SSVEP-based Brain-computer Interface System Research Articles

A Novel Filter Bank and Fourier Transform Convolutional Neural Network for SSVEP Classification

Abstract. Classifying Steady-State Visual Evoked Potentials (SSVEPs) is crucial for enhancing the performance of Brain-Computer Interface (BCI) systems. This study introduces a new method for SSVEP classification within BCI frameworks, called the Filter Bank and Fourier Transform Convolutional Neural Network (FBFCNN). The FBFCNN model effectively extracts key harmonic features by first segmenting SSVEP signals into multiple sub-bands through a filter bank. Subsequently, the Fast Fourier Transform (FFT) is applied to convert these signals from the time domain into the frequency domain. The generated spectrum's real and imaginary components are then fed into a convolutional neural network (CNN). This method improves SSVEP feature extraction and increases classification accuracy by fusing the advantages of CNNs with filter banks. Experimental results, derived from publicly available SSVEP datasets, show that the FBFCNN model surpasses traditional techniques in both accuracy and Information Transfer Rate (ITR). Offering a reliable and efficient solution for real-time brain signal decoding, the FBFCNN model represents a significant advancement in SSVEP-based BCI systems.

The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked potential

Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a "third hand", offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.

An improved SSVEP-based brain-computer interface with low-contrast visual stimulation and its application in UAV control.

Efficient communication and regulation are crucial for advancing brain-computer interfaces (BCIs), with the steady-state visual-evoked potential (SSVEP) paradigm demonstrating high accuracy and information transfer rates. However, the conventional SSVEP paradigm encounters challenges related to visual occlusion and fatigue. In this study, we propose an improved SSVEP paradigm that addresses these issues by lowering the contrast of visual stimulation. The improved paradigms outperform the traditional paradigm in the experiments, significantly reducing the visual stimulation of the SSVEP paradigm. Furthermore, we apply this enhanced paradigm to a BCI navigation system, enabling two-dimensional navigation of unmanned aerial vehicles (UAVs) through a first-person perspective. Experimental results indicate the enhanced SSVEP-based BCI system's accuracy in performing navigation and search tasks. Our findings highlight the feasibility of the enhanced SSVEP paradigm in mitigating visual occlusion and fatigue issues, presenting a more intuitive and natural approach for BCIs to control external equipment.NEW & NOTEWORTHY In this article, we proposed an improved steady-state visual-evoked potential (SSVEP) paradigm and constructed an SSVEP-based brain-computer interface (BCI) system to navigate the unmanned aerial vehicle (UAV) in two-dimensional (2-D) physical space. We proposed a modified method for evaluating visual fatigue including subjective score and objective indices. The results indicated that the improved SSVEP paradigm could effectively reduce visual fatigue while maintaining high accuracy.

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.

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 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.

A survey on encoding and decoding technology of non-invasive brain-computer interface

A survey on encoding and decoding technology of non-invasive brain-computer interface

A Hybrid Method Fusing Frequency Recognition With Attention Detection to Enhance an Asynchronous Brain-Computer Interface.

One critical problem in controlling an asynchronous brain-computer interface (BCI) system is to discriminate between control and idle states. This paper proposes a hybrid attention detection and frequency recognition method based on weighted Dempster-Shafer theory (ADFR-DS), which integrates information of different aspects of the task from two brain regions, to enhance asynchronous control performance of a steady-state visual evoked potential (SSVEP)-based BCI system. The ADFR-DS method utilizes a hybrid architecture to process electroencephalogram (EEG) data from different channels simultaneously: an individualized frequency band based optimized complex network (IFBOCN) algorithm processes neural activity from the prefrontal area for attention detection, and an ensemble task-related component analysis (eTRCA) algorithm processes data from the occipital area for frequency recognition. The ADFR-DS method then fuses their classification results at decision level to generate the final output of the BCI system. A novel weighted Dempster-Shafer fusion method was proposed to enhance the fusion performance. This study evaluated the proposed method using a 40-target dataset recorded from 35 participants. The proposed method outperformed the eTRCA algorithm in the true positive rate (TPR), true negative rate (TNR), accuracy (ACC) and information transfer rate (ITR). Specifically, ADFR-DS improved the average ACC of eTRCA from 62.71% to 69.30%, and improved the average ITR from 184.28 bits/min to 216.89 bits/min (data length 0.3 s). The results suggest that the proposed ADFR-DS method can enhance asynchronous SSVEP-based BCI systems.

