording has duration of 2 seconds, by replacing 30% of real data with synthetic data, there is an immediate time-saving of 48 s and 96 s in the cases with two and four visual stimuli, respectively. This trade-off between accuracy and efficiency has significant implications for improving the usability and accessibility of SSVEP-based BCI, especially for assistive applications.

A large database towards user-friendly SSVEP-based BCI

Background: Brain-computer interfaces (BCIs) have gained considerable attention for their potential in assisting individuals who have motor impairments with communication and rehabilitation. Among BCIs, steady-state visual evoked potential (SSVEP)-based systems have demonstrated high efficiency in interactive applications. However, ergonomic design challenges have limited their practical implementation in industrial settings. Issues such as visual and mental fatigue caused by flickering stimuli and the time-consuming preparation process hinder user adoption of such systems. Methods: To evaluate these BCI solutions, we introduced an open database comprising Electroencephalogram (EEG) data collected from 59 healthy volunteers using ergonomically designed semi-dry electrodes and grid stimuli. The database was acquired without electromagnetic shielding, and the preparation time for each participant was <5 min. A 40-target SSVEP speller system with cues was used in the experiment. Results: We validate the database by temporal and spectral analyzing methods. To further investigate the database, filter bank canonical correlation analysis (FBCCA), ensemble task-related component analysis (e-TRCA) and multi-stimulus task-related component analysis (msTRCA) were used for classification. The database can be downloaded from the following link: https://drive.google.com/drive/folders/1TXuxU863nZoniZRgNWZy0PRuL8lhBuP4?usp=sharing . Conclusions: This research contributes to enhancing the use of SSVEP-based BCIs in practical settings by addressing user experience and system design challenges. The proposed user-friendly visual stimuli and ergonomic electrode design improve comfort and usability. The open dataset serves as a valuable resource for future studies, enabling the development of robust and efficient SSVEP- BCI systems suitable for industrial applications.

Using oscillatory and aperiodic neural activity features for identifying idle state in SSVEP-based BCIs reduces false triggers

Objective. In existing studies, rhythmic (oscillatory) components were used as main features to identify brain states, such as control and idle states, while non-rhythmic (aperiodic) components were ignored. Recent studies have shown that aperiodic (1/f) activity is functionally related to cognitive processes. It is not clear if aperiodic activity can distinguish brain states in asynchronous brain–computer interfaces (BCIs) to reduce false triggers. In this paper, we propose an asynchronous method based on the fusion of oscillatory and aperiodic features for steady-state visual evoked potential-based BCIs. Approach. The proposed method first evaluates the oscillatory and aperiodic components of control and idle states using irregular-resampling auto-spectral analysis. Oscillatory features are then extracted using the spectral power of fundamental, second-harmonic, and third-harmonic frequencies of the oscillatory component, and aperiodic features are extracted using the slope and intercept of the first-order polynomial of the spectral fit of the aperiodic component under a log-logarithmic axis. The process produces two types of feature pools (oscillatory, aperiodic features). Next, feature selection (dimensionality reduction) is applied to the feature pools by Bonferroni corrected p-values from two-way analysis of variance. Last, these spatial-specific statistically significant features are used as input for classification to identify the idle state. Main results. On a 7-target dataset from 15 subjects, the mix of oscillatory and aperiodic features achieved an average accuracy of 88.39% compared to 83.53% when using oscillatory features alone (4.86% improvement). The results demonstrated that the proposed idle state recognition method achieved enhanced performance by incorporating aperiodic features. Significance. Our results demonstrated that (1) aperiodic features were effective in recognizing idle states and (2) fusing features of oscillatory and aperiodic components enhanced classification performance by 4.86% compared to oscillatory features alone.

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.

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.

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.

Multi-Brain Coding Expands the Instruction Set in SSVEP-Based Brain-Computer Interfaces

Previous studies have made great efforts to expand the instruction set in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces. However, most systems are limited to single persons and expand the instruction set by increasing the flicker stimulation frequency range or via multiple frequencies sequential coding or joint frequency/phase coding. In this article, we propose a multibrain coding SSVEP paradigm that encodes the SSVEP instructions generated by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> independent subjects with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> flicker stimuli, thus increasing the instruction set to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M^N</i> instructions. A total of 40 subjects participated in online experiments in this article. The results show that there is no significant difference in accuracy ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> > 0.05, paired t test) between the multibrain and single-brain coding systems, while the information transfer rate (ITR) increases significantly ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.0001, paired t test), with the ITR increasing from 37.66 ± 3.60 bits/min for the single-brain coding system to 111.65 ± 10.84 bits/min for multibrain coding system when <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> = 3 and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> = 5. In summary, the proposed multibrain coding paradigm exponentially increases the number of instructions without increasing the output instruction time, which is of great significance to the application and promotion of BCIs.

