SSVEP-based Brain-computer Interface System Research Articles

Alertness-based subject-dependent and subject-independent filter optimization for improving classification efficiency of SSVEP detection.

BACKGROUND: Mental task-based brain computer interface (BCI) systems are usually developed for neural prostheses technologies and medical rehabilitation. The mental workload was too heavy for the user to manipulate BCI effectively. Fortunately, electroencephalography (EEG) signal is not only used for BCI control but also relates to the changes of mental states.OBJECTIVE: We proposed a novel method for identifying non-effective trials of Steady State Visual Evoked Potential (SSVEP)-based BCI.METHODS: We used the subject-dependent and subject-independent alertness models identifying non-effective trials of SSVEP-BCI systems.RESULTS: The result implied that the subject-dependent alertness model was most useful for improving the classification accuracy in the task. However, the subject-independent alertness model could enhance the prediction ability of SSVEP-based BCI system.CONCLUSION: In comparison to the conventional canonical correlation analysis (CCA) method without alertness-model filtering, the raise of precision was valuable for the technical development of BCI works. It demonstrated the effectiveness of our proposed subject-dependent and subject-independent methods.

A CNN-based comparing network for the detection of steady-state visual evoked potential responses

Brain-computer interfaces (BCIs) based on Steady-State Visual Evoked Potentials (SSVEPs) has been attracting much attention because of its high information transfer rate and little user training. However, most methods applied to decode SSVEPs are limited to CCA and some extended CCA-based methods. This study proposed a comparing network based on Convolutional Neural Network (CNN), which was used to learn the relationship between EEG signals and the templates corresponding to each stimulus frequency of SSVEPs. The effectiveness of the proposed method is validated by comparing it with the standard CCA and other state-of-the art methods for decoding SSVEPs (i.e., CNN and TRCA) on the actual SSVEP datasets collected from 23 subjects. The comparison results indicate that the CNN-based comparing network can significantly improve the classification accuracy. Furthermore, the comparing network with TRCA achieved the best performance among three methods based on comparing network with the averaged accuracy of 91.24% (data length: 2s) and 86.15% (data length: 1s). The study validated the efficiency of the proposed CNN-based comparing network in decoding SSVEPs. It suggests that the comparing network with TRCA is a promising methodology for target identification of SSVEPs and could further improve the performance of SSVEP-based BCI system.

Channel Projection-Based CCA Target Identification Method for an SSVEP-Based BCI System of Quadrotor Helicopter Control.

The brain-computer interface (BCI) plays an important role in assisting patients with amyotrophic lateral sclerosis (ALS) to enable them to participate in communication and entertainment. In this study, a novel channel projection-based canonical correlation analysis (CP-CCA) target identification method for steady-state visual evoked potential- (SSVEP-) based BCI system was proposed. The single-channel electroencephalography (EEG) signals of multiple trials were recorded when the subject is under the same stimulus frequency. The CCAs between single-channel EEG signals of multiple trials and sine-cosine reference signals were obtained. Then, the optimal reference signal of each channel was utilized to estimate the test EEG signal. To validate the proposed method, we acquired the training dataset with two testing conditions including the optimal time window length and the number of the trial of training data. The offline experiments conducted a comparison of the proposed method with the traditional canonical correlation analysis (CCA) and power spectrum density analysis (PSDA) method using a 5-class SSVEP dataset that was recorded from 10 subjects. Based on the training dataset, the online 3D-helicopter control experiment was carried out. The offline experimental results showed that the proposed method outperformed the CCA and the PSDA methods in terms of classification accuracy and information transfer rate (ITR). Furthermore, the online experiments of 3-DOF helicopter control achieved an average accuracy of 87.94 ± 5.93% with an ITR of 21.07 ± 4.42 bit/min.