Bayesian-Based Classification Confidence Estimation for Enhancing SSVEP Detection

The brain-computer interface (BCI) enables paralyzed people to directly communicate with and operate peripheral equipment. The steady-state visual evoked potential (SSVEP)-based BCI system has been extensively investigated in recent years due to its fast communication rate and high signal-to-noise ratio. Many present SSVEP recognition methods determine the target class via finding the largest correlation coefficient. However, the classification performance usually degrades when the largest coefficient is not significantly different from the rest of the values. This study proposed a Bayesian-based classification confidence estimation method to enhance the target recognition performance of SSVEP-based BCI systems. In our method, the differences between the largest and the other values generated by a basic target identification method are used to define a feature vector during the training process. The Gaussian mixture model (GMM) is then employed to estimate the probability density functions of feature vectors for both correct and wrong classifications. Subsequently, the posterior probabilities of being an accurate and false classification are calculated via Bayesian inference in the test procedure. A classification confidence value (CCValue) is presented based on two posterior probabilities to estimate the classification confidence. Finally, the decision-making rule can determine whether the present classification result should be accepted or rejected. Extensive evaluation studies were performed on an open-access benchmark dataset and a self-collected dataset. The experimental results demonstrated the effectiveness and feasibility of the proposed method for improving the reliability of SSVEP-based BCI systems.

A Generalized Zero-Shot Learning Scheme for SSVEP-Based BCI System.

The steady-state visual evoked potential (SSVEP) has been widely used in building multi-target brain-computer interfaces (BCIs) based on electroencephalogram (EEG). However, methods for high-accuracy SSVEP systems require training data for each target, which needs significant calibration time. This study aimed to use the data of only part of the targets for training while achieving high classification accuracy on all targets. In this work, we proposed a generalized zero-shot learning (GZSL) scheme for SSVEP classification. We divided the target classes into seen and unseen classes and trained the classifier only using the seen classes. During the test time, the search space contained both seen classes and unseen classes. In the proposed scheme, the EEG data and the sine waves are embedded into the same latent space using convolutional neural networks (CNN). We use the correlation coefficient of the two outputs in the latent space for classification. Our method was tested on two public datasets and reached 89.9% of the classification accuracy of the state-of-the-art (SOTA) data-driven method, which needs the training data of all targets. Compared to the SOTA training-free method, our method achieved a multifold improvement. This work shows that it is promising to build an SSVEP classification system that does not need the training data of all targets.

Improving the Classification of Real-World SSVEP Data in Brain-Computer Interface Speller Systems Using Deep Convolutional Neural Networks

Purpose: Brain-Computer Interface (BCI) Speller systems help people with mobility impairments improve their cognitive and physical abilities. Steady-State Visual Evoked Potential (SSVEP) signals have been used to build high-speed BCI speller systems. SSVEP signals are a subtype of Visual Evoked Potential (VEP), a form of co-frequency, and the harmonics response elicited by a specific frequency stimulus. Noise and artifacts are critical issues for target detection in SSVEP-based BCI systems.
 Materials and Methods: Thus, it is essential to provide target detection techniques that operate well in the presence of noises. One solution for overcoming the noise impact is to employ approaches that automatically extract the appropriate features for target detection from the training data. Deep Convolutional Neural Network (DCNN) was utilized in this study to automatically extract features from SSVEP data in noisy conditions. Moreover, the BETA database, which contains SSVEP data from 70 individuals collected outside of the electromagnetic shielding room, was used. In this regard, a suitable DCNN structure for target stimulus frequency identification was first designed. The network was pre-trained with part of the data from the BETA database. Finally, at the single-subject level, this pre-trained network was retrained and evaluated.
 Results: The results showed that after retraining, the accuracy and Information Transfer Rate (ITR) increased (p-value < 0.01) for all participants.
 Conclusion:.The enhancement in accuracy and ITR are 25.72% and 43.10 bpm, respectively.

