SSVEP-based Based Brain-computer Interface Research Articles

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

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

An open dataset for human SSVEPs in the frequency range of 1-60 Hz

A steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system relies on the photic driving response to effectively elicit characteristic electroencephalogram (EEG) signals. However, traditional visual stimuli mainly adopt high-contrast black-and-white flickering stimulations, which are easy to cause visual fatigue. This paper presents an SSVEP dataset acquired at a wide frequency range from 1 to 60 Hz with an interval of 1 Hz using flickering stimuli under two different modulation depths. This dataset contains 64-channel EEG data from 30 healthy subjects when they fixated on a single flickering stimulus. The stimulus was rendered on an LCD display with a refresh rate of 240 Hz. Initially, the dataset was rigorously validated through comprehensive data analysis to investigate SSVEP responses and user experiences. Subsequently, BCI performance was evaluated through offline simulations of frequency-coded and phase-coded BCI paradigms. This dataset provides comprehensive and high-quality data for studying and developing SSVEP-based BCI systems.

SSVEP-DAN: Cross-Domain Data Alignment for SSVEP-Based Brain-Computer Interfaces.

Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency is highly dependent on individual training data acquired during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we introduce SSVEP-DAN, the first dedicated neural network model designed to align SSVEP data across different domains, encompassing various sessions, subjects, or devices. Our experimental results demonstrate the ability of SSVEP-DAN to transform existing source SSVEP data into supplementary calibration data. This results in a significant improvement in SSVEP decoding accuracy while reducing the calibration time. We envision SSVEP-DAN playing a crucial role in future applications of high-performance SSVEP-based BCIs. The source code for this work is available at: https://github.com/CECNL/SSVEP-DAN.

Optimizing Visual Stimulation Paradigms for User-Friendly SSVEP-Based BCIs.

In steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems, traditional flickering stimulation patterns face challenges in achieving a trade-off in both BCI performance and visual comfort across various frequency bands. To investigate the optimal stimulation paradigms with high performance and high comfort for each frequency band, this study systematically compared the characteristics of SSVEP and user experience of different stimulation paradigms with a wide stimulation frequency range of 1-60 Hz. The findings suggest that, for a better balance between system performance and user experience, ON and OFF grid stimuli with a Weber contrast of 50% can be utilized as alternatives to traditional flickering stimulation paradigms in the frequency band of 1-25 Hz. In the 25-35 Hz range, uniform flicker stimuli with the same 50% contrast are more suitable. In the higher frequency band, traditional uniform flicker stimuli with a high 300% contrast are preferred. These results are significant for developing high performance and user-friendly SSVEP-based BCI systems.

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.

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.

Data augmentation of SSVEPs using source aliasing matrix estimation for brain-computer interfaces.

Currently, ensemble task-related component analysis (eTRCA) and task discriminative component analysis (TDCA) are the state-of-the-art algorithms for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, training the BCIs requires multiple calibration trials. With insufficient calibration data, the accuracy of the BCI will degrade, or even become invalid with only one calibration trial. However, collecting a large amount of electroencephalography (EEG) data for calibration is a time-consuming and laborious process, which hinders the practical use of eTRCA and TDCA. This study proposed a novel method, namely Source Aliasing Matrix Estimation (SAME), to augment the calibration data for SSVEP-BCIs. SAME could generate artificial EEG trials with the featured SSVEPs. Its effectiveness was evaluated using two public datasets (i.e., Benchmark, BETA). When combined with SAME, both eTRCA and TDCA had significantly improved performance with a limited number of calibration data. Specifically, SAME increased the average accuracy of eTRCA and TDCA by about 12% and 3%, respectively, with as few as two calibration trials. Notably, SAME enabled eTRCA and TDCA to work well with a single calibration trial, achieving an average accuracy >90% for the Benchmark dataset and >70% for the BETA dataset with 1-second EEG. SAME is an effective method for SSVEP-BCIs to augment the calibration data, thereby significantly enhancing the performance of eTRCA and TDCA. We propose a new data-augmentation method that is compatible with the state-of-the-art algorithms of SSVEP-based BCIs. It can significantly reduce the efforts required to calibrate SSVEP-BCIs, which is promising for the development of practical BCIs.

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology.

The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (n = 21/30). The second category, recurrent neural network (RNN), accounts for 10% (n = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (n = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (n = 1/30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.

Block Distributed Joint Temporal-Frequency-Phase Modulation for Steady-State Visual Evoked Potential Based Brain-Computer Interface With a Limited Number of Frequencies.

How to encode as many targets as possible with limited frequency resources is a grave problem that restricts the application of steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). In the current study, we propose a novel block-distributed joint temporal-frequency-phase modulation method for a virtual speller based on SSVEP-based BCI. A 48-target speller keyboard array is virtually divided into eight blocks and each block contains six targets. The coding cycle consists of two sessions: in the first session, each block flashes at different frequencies while all the targets in the same block flicker at the same frequency; in the second session, all the targets in the same block flash at different frequencies. Using this method, 48 targets can be coded with only eight frequencies, which greatly reduces the frequency resources required, and average accuracies of 86.81 ±9.41% and 91.36 ±6.41% were obtained for both the offline and online experiments. This study provides a new coding approach for a large number of targets with a small number of frequencies, which can further expand the application potential of SSVEP-based 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.

Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-based BCIs.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been substantially studied in recent years due to their fast communication rate and high signal-to-noise ratio. The transfer learning is typically utilized to improve the performance of SSVEP-based BCIs with auxiliary data from the source domain. This study proposed an inter-subject transfer learning method for enhancing SSVEP recognition performance through transferred templates and transferred spatial filters. In our method, the spatial filter was trained via multiple covariance maximization to extract SSVEP-related information. The relationships between the training trial, the individual template, and the artificially constructed reference are involved in the training process. The spatial filters are applied to the above templates to form two new transferred templates, and the transferred spatial filters are obtained accordingly via the least-square regression. The contribution scores of different source subjects can be calculated based on the distance between the source subject and the target subject. Finally, a four-dimensional feature vector is constructed for SSVEP detection. To demonstrate the effectiveness of the proposed method, a publicly available dataset and a self-collected dataset were employed for performance evaluation. The extensive experimental results validated the feasibility of the proposed method for improving SSVEP detection.

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.

Adaptive Window Method Based on FBCCA for Optimal SSVEP Recognition.

In the conventional studies related to steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the window length (detection time) was typically predetermined through the offline analysis, which had limitations of practical applicability of a BCI system due to the inter-subject/trial variability of electroencephalography (EEG) signals. To address these limitations, this study aims to automatically optimize the window length for each trial based on training-free approaches and proposes a novel adaptive window method (ANCOVA-based filter-bank canonical correlation analysis, ABFCCA) for SSVEP-based BCIs. The proposed method is based on analysis of covariance (ANCOVA) which is applied after feature extraction by the conventional training-free SSVEP recognition approaches. To evaluate the performance of the proposed method, conventional fixed window and recent adaptive window methods were compared using two open-access datasets. In the Benchmark dataset, the average information transfer rate (ITR) was 146.81 bits/min, the average accuracy 93.55%, and the average window length 1.53 s. In the OpenBMI dataset, the average ITR was 119.01 bits/min, the average accuracy 83.50%, and the average window length 0.65 s. The proposed method significantly outperformed the conventional approaches with fixed window in terms of the accuracy and ITR, and is applicable to various SSVEP-based BCI paradigms based on the criterion of significance level without offline analysis to find optimal hyper-parameters. ABFCCA is enabled the practical use of various BCI systems by automatically optimizing the window length independently.

SSVEP-based Brain-Computer Interface Controlled Robotic Platform with Velocity Modulation.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been extensively studied due to many benefits, such as non-invasiveness, high information transfer rate, and ease of use. SSVEP-based BCI has been investigated in various applications by projecting brain signals to robot control commands. However, the movement direction and speed are generally fixed and prescribed, neglecting the user's requirement for velocity changes during practical implementations. In this study, we proposed a velocity modulation method based on stimulus brightness for controlling the robotic arm in the SSVEP-based BCI system. A stimulation interface was designed, incorporating flickers, target and a cursor workspace. The synchronization of the cursor and robotic arm does not require the subject's eye switch between the stimuli and the robot. The feature vector consists of the characteristics of the signal and the classification result. Subsequently, the Gaussian mixture model (GMM) and Bayesian inference were used to calculate the posterior probabilities that the signal came from a high or low brightness flicker. A brain-actuated speed function was designed by incorporating the posterior probability difference. Finally, the historical velocity was considered to determine the final velocity. To demonstrate the effectiveness of the proposed method, online experiments, including single- and multi-target reaching tasks, were conducted. The extensive experimental results validated the feasibility of the proposed method in reducing reaching time and achieving proximity to the target.

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

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

A Novel SSVEP Brain-Computer Interface System Based on Simultaneous Modulation of Luminance and Motion.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have received significant attention owing to their high information transfer rate (ITR) and low training requirements. Previous SSVEP-based BCIs mostly adopt the stationary visual flickers where only a few studies have explored the effect of moving visual flickers on the SSVEP-BCI. In this study, a novel stimulus encoding method based on the simultaneous modulation of luminance and motion was proposed. We adopted the sampled sinusoidal stimulation method to encode the frequencies and phases of stimulus targets. In addition to luminance modulation, at the same time, visual flickers also moved horizontally towards right and left at different frequencies (i.e., 0, 0.2, 0.4, and 0.6 Hz) following a sinusoidal function. Accordingly, a nine-target SSVEP-BCI was built to evaluate the influence of motion modulation on the BCI performance. Filter bank canonical correlation analysis (FBCCA) approach was adopted to identify the stimulus targets. Offline experimental results of 17 subjects revealed that the system performance decreased with the increase of superimposed horizontal periodic motion frequency. Our online experimental results showed that the subjects achieved 85.00 ± 6.77 % and 83.15 ± 9.88 % accuracy for the superimposed horizontal periodic motion frequencies of 0 and 0.2 Hz, respectively. These results verified the feasibility of the proposed systems. In addition, the system with 0.2 Hz horizontal motion frequency provided the best visual experience for subjects. These results indicated that moving visual stimulus can provide an alternative option for SSVEP-BCIs. Furthermore, the proposed paradigm is expected to develop a more comfortable BCI system.