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.

Adaptive Spatial Filtering-based Component Exploration model for SSVEP-based Brain-Computer Interface for target identification

Adaptive Spatial Filtering-based Component Exploration model for SSVEP-based Brain-Computer Interface for target identification

A sub-region combination scheme for spatial coding in a high-frequency SSVEP-based BCI

Objective. In studying the spatial coding mechanism of visual evoked potentials, it is significant to construct a model that shows the relationship between steady-state visual evoked potential (SSVEP) responses to the local and global visual field stimulation. In order to investigate whether SSVEPs produced by sub-region stimulation can predict that produced by joint region stimulation, a sub-region combination scheme for spatial coding in a high-frequency SSVEP-based brain-computer interface (BCI) is developed innovatively. Approach. An annular visual field is divided equally into eight sub-regions. The 60 Hz visual stimuli in different sub-regions and joint regions are presented separately to participants. The SSVEP produced by the sub-region stimulation is superimposed to simulate the SSVEP produced by the joint region stimulation with different spatial combinations. A four-class spatially-coded BCI paradigm is used to evaluate the simulated classification performance, and the performance ranking of all simulated SSVEPs is obtained. Six representative stimulus patterns from two performance levels and three stimulus areas are applied to the online BCI system for each participant. Main results. The experimental result shows that the proposed scheme can implement a spatially-coded visual BCI system and realize satisfactory performance with imperceptible flicker. Offline analysis indicates that the classification accuracy and information transfer rate (ITR) are 89.69 ± 8.75% and 24.35 ± 7.09 bits min−1 with 3 s data length under the 3/8 stimulus area. The online BCI system reaches an average classification accuracy of 87.50 ± 9.13% with 3 s data length, resulting in an ITR of 22.48 ± 6.71 bits min−1 under the 3/8 stimulus area. Significance. This study proves the feasibility of using the sub-region’s response to predict the joint region’s response. It has the potential to extend to other frequency bands and lays a foundation for future research on more complex spatial coding methods.

Adaptive filters based efficient EEG classification for steady state visually evoked potential based BCI system

Brain-computer interfaces (BCIs) system is a link to generate a communication between disable people and physical devices. Thus, steady state visually evoked potential (SSVEP) is analysed to improve performance efficiency of BCIs system using multi-class classification process. Thus, an adaptive filtering-based component analysis (AFCA) method is adopted to examine SSVEP from multiple-channel electroencephalography (EEG) signals for BCIs system efficiency enhancement. Further, flickering at varied frequencies is used in a visual stimulation process to examine user intentions and brain responses. A detailed solution for optimization problem and efficient feature extraction is also presented. Here, a large SSVEP dataset is utilized which contains 256 channel EEG data. Experimental results are evaluated in terms of classification accuracy and information transfer rate to measure efficiency of proposed SSVEP extraction method against varied traditional SSVEP-based BCIs. The average information transfer rate (ITR) results are 308.23 bits per minute and classification accuracy is 93.48% using proposed AFCA method. Thus, proposed AFCA method shows decent performance in comparison with state-of-art-SSVEP extraction methods.

A transformer-based deep neural network model for SSVEP classification

Steady-state visual evoked potential (SSVEP) is one of the most commonly used control signals in the brain–computer interface (BCI) systems. However, the conventional spatial filtering methods for SSVEP classification highly depend on the subject-specific calibration data. The need for the methods that can alleviate the demand for the calibration data becomes urgent. In recent years, developing the methods that can work in inter-subject scenario has become a promising new direction. As a popular deep learning model nowadays, Transformer has been used in EEG signal classification tasks owing to its excellent performance. Therefore, in this study, we proposed a deep learning model for SSVEP classification based on Transformer architecture in inter-subject scenario, termed as SSVEPformer, which was the first application of Transformer on the SSVEP classification. Inspired by previous studies, we adopted the complex spectrum features of SSVEP data as the model input, which could enable the model to simultaneously explore the spectral and spatial information for classification. Furthermore, to fully utilize the harmonic information, an extended SSVEPformer based on the filter bank technology (FB-SSVEPformer) was proposed to improve the classification performance. Experiments were conducted using two open datasets (Dataset 1: 10 subjects, 12 targets; Dataset 2: 35 subjects, 40 targets). The experimental results show that the proposed models could achieve better results in terms of classification accuracy and information transfer rate than other baseline methods. The proposed models validate the feasibility of deep learning models based on Transformer architecture for SSVEP data classification, and could serve as potential models to alleviate the calibration procedure in the practical application of SSVEP-based BCI systems.