Training -Free Steady-State Visual Evoked Potential Brain–Computer Interface Based on Filter Bank Canonical Correlation Analysis and Spatiotemporal Beamforming Decoding

A brain-computer interface (BCI) provides a novel non-muscular communication pathway for individuals with severe neuromuscular diseases. BCI systems based on steady-state visual evoked potentials (SSVEPs) have high classification accuracy, information transfer rate, and signal-to-noise ratio, giving them high research and application value. However, SSVEP-based BCI has several limitations in real-world applications. The main challenge is how to reduce or eliminate the need for a dedicated training process while maintaining high classification accuracy. Filter bank canonical correlation analysis (FBCCA) is a powerful and widely used feature extraction method for SSVEP-based BCI systems. However, the reference signals of FBCCA are fixed-frequency sine-cosine waves, which makes it difficult to accurately describe the complex, mutative, and individually different physiological SSVEPs. Therefore, there is huge room for improvement in classification performance based on the FBCCA method. In contrast, although spatiotemporal beamforming (BF) detects SSVEPs with high accuracy, it needs an additional training process, which limits its application. In this study, we propose a bimodal decoding algorithm (FBCCA+BF), which combines the advantages of the training-free classification of FBCCA and the data-driven and adaptive features of BF. Six-channel SSVEP data corresponding to eight targets measured from 15 subjects were used to test the effectiveness of three different CCA-based methods, BF, and our proposed FBCCA+BF methods. It was found that the classification accuracies for BF and FBCCA+BF are 95.6% and 92.2%, respectively, which are significantly higher than the other CCA-based methods. Notably, both BF and FBCCA+BF obtain state-of-the-art performance, but FBCCA+BF does this without the need for a dedicated training process. Therefore, we conclude that our proposed FBCCA+BF method provides a training-free and high-accuracy approach for SSVEP-based BCIs.

Assessment of Cognitive Aging Using an SSVEP-Based Brain–Computer Interface System

Cognitive deterioration caused by illness or aging often occurs before symptoms arise, and its timely diagnosis is crucial to reducing its medical, personal, and societal impacts. Brain–computer interfaces (BCIs) stimulate and analyze key cerebral rhythms, enabling reliable cognitive assessment that can accelerate diagnosis. The BCI system presented analyzes steady-state visually evoked potentials (SSVEPs) elicited in subjects of varying age to detect cognitive aging, predict its magnitude, and identify its relationship with SSVEP features (band power and frequency detection accuracy), which were hypothesized to indicate cognitive decline due to aging. The BCI system was tested with subjects of varying age to assess its ability to detect aging-induced cognitive deterioration. Rectangular stimuli flickering at theta, alpha, and beta frequencies were presented to subjects, and frontal and occipital Electroencephalographic (EEG) responses were recorded. These were processed to calculate detection accuracy for each subject and calculate SSVEP band power. A neural network was trained using the features to predict cognitive age. The results showed potential cognitive deterioration through age-related variations in SSVEP features. Frequency detection accuracy declined after age group 20–40, and band power declined throughout all age groups. SSVEPs generated at theta and alpha frequencies, especially 7.5 Hz, were the best indicators of cognitive deterioration. Here, frequency detection accuracy consistently declined after age group 20–40 from an average of 96.64% to 69.23%. The presented system can be used as an effective diagnosis tool for age-related cognitive decline.

Multiple-command single-frequency SSVEP-based BCI system using flickering action video

BackgroundThe number of commands in a brain–computer interface (BCI) system is important. This study proposes a new BCI technique to increase the number of commands in a single BCI system without loss of accuracy. New methodWe expected that a flickering action video with left and right elbow movements could simultaneously activate the different pattern of event-related desynchronization (ERD) according to the video contents (e.g., left or right) and steady-state visually evoked potential (SSVEP). The classification accuracy to discriminate left, right, and rest states was compared under the three following feature combinations: SSVEP power (19–21 Hz), Mu power (8–13 Hz), and simultaneous SSVEP and Mu power. ResultsThe SSVEP feature could discriminate the stimulus condition, regardless of left or right, from the rest condition, while the Mu feature discriminated left or right, but was relatively poor in discriminating stimulus from rest. However, combining the SSVEP and Mu features, which were evoked by the stimulus with a single frequency, showed superior performance for discriminating all the stimuli among rest, left, or right. Comparison with the existing methodThe video contents could activate the ERD differently, and the flickering component increased its accuracy, such that it revealed a better performance to discriminate when considering together. ConclusionsThis paradigm showed possibility of increasing performance in terms of accuracy and number of commands with a single frequency by applying flickering action video paradigm and applicability to rehabilitation systems used by patients to facilitate their mirror neuron systems while training.

A Dynamic Window Recognition Algorithm for SSVEP-Based Brain-Computer Interfaces Using a Spatio-Temporal Equalizer.

