Steady-state Visual Evoked Potentials Paradigm Research Articles

A subjective and objective fusion visual fatigue assessment system for different hardware and software parameters in SSVEP-based BCI applications.

With the development of brain-computer interface industry, large amounts of related applications have entered people's vision. BCI applications based on steady-state visual evoked potentials (SSVEP) are widely used because they do not require pre-training and have high information transmission rates. However, in the actual use of SSVEP stimulus paradigm, the subjects will produce visual fatigue with the use, and fatigue will affect the transmission efficiency. In this experiment, an experimental environment consisting of two paradigm stimulus frequencies (7.5Hz, 15Hz), three resolutions (800 × 600, 1280 × 720, 1920 × 1080) and three refresh rates (120Hz, 240Hz, 360Hz) is set up. The Likert scale is used to collect subjective fatigue and preference scores, and the EEG acquisition system and eye tracker are used to collect objective data. Using the proposed information entropy-CRITIC algorithm to combine subjective and objective indicators, a fatigue assessment system (display screen fitness-DSF) is innovated to score different experimental environments. The higher the DSF score, the better the visual experience. The results show that when using the 7.5Hz SSVEP paradigm, the combination of 360Hz and 1920 × 1080 can bring the best visual experience. When using the 15Hz SSVEP paradigm, the combination of 240Hz and 1280 × 720 is the best. DSF provides powerful help for hardware and software selection guidance and vision protection when using SSVEP-based BCI applications.

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.

A 4-DOF Exosuit Using a Hybrid EEG-Based Control Approach for Upper-Limb Rehabilitation.

Rehabilitation devices, such as traditional rigid exoskeletons or exosuits, have been widely used to rehabilitate upper limb function post-stroke. In this paper, we have developed an exosuit with four degrees of freedom to enable users to involve more joints in the rehabilitation process. Additionally, a hybrid electroencephalogram-based (EEG-based) control approach has been developed to promote active user engagement and provide more control commands.The hybrid EEG-based control approach includes steady-state visual evoked potential (SSVEP) paradigm and motor imagery (MI) paradigm. Firstly, the rehabilitation movement was selected by SSVEP paradigm, and the multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA) method was used for SSVEP EEG recognition; then, the motion intention was obtained by MI paradigm, and the convolutional neural network (CNN) and long short-term memory network (LSTM) were used to build a CNN-LSTM model for MI EEG recognition; finally, the recognition results were translated into control commands of Bowden cables to achieve multi-degree-of-freedom rehabilitation.Experimental results show that the average classification accuracy of the CNN-LSTM model reaches to 90.07% ± 2.23%, and the overall accuracy of the hybrid EEG-based control approach reaches to 85.26% ± 1.95%. The twelve subjects involved in the usability assessment demonstrated an average system usability scale (SUS) score of 81.25 ± 5.82. Additionally, four participants who underwent a 35-day rehabilitation training demonstrated an average 10.33% increase in range of motion (ROM) across 4 joints, along with a 11.35% increase in the average electromyography (EMG) amplitude of the primary muscle involved.The exosuit demonstrates good accuracy in control, exhibits favorable usability, and shows certain efficacy in multi-joint rehabilitation. Our study has taken into account the neuroplastic principles, aiming to achieve active user engagement while introducing additional degrees of freedom, offering novel ideas and methods for potential brain-computer interface (BCI)-based rehabilitation strategies and hardware development.Clinical impact: Our study presents an exosuit with four degrees of freedom for stroke rehabilitation, enabling multi-joint movement and improved motor recovery. The hybrid EEG-based control approach enhances active user engagement, offering a promising strategy for more effective and user-driven rehabilitation, potentially improving clinical outcomes.Clinical and Translational Impact Statement: By developing an exosuit and a hybrid EEG-based control approach, this study enhances stroke rehabilitation through better user engagement and multi-joint capabilities. These innovations consider neuroplasticity principles, integrating rehabilitation theory with rehabilitation device.

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

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

BCI Control of a Robotic arm based on SSVEP with Moving Stimuli for Reach and grasp Tasks.

Brain-computer interface (BCI) provides a novel technology for patients and healthy human subjects to control a robotic arm. Currently, BCI control of a robotic arm to complete the reaching and grasping tasks in an unstructured environment is still challenging because the current BCI technology does not meet the requirement of manipulating a multi-degree robotic arm accurately and robustly. BCI based on steady-state visual evoked potential (SSVEP) could output a high information transfer rate; however, the conventional SSVEP paradigm failed to control a robotic arm to move continuously and accurately because the users have to switch their gaze between the flickering stimuli and the target frequently. This study proposed a novel SSVEP paradigm in which the flickering stimuli were attached to the robotic arm's gripper and moved with it. First, an offline experiment was designed to investigate the effects of moving flickering stimuli on the SSVEP's responses and decoding accuracy. After that, contrast experiments were conducted, and twelve subjects were recruited to participate in a robotic arm control experiment using both the paradigm one (P1, with moving flickering stimuli) and the paradigm two (P2, conventional fixed flickering stimuli) using a block randomization design to balance their sequences. Double blinks were used to trigger the grasping action asynchronously whenever the subjects were confident that the position of the robotic arm's gripper was accurate enough. Experimental results showed that the paradigm P1 with moving flickering stimuli provided a much better control performance than the conventional paradigm P2 in completing a reaching and grasping task in an unstructured environment. Subjects' subjective feedback scored by a NASA-TLX mental workload scale also corroborated the BCI control performance. The results of this study suggest that the proposed control interface based on SSVEP BCI provides a better solution for robotic arm control to complete the accurate reaching and grasping tasks.