Effects of Background Music on Mental Fatigue in Steady-State Visually Evoked Potential-Based BCIs

As a widely used brain-computer interface (BCI) paradigm, steady-state visually evoked potential (SSVEP)-based BCIs have the advantages of high information transfer rates, high tolerance for artifacts, and robust performance across diverse users. However, the incidence of mental fatigue from prolonged, repetitive stimulation is a critical issue for SSVEP-based BCIs. Music is often used as a convenient, non-invasive means of relieving mental fatigue. This study investigates the compensatory effect of music on mental fatigue through the introduction of different modes of background music in long-duration, SSVEP-BCI tasks. Changes in electroencephalography power index, SSVEP amplitude, and signal-to-noise ratio were used to assess participants' mental fatigue. The study's results show that the introduction of exciting background music to the SSVEP-BCI task was effective in relieving participants' mental fatigue. In addition, for continuous SSVEP-BCI tasks, a combination of musical modes that used soothing background music during the rest interval phase proved more effective in reducing users' mental fatigue. This suggests that background music can provide a practical solution for long-duration SSVEP-based BCI implementation.

A new grid stimulus with subtle flicker perception for user-friendly SSVEP-based BCIs

Objective. The traditional uniform flickering stimulation pattern shows strong steady-state visual evoked potential (SSVEP) responses and poor user experience with intense flicker perception. To achieve a balance between performance and comfort in SSVEP-based brain–computer interface (BCI) systems, this study proposed a new grid stimulation pattern with reduced stimulation area and low spatial contrast. Approach. A spatial contrast scanning experiment was conducted first to clarify the relationship between the SSVEP characteristics and the signs and values of spatial contrast. Four stimulation patterns were involved in the experiment: the ON and OFF grid stimulation patterns that separately activated the positive or negative contrast information processing pathways, the ON–OFF grid stimulation pattern that simultaneously activated both pathways, and the uniform flickering stimulation pattern that served as a control group. The contrast-intensity and contrast-user experience curves were obtained for each stimulation pattern. Accordingly, the optimized stimulation schemes with low spatial contrast (the ON-50% grid stimulus, the OFF-50% grid stimulus, and the Flicker-30% stimulus) were applied in a 12-target and a 40-target BCI speller and compared with the traditional uniform flickering stimulus (the Flicker-500% stimulus) in the evaluation of BCI performance and subjective experience. Main results. The OFF-50% grid stimulus showed comparable online performance (12-target, 2 s: 69.87 ± 0.74 vs. 69.76 ± 0.58 bits min−1, 40-target, 4 s: 57.02 ± 2.53 vs. 60.79 ± 1.08 bits min−1) and improved user experience (better comfortable level, weaker flicker perception and higher preference level) compared to the traditional Flicker-500% stimulus in both multi-targets BCI spellers. Significance. Selective activation of the negative contrast information processing pathway using the new OFF-50% grid stimulus evoked robust SSVEP responses. On this basis, high-performance and user-friendly SSVEP-based BCIs have been developed and implemented, which has important theoretical significance and application value in promoting the development of the visual BCI technology.

An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey.

The brain-computer interface (BCI), which provides a new way for humans to directly communicate with robots without the involvement of the peripheral nervous system, has recently attracted much attention. Among all the BCI paradigms, BCIs based on steady-state visual evoked potentials (SSVEPs) have the highest information transfer rate (ITR) and the shortest training time. Meanwhile, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, and many researchers have started to apply deep learning to classify SSVEP signals. However, the designs of deep learning models vary drastically. There are many hyper-parameters that influence the performance of the model in an unpredictable way. This study surveyed 31 deep learning models (2011-2023) that were used to classify SSVEP signals and analyzed their design aspects including model input, model structure, performance measure, etc. Most of the studies that were surveyed in this paper were published in 2021 and 2022. This survey is an up-to-date design guide for researchers who are interested in using deep learning models to classify SSVEP signals.