The past decade has witnessed rapid development in the field of brain-computer interfaces (BCIs). While the performance is no longer the biggest bottleneck in the BCI application, the tedious training process and the poor ease-of-use have become the most significant challenges. In this study, a spatio-temporal equalization dynamic window (STE-DW) recognition algorithm is proposed for steady-state visual evoked potential (SSVEP)-based BCIs. The algorithm can adaptively control the stimulus time while maintaining the recognition accuracy, which significantly improves the information transfer rate (ITR) and enhances the adaptability of the system to different subjects. Specifically, a spatio-temporal equalization algorithm is used to reduce the adverse effects of spatial and temporal correlation of background noise. Based on the theory of multiple hypotheses testing, a stimulus termination criterion is used to adaptively control the dynamic window. The offline analysis which used a benchmark dataset and an offline dataset collected from 16 subjects demonstrated that the STE-DW algorithm is superior to the filter bank canonical correlation analysis (FBCCA), canonical variates with autoregressive spectral analysis (CVARS), canonical correlation analysis (CCA) and CCA reducing variation (CCA-RV) algorithms in terms of accuracy and ITR. The results show that in the benchmark dataset, the STE-DW algorithm achieved an average ITR of 134 bits/min, which exceeds the FBCCA, CVARS, CCA and CCA-RV. In off-line experiments, the STE-DW algorithm also achieved an average ITR of 116 bits/min. In addition, the online experiment also showed that the STE-DW algorithm can effectively expand the number of applicable users of the SSVEP-based BCI system. We suggest that the STE-DW algorithm can be used as a reliable identification algorithm for training-free SSVEP-based BCIs, because of the good balance between ease of use, recognition accuracy, ITR and user applicability.

Comparing Steady-State Visually Evoked Potentials Frequency Estimation Methods in Brain-Computer Interface With the Minimum Number of EEG Channels.

Introduction:Brain-Computer Interface (BCI) systems provide a communication pathway between users and systems. BCI systems based on Steady-State Visually Evoked Potentials (SSVEP) are widely used in recent decades. Different feature extraction methods have been introduced in the literature to estimate SSVEP responses to BCI applications.Methods:In this study, the new algorithms, including Canonical Correlation Analysis (CCA), Least Absolute Shrinkage and Selection Operator (LASSO), L1-regularized Multi-way CCA (L1-MCCA), Multi-set CCA (MsetCCA), Common Feature Analysis (CFA), and Multiple Logistic Regression (MLR) are compared using proper statistical methods to determine which one has better performance with the least number of EEG electrodes.Results:It was found that MLR, MsetCCA, and CFA algorithms provided the highest performances and significantly outperformed CCA, LASSO, and L1-MCCA algorithms when using 8 EEG channels. However, when using only 1 or 2 EEG channels d, CFA method provided the highest F-scores. This algorithm not only outperformed MLR and MsetCCA when applied on different electrode montages but also provided the fastest computation time on the test set.Conclusion:Although MLR method has already demonstrated to have higher performance in comparison with other frequency recognition algorithms, this study showed that in a practical SSVEP-based BCI system with 1 or 2 EEG channels and short-time windows, CFA method outperforms other algorithms. Therefore, it is proposed that CFA algorithm is a promising choice for the expansion of practical SSVEP-based BCI systems.

SSVEP-based brain-computer interface for computer control application using SVM classifier

In this research, a Brain Computer Interface (BCI) based on Steady State Visually Evoked Potential (SSVEP) for computer control applications using Support Vector Machine (SVM) is presented. For many years, people have speculated that electroencephalographic activities or other electrophysiological measures of brain function might provide a new non-muscular channel that can be used for sending messages or commands to the external world. BCI is a fast-growing emergent technology in which researchers aim to build a direct channel between the human brain and the computer. BCI systems provide a new communication channel for disabled people. Among many different types of the BCI systems, the SSVEP based has attracted more attention due to its ease of use and signal processing. SSVEPs are usually detected from the occipital lobe of the brain when the subject is looking at a twinkling light source. In this paper, SVM is used to classify SSVEP based on electroencephalogram data with proper features. Based on the experiment utilizing a 14-channel Electroencephalography (EEG) device, 80 percent of accuracy can be reached by our SSVEP-based BCI system using Linear SVM Kernel as classification engine.