Improving Generalized Zero-Shot Learning SSVEP Classification Performance From Data-Efficient Perspective.

Generalized zero-shot learning (GZSL) has significantly reduced the training requirements for steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). Traditional methods require complete class data sets for training, but GZSL allows for only partial class data sets, dividing them into 'seen' (those with training data) and 'unseen' classes (those without training data). However, inefficient utilization of SSVEP data limits the accuracy and information transfer rate (ITR) of existing GZSL methods. To this end, we proposed a framework for more effective utilization of SSVEP data at three systematically combined levels: data acquisition, feature extraction, and decision-making. First, prevalent SSVEP-based BCIs overlook the inter-subject variance in visual latency and employ fixed sampling starting time (SST). We introduced a dynamic sampling starting time (DSST) strategy at the data acquisition level. This strategy uses the classification results on the validation set to find the optimal sampling starting time (OSST) for each subject. In addition, we developed a Transformer structure to capture the global information of input data for compensating the small receptive field of existing networks. The global receptive fields of the Transformer can adequately process the information from longer input sequences. For the decision-making level, we designed a classifier selection strategy that can automatically select the optimal classifier for the seen and unseen classes, respectively. We also proposed a training procedure to make the above solutions in conjunction with each other. Our method was validated on three public datasets and outperformed the state-of-the-art (SOTA) methods. Crucially, we also outperformed the representative methods that require training data for all classes.

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.

SSVEP-Based Emotion Recognition for IoT via Multiobjective Neural Architecture Search

Emotion recognition is one of the significant research areas and applications of electroencephalography (EEG)-based brain–computer interface (BCI), which is widely concerned in the Internet of Things (IoT) framework. Steady-state visual evoked potential (SSVEP) is frequently used in EEG-based BCI (EEG-BCI) due to its high signal-to-noise ratio and short response time. However, there are few related studies and applications on the emotional features of SSVEP. In this article, we build an SSVEP-based BCI (SSVEP-BCI) for affective computing in the IoT system and utilize neural architecture search (NAS) to analyze the emotional information of SSVEP signals. Our proposed NAS is capable of optimizing the classification performance and model size of the deep neural networks simultaneously, thus enhancing the sentiment analysis accuracy for SSVEP and improving the response speed of the actual BCI system for IoT implementation. Furthermore, we employ network morphism and Bayesian optimization in the offspring generation algorithm of the NAS framework to apply knowledge inheritance and performance estimation for the child models, respectively. The experimental results show that the proposed NAS outperforms the baseline models in terms of accuracy and model size for valence and arousal dimensions. Moreover, the offspring generation algorithm is able to promote the search efficiency of NAS.

Novel hybrid visual stimuli incorporating periodic motions into conventional flickering or pattern-reversal visual stimuli for steady-state visual evoked potential-based brain-computer interfaces.

In this study, we proposed a new type of hybrid visual stimuli for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), which incorporate various periodic motions into conventional flickering stimuli (FS) or pattern reversal stimuli (PRS). Furthermore, we investigated optimal periodic motions for each FS and PRS to enhance the performance of SSVEP-based BCIs. Periodic motions were implemented by changing the size of the stimulus according to four different temporal functions denoted by none, square, triangular, and sine, yielding a total of eight hybrid visual stimuli. Additionally, we developed the extended version of filter bank canonical correlation analysis (FBCCA), which is a state-of-the-art training-free classification algorithm for SSVEP-based BCIs, to enhance the classification accuracy for PRS-based hybrid visual stimuli. Twenty healthy individuals participated in the SSVEP-based BCI experiment to discriminate four visual stimuli with different frequencies. An average classification accuracy and information transfer rate (ITR) were evaluated to compare the performances of SSVEP-based BCIs for different hybrid visual stimuli. Additionally, the user's visual fatigue for each of the hybrid visual stimuli was also evaluated. As the result, for FS, the highest performances were reported when the periodic motion of the sine waveform was incorporated for all window sizes except for 3 s. For PRS, the periodic motion of the square waveform showed the highest classification accuracies for all tested window sizes. A significant statistical difference in the performance between the two best stimuli was not observed. The averaged fatigue scores were reported to be 5.3 ± 2.05 and 4.05 ± 1.28 for FS with sine-wave periodic motion and PRS with square-wave periodic motion, respectively. Consequently, our results demonstrated that FS with sine-wave periodic motion and PRS with square-wave periodic motion could effectively improve the BCI performances compared to conventional FS and PRS. In addition, thanks to its low visual fatigue, PRS with square-wave periodic motion can be regarded as the most appropriate visual stimulus for the long-term use of SSVEP-based BCIs, particularly for window sizes equal to or larger than 2 s.