Overview of the winning approaches in BCI Controlled Robot Contest in World Robot Contest 2021: Calibration-free SSVEP

Recently, steady-state visual evoked potential (SSVEP) has become one of the most popular electroencephalography paradigms due to its high information transfer rate. Several approaches have been proposed to improve the performance of SSVEP. The calibration-free scenario is significant in SSVEP-based brain–computer interface systems, where the subject is the first time to use the system. The participating teams proposed several effective calibration-free algorithm frameworks in the SSVEP competition (calibration-free) of the BCI Controlled Robot Contest in World Robot Contest 2021. This paper introduces the approaches used in the algorithms of the top five teams in the final. The results of the five subjects in the final proved the effectiveness of the approaches. This paper discusses the effectiveness of each approach in improving the system performance in the calibration-free scenario and gives suggestions on how to use these approaches in a real-world system.

Investigating the Effect of Flickering Frequency Pair and Mother Wavelet Selection in Steady-State Visually-Evoked Potentials on Two-Command Brain-Computer Interfaces

IntroductionSteady-state visually evoked potentials (SSVEPs) have become popular in brain-computer interface (BCI) applications in addition to many other applications on clinical neuroscience (neurodegenerative disorders, schizophrenia, epilepsy, etc.), cognitive (visual attention, working memory, brain rhythms, etc.), and use of engineering researches. Among available methods to measure brain activities, SSVEPs have advantages like higher information transfer rate, simplicity in structure, and short training time. SSVEP-based BCIs use flickering stimuli at different frequencies to discriminate distinct commands in real life. Some features are extracted from the SSVEP signals before these commands are classified. The wavelet transform (WT) has attracted researchers among feature extraction methods since it utilizes the non-stationary signals well. In the WT, a sample function (named mother wavelet) represents the SSVEP signal in both time and frequency domains. Unfortunately, there is no universal mother wavelet function that fits all the signals. Therefore, choosing an appropriate mother wavelet function may be a challenge in WT-related studies. Although there are such studies in three- and seven-command SSVEP-based studies, there is no study for two-command systems in our knowledge. Materials and MethodsIn this study, two user commands flickered at the combinations of seven different frequencies were tested to determine which frequency pairs give the highest performance. For this purpose, three well-known wavelet features (energy, entropy, and variance) were calculated for each of derived EEG frequency bands from the discrete WT coefficients of SSVEP signals. The WT was repeated for six different mother wavelet functions (Haar, Db4, Sym4, Coif1, Bior3.5, and Rbior2.8). Then, four feature sets (every three features, and all together) were applied to seven commonly-used machine learning algorithms (Decision Tree, Discriminant Analysis, Logistic Regression, Naive Bayes, Support Vector Machines, Nearest Neighbors, and Ensemble Classifiers). Results and DiscussionWe achieved 100% accuracies among these 3,528 runs (7 classifiers x 4 feature sets x 6 mother wavelets x 21 flickering frequency pairs) using the mother wavelet function of Haar and the Ensemble Learner classifier. The highest classifier performances are 100% when two commands have the flickering frequency pairs of (6.0 and 10 Hz), (6.5 and 8.2 Hz), or (6.5 and 10.0 Hz). ConclusionWe obtained three main outcomes from this study. First, the most representative mother wavelet function was Haar, while the worst one was Symlet 4. Second, the Ensemble Learner classifier gave the maximum classifier performance in a two-command SSVEP-based BCI system. Besides, two user commands from SSVEP should be one of the frequency pairs of (6.0 and 10.0 Hz), (6.5 and 8.2 Hz), and (6.5 and 10.0 Hz) to achieve the maximum accuracy.

A classification algorithm of an SSVEP brain-Computer interface based on CCA fusion wavelet coefficients