Improving the performance of the SSVEP-based BCI system using optimized singular spectrum analysis (OSSA)

Abstract A brain-computer interface (BCI) is a system that provides a new communication path for people with disabilities to communicate with the outside world. Recently, steady-state visual evoked potential (SSVEP)-based BCI system has received more attention because it has a higher information transfer rate (ITR) and requires less user and system training. SSVEP is a periodic signal that generated by a periodic visual stimulation and its frequency spectrum contains stimulation frequency and its harmonics. The aim of the SSVEP based BCI system is to detect the stimulation frequency and thus understand the intention of the person. In recent years, various methods have been proposed for SSVEP recognition. Among these methods, the canonical correlation analysis (CCA) as a well-known powerful method and multivariate linear regression (MLR) as a state-of-the-art method can be mentioned. Although these methods find the dominant frequency in the short time EEG signal, their performance severely degraded by the presence of the noise. Therefore, the appropriate removal of the background EEG signal, that plays the role of the noise, is essential. In this paper, singular spectrum analysis (SSA) is used as a preprocessing method for separating random and periodic components of the recorded EEG signal. Also, the skewness coefficient and the Spearman correlation are used to optimize the values of SSA parameters. The experimental results show a significant improvement in performance after preprocessing by SSA using CCA and MLR methods, especially for short data lengths. Also, it has been shown that SSA with the optimal parameters (OSSA) has a higher performance than SSA with fixed parameters. The superiority of proposed method suggests it as an appropriate choice for implementation of the real-time SSVEP based BCI system.

A Wearable SSVEP-Based BCI System for Quadcopter Control Using Head-Mounted Device

Restoring the interaction between disabled people and the 3-D physical world via a brain-computer interface (BCI) is an exciting topic. To this end, we designed a wearable BCI system based on the steady-state visual evoked potential (SSVEP), which enables 3-D navigation of quadcopter flight with immersive first-person visual feedback using a head-mounted device. In addition, to alleviate the user’s operational burden, this paper provides asynchronous switch control for the users. The transitional state due to head movement in an asynchronous BCI was isolated online and translated into hover to eliminate its influence. The experimental results in the physical environment showed that the subjects could accomplish the 3-D flight tasks accurately and smoothly using our system. In particular, in this paper, we proposed an information transfer rate metric that is suitable for the asynchronous task. We demonstrated the feasibility of using the head-mounted device and a proper control strategy to facilitate the portability and practicability of the SSVEP-based BCI system for its navigation utility.

Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System

Subjects with amyotrophic lateral sclerosis (ALS) consistently experience decreasing quality of life because of this distinctive disease. Thus, a practical brain-computer interface (BCI) application can effectively help subjects with ALS to participate in communication or entertainment. In this study, a fuzzy tracking and control algorithm is proposed for developing a BCI remote control system. To represent the characteristics of the measured electroencephalography (EEG) signals after visual stimulation, a fast Fourier transform is applied to extract the EEG features. A self-developed fuzzy tracking algorithm quickly traces the changes of EEG signals. The accuracy and stability of a BCI system can be greatly improved by using a fuzzy control algorithm. Fifteen subjects were asked to attend a performance test of this BCI system. The canonical correlation analysis (CCA) was adopted to compare the proposed approach, and the average recognition rates are 96.97% and 94.49% for proposed approach and CCA, respectively. The experimental results showed that the proposed approach is preferable to CCA. Overall, the proposed fuzzy tracking and control algorithm applied in the BCI system can profoundly help subjects with ALS to control air swimmer drone vehicles for entertainment purposes.

A multi-command SSVEP-based BCI system based on single flickering frequency half-field steady-state visual stimulation

Steady-state visual evoked potentials (SSVEPs) are widely employed in brain-computer interface (BCI) applications, especially to control machines. However, the use of SSVEPs leads to eye fatigue and causes lower accuracy over the long term, particularly when multi-commands are required. Therefore, this paper proposes a half-field steady-state visual stimulation pattern and paradigm to increase the limited number of commands that can be achieved with existing SSVEP-based BCI methods. Following the theory of vision perception and existing half-field SSVEP-based BCI systems, the new stimulation pattern generates four commands using only one frequency flickering stimulus and has an average classification accuracy of approximately 75%. According to the proposed stimulus pattern, using only one frequency without requiring users to stare directly at the flickering stimulus allows users to easily focus on the system and experience less visual fatigue compared to existing systems. Furthermore, new half-field SSVEP-based BCI systems are proposed, incorporating our proposed feature extraction and decision-making algorithm. Extracting the signal from the occipital area and using a reference electrode position at the parietal area yielded better results compared to the central area. In addition, we recommend using an LED or LCD as the visual stimulus device (at the recommended size), which yielded comparable results to our proposed feature extraction and decision-making algorithm. Finally, an application of the proposed system is demonstrated for real-time television control.