A Binocular Vision SSVEP Brain-Computer Interface Paradigm for Dual-Frequency Modulation.

This study presents a novel brain-computer interface paradigm of dual-frequency modulated steady-state visual evoked potential (SSVEP), aiming to suppress the unpredictable intermodulation components in current applications. This paradigm is especially suitable for training-free scenarios. This study built a dual-frequency binocular vision SSVEP brain-computer interface system using circularly polarized light technology. Two experiments, including a 6-target offline experiment and a 40-target online experiment, were taken with this system. Meanwhile, an improved algorithm filter bank dual-frequency canonical correlation analysis (FBDCCA) was presented for the dual-frequency SSVEP paradigm. Energy analysis was conducted for 9 subjects in the 6-target dual-frequency offline experiment, among which the signal-to-noise ratio of target frequency components have increased by 2 dB compared to the one of unpredictable intermodulation components. Subsequently, the online experiment with 40 targets was conducted with 12 subjects. With this new dual-frequency paradigm, the online training-free experiment's average information transmission rate (ITR) reached 104.56 ± 15.74 bits/min, which was almost twice as fast as the current best dual-frequency paradigm. And the average information transfer rate for offline training analysis of this new paradigm was 180.87 ± 17.88 bits/min. These results demonstrate that this new dual-frequency SSVEP brain-computer interface paradigm can suppress the unpredictable intermodulation harmonics and generate higher quality responses while completing dual-frequency encoding. Moreover, its performance shows high ITR in applications both with and without training. It is thus believed that this paradigm is competent for achieving large target numbers in brain-computer interface systems and has more possible practices.

A Novel Hybrid Brain-Computer Interface Combining the Illusion-Induced VEP and SSVEP.

Traditional single-modality brain-computer interface (BCI) systems are limited by their reliance on a single characteristic of brain signals. To address this issue, incorporating multiple features from EEG signals can provide robust information to enhance BCI performance. In this study, we designed and implemented a novel hybrid paradigm that combined illusion-induced visual evoked potential (IVEP) and steady-state visual evoked potential (SSVEP) with the aim of leveraging their features simultaneously to improve system efficiency. The proposed paradigm was validated through two experimental studies, which encompassed feature analysis of IVEP with a static paradigm, and performance evaluation of hybrid paradigm in comparison with the conventional SSVEP paradigm. The characteristic analysis yielded significant differences in response waveforms among different motion illusions. The performance evaluation of the hybrid BCI demonstrates the advantage of integrating illusory stimuli into the SSVEP paradigm. This integration effectively enhanced the spatio-temporal features of EEG signals, resulting in higher classification accuracy and information transfer rate (ITR) within a short time window when compared to traditional SSVEP-BCI in four-command task. Furthermore, the questionnaire results of subjective estimation revealed that proposed hybrid BCI offers less eye fatigue, and potentially higher levels of concentration, physical condition, and mental condition for users. This work first introduced the IVEP signals in hybrid BCI system that could enhance performance efficiently, which is promising to fulfill the requirements for efficiency in practical BCI control systems.

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.

Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach.

This paper presents the implementation of nonlinear canonical correlation analysis (NLCCA) approach to detect steady-state visual evoked potentials (SSVEP) quickly. The need for the fast recognition of proper stimulus to help end an SSVEP task in a BCI system is justified due to the flickering external stimulus exposure that causes users to start to feel fatigued. Measuring the accuracy and exposure time can be carried out through the information transfer rate—ITR, which is defined as a relationship between the precision, the number of stimuli, and the required time to obtain a result. NLCCA performance was evaluated by comparing it with two other approaches—the well-known canonical correlation analysis (CCA) and the least absolute reduction and selection operator (LASSO), both commonly used to solve the SSVEP paradigm. First, the best average ITR value was found from a dataset comprising ten healthy users with an average age of 28, where an exposure time of one second was obtained. In addition, the time sliding window responses were observed immediately after and around 200 ms after the flickering exposure to obtain the phase effects through the coefficient of variation (CV), where NLCCA obtained the lowest value. Finally, in order to obtain statistical significance to demonstrate that all approaches differ, the accuracy and ITR from the time sliding window responses was compared using a statistical analysis of variance per approach to identify differences between them using Tukey’s test.