BackgroundIn the study of brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs), how to improve the classification accuracies of BCIs has always been the focus of researchers. Canonical correlation analysis (CCA) is widely used in BCI systems of SSVEPs because of its rapidity and scalability. However, the classical CCA algorithm always encounters the difficulty of low accuracy in a short time. New methodFor targetless stimuli, this paper proposes a fusion algorithm (CCA-CWT-SVM) that is combined with CCA, a continuous wavelet transform, and a support vector machine (SVM) to improve the low classification accuracies when a single feature extraction method is used. ResultsThis fusion algorithm achieves high accuracies and information transfer rates (ITRs) in the SSVEP paradigm with few targets. Comparison with existing methods and conclusionsThrough the study of 400 groups of experimental data from 10 subjects, the results show that CCA-CWT-SVM has a classification accuracy of 91.76% within 2 s and an ITR of 48.92 bits/min, which are 10.88% and 13.18 bits/min higher than those of the standard CCA. Compared with a mainstream EEG decoding algorithm, filter bank canonical correlation analysis (FBCCA), the classification accuracy and ITR of the CCA-CWT-SVM algorithm also improved (4.45% and 5.69 bit/min, respectively). Using a dataset from Tsinghua University (THU), we also showed that the fusion algorithm is better than the classical algorithms. The CCA-CWT-SVM algorithm obtained an 89.1% accuracy and a 39.91 bit/min ITR in a time window of 2 s. The results were significantly improved compared with those of CCA and the FBCCA (CCA: 79.44% and 28.23 bits/min, FBCCA: 84.03% and 33.4 bits/min). Hence, this work provides an experimental basis for designing an SSVEP-based BCI system with a high task classification accuracy in some crucial biomedical applications.

Oscillatory Source Tensor Discriminant Analysis (OSTDA): A regularized tensor pipeline for SSVEP-based BCI systems

Periodic signals called Steady-State Visual Evoked Potentials (SSVEP) are elicited in the brain by flickering stimuli. They are usually detected by means of regression techniques that need relatively long trial lengths to provide feedback and/or sufficient number of calibration trials to be reliably estimated in the context of brain-computer interface (BCI). Thus, for BCI systems designed to operate with SSVEP signals, reliability is achieved at the expense of speed or extra recording time. Furthermore, regardless of the trial length, calibration free regression-based methods have been shown to suffer from significant performance drops when cognitive perturbations are present affecting the attention to the flickering stimuli. In this study we present a novel technique called Oscillatory Source Tensor Discriminant Analysis (OSTDA) that extracts oscillatory sources and classifies them using the newly developed tensor-based discriminant analysis with shrinkage. The proposed approach is robust for small sample size settings where only a few calibration trials are available. Besides, it works well with both low- and high-number-of-channel settings, using trials as short as one second. OSTDA performs similarly or significantly better than other three benchmarked state-of-the-art techniques under different experimental settings, including those with cognitive disturbances (i.e. four datasets with control, listening, speaking and thinking conditions). Overall, in this paper we show that OSTDA is the only pipeline among all the studied ones that can achieve optimal results in all analyzed conditions.

Durağan-Durum Görsel Uyarılmış Potansiyellere Dayalı Beyin-Bilgisayar Arayüzünün Zamansal ve Frekanssal Derinliğinden Frekans Tanıma

Brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) have been acceleratingly used in different application areas from entertainment to rehabilitation, like clinical neuroscience, cognitive, and use of engineering researches. Of various electroencephalography paradigms, SSVEP-based BCI systems enable apoplectic people to communicate with outside world easily, due to their simple system structure, short or no training time, high temporal resolution, high information transfer rate, and affordable by comparing to other methods. SSVEP-based BCIs use multiple visual stimuli flickering at different frequencies to generate distinct commands. In this paper, we compared the classifier performances of combinations of binary commands flickering at seven different frequencies to determine which frequency pair gives the highest performance using temporal and spectral methods. For SSVEP frequency recognition, in total 25 temporal change characteristics of the signals and 15 frequency-based feature vectors extracted from the SSVEP signal. These feature vectors were applied to the input of seven well-known machine learning algorithms (Decision Tree, Discriminant Analysis, Logistic Regression, Naive Bayes, Support Vector Machines, Nearest Neighbour, and Ensemble Learning). In conclusion, we achieved 100% accuracy in 7.5 - 10 frequency pairs among these 2,520 distinct runs and we found that the most successful classifier is the Ensemble Learning classifier. The combination of these methods leads to an appropriate detailed and comparative analysis that represents the robustness and effectiveness of classical approaches.