A Double-Partial Least-Squares Model for the Detection of Steady-State Visual Evoked Potentials.

Establishing a high-accuracy and training-free brain-computer interface (BCI) system is essential for improving BCI practicality. In this study, we propose for the first time a training-free double-partial least-squares (D-PLS) model for steady-state visual evoked potential (SSVEP) detection that consists of double-layer PLS, a PLS spatial filter, and a PLS feature extractor. Electroencephalographic data from 11 healthy volunteers under four different visual stimulation frequencies were used to test the proposed method. Compared with commonly used spatial filters, minimum energy combination and average maximum contrast combination, the classification accuracies could be improved 2-10% by our proposed PLS spatial filter. Furthermore, our proposed PLS feature extractor achieved better performance than current feature extraction methods, namely power spectral density analysis, canonical correlation analysis, and the use of the least absolute shrinkage and selection operator. The average classification accuracy for our proposed D-PLS model exceeded [Formula: see text] when the signal time window was longer than 3.5 s and reached as high as [Formula: see text] when the time window was 5 s. Moreover, the D-PLS model can be easily set without training data, so it can be used widely in SSVEP-based BCI systems.

Effect of higher frequency on the classification of steady-state visual evoked potentials

Objective. Most existing brain–computer interface (BCI) designs based on steady-state visual evoked potentials (SSVEPs) primarily use low frequency visual stimuli (e.g., <20 Hz) to elicit relatively high SSVEP amplitudes. While low frequency stimuli could evoke photosensitivity-based epileptic seizures, high frequency stimuli generally show less visual fatigue and no stimulus-related seizures. The fundamental objective of this study was to investigate the effect of stimulation frequency and duty-cycle on the usability of an SSVEP-based BCI system. Approach. We developed an SSVEP-based BCI speller using multiple LEDs flickering with low frequencies (6–14.9 Hz) with a duty-cycle of 50%, or higher frequencies (26–34.7 Hz) with duty-cycles of 50%, 60%, and 70%. The four different experimental conditions were tested with 26 subjects in order to investigate the impact of stimulation frequency and duty-cycle on performance and visual fatigue, and evaluated with a questionnaire survey. Resting state alpha powers were utilized to interpret our results from the neurophysiological point of view. Main results. The stimulation method employing higher frequencies not only showed less visual fatigue, but it also showed higher and more stable classification performance compared to that employing relatively lower frequencies. Different duty-cycles in the higher frequency stimulation conditions did not significantly affect visual fatigue, but a duty-cycle of 50% was a better choice with respect to performance. The performance of the higher frequency stimulation method was also less susceptible to resting state alpha powers, while that of the lower frequency stimulation method was negatively correlated with alpha powers. Significance. These results suggest that the use of higher frequency visual stimuli is more beneficial for performance improvement and stability as time passes when developing practical SSVEP-based BCI applications.

Time-varying and simultaneous frequency stimulation for multi-class SSVEP-based brain-computer interface.

In recent years, there has been increasing interest in using steady-state visual evoked potentials (SSVEP) in brain-computer interface (BCI) systems for their high signal to noise ratio. However, due to the limitations of brain physiology and the refresh rate of the display devices, the available stimulation frequencies that evoke strong SSVEPs are limited. The goal of this paper is to investigate time-varying and simultaneous frequency stimulation in order to increase the number of visual stimuli with a fixed number of stimulation frequencies in multiclass SSVEP-based BCI systems. This study analyzes the SSVEPs induced by groups of light-emitting diodes (LEDs). The proposed method produces more selections than the number of stimulation frequencies through an efficient combination of time-varying and simultaneous frequencies for stimulation. The feasibility and effectiveness of our proposed method was confirmed by a set of experiments conducted on six subjects. The results confirmed that our proposed stimulation is a promising method to increase the number of stimuli using a fixed number of frequencies for multi-class SSVEP-based BCI tasks.

SSVEP Based BCI for an Object Control in 2D Space

The Brain-Computer Interfaces can successfully help the healthy and disabled users, in performing various activities in their day to day life. But still such high expectations are yet to be fulfilled by the existing BCI designs due to their restricted reliability and less understanding of the brain mechanisms used in it. Brain-computer interface (BCI) systems based on the steady-state visual evoked potential (SSVEP) provide higher information transfer rate and require lesser training time than BCI systems using other paradigms. This work aimed to directly address the above problems by optimizing the BCI designs based on Steady-State Visual Evoked Potential (SSVEP) brain responses. The main goal of this work was to optimize the frequency to be used by the users and further enhance the information transfer rates and the reliability of multi-command SSVEP-based BCI systems. The BCI system is designed to allow control of a virtual ball in 2D space with more than 97% of average accuracy with different users.

Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset.

BackgroundBridging the gap between laboratory brain-computer interface (BCI) demonstrations and real-life applications has gained increasing attention nowadays in translational neuroscience. An urgent need is to explore the feasibility of using a low-cost, ease-of-use electroencephalogram (EEG) headset for monitoring individuals’ EEG signals in their natural head/body positions and movements. This study aimed to assess the feasibility of using a consumer-level EEG headset to realize an online steady-state visual-evoked potential (SSVEP)-based BCI during human walking.MethodsThis study adopted a 14-channel Emotiv EEG headset to implement a four-target online SSVEP decoding system, and included treadmill walking at the speeds of 0.45, 0.89, and 1.34 meters per second (m/s) to initiate the walking locomotion. Seventeen participants were instructed to perform the online BCI tasks while standing or walking on the treadmill. To maintain a constant viewing distance to the visual targets, participants held the hand-grip of the treadmill during the experiment. Along with online BCI performance, the concurrent SSVEP signals were recorded for offline assessment.ResultsDespite walking-related attenuation of SSVEPs, the online BCI obtained an information transfer rate (ITR) over 12 bits/min during slow walking (below 0.89 m/s).ConclusionsSSVEP-based BCI systems are deployable to users in treadmill walking that mimics natural walking rather than in highly-controlled laboratory settings. This study considerably promotes the use of a consumer-level EEG headset towards the real-life BCI applications.

An SSVEP-Based BCI System for SMS in a Mobile Phone

In this paper, the purpose is to develop a Short Message Service (SMS) system that can aid the ALS patients to control a mobile phone to send a message by using of the steady state visually evoked potentials (SSVEP) brain computer interface (BCI) to a person who can help him/her. The EEG signals may be detected by electrodes and signal extracting chip. Then these signal can be transmitted to the mobile platforms by using of Bluetooth interface. Finally, the message and phone number can be selected in accordance with the components in frequency domain transferred from time domain. The experimental results had shown that the proposed system can easily send a message from a mobile phone to another.

A new dual-frequency stimulation method to increase the number of visual stimuli for multi-class SSVEP-based brain–computer interface (BCI)

In the present study, we introduce a new dual-frequency stimulation method that can produce more visual stimuli with limited number of stimulation frequencies for use in multiclass steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) systems. Methods for increasing the number of visual stimuli are necessary, particularly for the implementation of multi-class SSVEP-based BCI, as available stimulation frequencies are generally limited when visual stimuli are presented through a computer monitor. The new stimulation was based on a conventional black–white checkerboard pattern; however, unlike the conventional approach, ten visual stimuli eliciting distinct SSVEP responses at different frequencies could be generated by combining four different stimulation frequencies. Through the offline experiments conducted with eleven participants, we confirmed that all ten visual stimuli could evoke distinct and discriminable single SSVEP peaks, of which the signal-to-noise ratios were high enough to be used for practical SSVEP-based BCI systems. In order to demonstrate the possibility of the practical use of the proposed method, a mental keypad system was implemented and online experiments were conducted with additional ten participants. We achieved an average information transfer rate of 33.26bits/min and an average accuracy of 87.23%, and all ten participants succeeded in calling their mobile phones using our online BCI system.