Enhancing detection of steady-state visual evoked potentials using channel ensemble method

Objective. This study proposed and evaluated a channel ensemble approach to enhance detection of steady-state visual evoked potentials (SSVEPs). Approach. Collected multi-channel electroencephalogram signals were classified into multiple groups of new analysis signals based on correlation analysis, and each group of analysis signals contained signals from a different number of electrode channels. These groups of analysis signals were used as the input of a training-free feature extraction model, and the obtained feature coefficients were converted into feature probability values using the softmax function. The ensemble value of multiple sets of feature probability values was determined and used as the final discrimination coefficient. Main results. Compared with canonical correlation analysis, likelihood ratio test, and multivariate synchronization index analysis methods using a standard approach, the recognition accuracies of the methods using a channel ensemble approach were improved by 5.05%, 3.87%, and 3.42%, and the information transfer rates (ITRs) were improved by 6.00%, 4.61%, and 3.71%, respectively. The channel ensemble method also obtained better recognition results than the standard algorithm on the public dataset. This study validated the efficiency of the proposed method to enhance the detection of SSVEPs, demonstrating its potential use in practical brain–computer interface (BCI) systems. Significance. A SSVEP-based BCI system using a channel ensemble method could achieve high ITR, indicating great potential of this design for various applications with improved control and interaction.

An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces.

Brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenge to the practical application. This study provides an open dataset, which is collected based on a wearable SSVEP-based BCI system, and comprehensively compares the SSVEP data obtained by wet and dry electrodes. The dataset consists of 8-channel EEG data from 102 healthy subjects performing a 12-target SSVEP-based BCI task. For each subject, 10 consecutive blocks were recorded using wet and dry electrodes, respectively. The dataset can be used to investigate the performance of wet and dry electrodes in SSVEP-based BCIs. Besides, the dataset provides sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.

Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling.

The brain–computer interface (BCI) spellers based on steady-state visual evoked potentials (SSVEPs) have recently been widely investigated for their high information transfer rates (ITRs). This paper aims to improve the practicability of the SSVEP-BCIs for high-speed spelling. The system acquired the electroencephalogram (EEG) data from a self-developed dedicated EEG device and the stimulation was arranged as a keyboard. The task-related component analysis (TRCA) spatial filter was modified (mTRCA) for target classification and showed significantly higher performance compared with the original TRCA in the offline analysis. In the online system, the dynamic stopping (DS) strategy based on Bayesian posterior probability was utilized to realize alterable stimulating time. In addition, the temporal filtering process and the programs were optimized to facilitate the online DS operation. Notably, the online ITR reached 330.4 ± 45.4 bits/min on average, which is significantly higher than that of fixed stopping (FS) strategy, and the peak value of 420.2 bits/min is the highest online spelling ITR with a SSVEP-BCI up to now. The proposed system with portable EEG acquisition, friendly interaction, and alterable time of command output provides more flexibility for SSVEP-based BCIs and is promising for practical high-speed spelling.

A Synchronized Hybrid Brain-Computer Interface System for Simultaneous Detection and Classification of Fusion EEG Signals

Brain-computer interface (BCI) technology represents a fast-growing field of research and applications for disabled and healthy people, which is a direct communication pathway to translate the neural information into an active command. Owing to the complicated headset structure, low accuracies, extended training periods, and nonstationary noises, BCI still has many challenges that should be dealt with for further facilitation of BCI technology use in daily life. In this study, a simplified synchronized hybrid BCI system is proposed for multiple command control by the electroencephalograph (EEG) signals in the motor cortex. This system can detect the single motor imagery (MI) task, single steady-state visually evoked potential (SSVEP) task, and hybrid MI + SSVEP tasks simultaneously (total ten mental tasks) via 2 EEG channels with high accuracy. The fast independent component analysis algorithm is employed to hybrid signals for obtaining clear EEG signals resulting from denoising. Feature extraction is performed by the wavelet transform, which is extracted by the features in the frequency and time domains. Furthermore, a four-layer convolutional neural network (CNN) is used as a classifier to distinguish different mental tasks. Finally, the hybrid MI + SSVEP system with a simple structure achieves a high accuracy of 95.56%. Additionally, the single MI-based and the SSVEP-based BCI system obtain the classification accuracy of 90.16% and 93.21%, respectively. Experimental results indicate that the synchronized hybrid BCI system could achieve multiple command control with a simple structure. In comparison with the single MI-based and the SSVEP-based BCI system, the hybrid MI + SSVEP BCI system shows a stable performance and higher efficiency. The proposed investigation provides a new method for the multiple command control by a hybrid BCI system. Also, the proposed BCI system offers the possibility of friendly utilization for disabled people because of its reliability, ease of use, and simplified headset structure.