A dry electroencephalogram electrode for applications in steady-state visual evoked potential-based brain–computer interface systems

High-efficiency electroencephalogram (EEG) dry electrodes are a key component of brain–computer interface (BCI) technology because of their direct contact with the scalp. In this study, a semi-flexible polydopamine (PDA)/Pt-TiO2 electrode is prepared for the dry-contact acquisition of EEG signals. The PDA biofilm adheres strongly to the scalp and maintains a dynamic balance of water and ions. The Pt nanoparticles and TiO2 nanotube array together result in fast electron transfer. Therefore, the interface impedance between the dry PDA/Pt-TiO2 electrode and scalp is as low as 19.63–24.53 kΩ. The spontaneous EEG signal collected simultaneously using the dry PDA/Pt-TiO2 and wet Ag/AgCl electrodes had a correlation coefficient of up to 99.9%. In a steady-state visual evoked potential (SSVEP)-based BCI system, the dry electrode was used to collect EEG feedback signals for stimulations at 27 different frequencies in the range of 7–19.25 Hz. For these feedback signals, O1, Oz, and O2 channels in the occipital area exhibited high signal-to-noise ratios of 11.3, 11.8, and 11 dB, respectively. A volunteer wore an EEG headband with three PDA/Pt-TiO2 dry electrodes and successfully controlled the robotic arm of the SSVEP-BCI system in the untrained mode. The dry PDA/Pt-TiO2 electrode-based EEG cap is comfortable to wear, the identification signals of the SSVEP paradigm are accurate, and it is suitable for controlling external devices including a keyboard in the SSVEP-BCI system.

A comfortable steady state visual evoked potential stimulation paradigm using peripheral vision

Objective. Steady-state visual evoked potential (SSVEP)-brain–computer interfaces (BCIs) can cause much visual discomfort if the users use the SSVEP-BCIs for a long time. As an alternative scheme to reduce users’ visual fatigue, this study proposes a new stimulation paradigm (termed as steady state peripheral visual evoked potential, abbreviated as SSPVEP) which makes full use of peripheral vision. The electroencephalography (EEG) signals are classifiable which means this proposed stimulation paradigm can be used in BCI system with the aid of the latest hybrid signal processing approach. Approach. Under the SSPVEP stimulation paradigm, 20 targets are mounted on 20 frequencies and other targets are set between two targets with flicker stimuli coding. In order to ensure the classification accuracy of SSPVEP signal detection under the proposed stimulation paradigm, two optimization schemes are proposed for the detection stage of the conventional ensemble task-related component analysis (ETRCA) algorithm. The first optimization scheme uses nonlinear correlation coefficient at the detection part for the first time to improve the classification accuracy of the system. The second optimization scheme uses γ correction to enhance the time domain features of the SSPVEP signals, and uses Manhattan distance for the final detection. Main results. According to the response waveforms of the EEG signals generated under the SSPVEP stimulation paradigm and the results of the questionnaire on user’s comfort level to the two stimulation paradigms (SSPVEP paradigm and conventional SSVEP paradigm), the proposed stimulation paradigm brings less visual fatigue. The comparison results indicate that the proposed detection methods (ETRCA + γ correction + Manhattan distance, ETRCA + Spearman correlation) can greatly improve the classification accuracy compared with the individual template canonical correlation analysis method and conventional ETRCA method based on Pearson correlation. Significance. The SSPVEP stimulation paradigm reduces users’ visual fatigue via using peripheral vision, which provides a new design idea for SSVEP stimulation paradigm aimed at visual comfort.

Effects of Stimulus Spatial Resolution on SSVEP Responses under Overt and Covert Attention.

Steady-State Visual Evoked Potentials (SSVEP) Brain-Computer Interface (BCI) relies on overt spatial attention to exhibit reliable steady-state responses. There is a promising potential to employ the SSVEP paradigm in with vision research and clinical use, for instance, for visual field assessment. In this study, we investigate the SSVEP characteristics with different spatial attention, the different number of stimuli, and different viewing/visual angles. We collected data from eleven subjects in three experiment sessions, lasting about forty minutes, including the setup and calibration. Our evaluation results show similar SSVEP responses between overt and covert attention in multiple stimuli scenarios in most of the visual angles. We do not find any significant differences in SSVEP responses in visual angles between single and multi stimuli in covert attention. From this study, we found that reliable SSVEP responses can be achieved with covert spatial attention regardless of visual angles and stimulus spatial resolution.

A hybrid BCI-controlled smart home system combining SSVEP and EMG for individuals with paralysis

In this study, electromyogram (EMG) signals associated with occlusal movement were integrated with steady-state visual evoked potentials (SSVEPs) to develop a hybrid brain–computer interface (hBCI)-based smart home control system for individuals with paralysis. The SSVEP paradigm was used to develop a system containing one main interface and five sub-interfaces corresponding to several devices during the working state, and one interface for the idle state. Participants controlled the devices by gazing at certain stimuli, which flickered at different frequencies for each function. Classical correlation analysis (CCA) of four channel EEG signals was used to recognize SSVEP features as intended selection. Several particular occlusal EMG patterns from the single channel of temporalis muscle were used to confirm the selected function, return from the sub-interface to the main interface, and switch the system on/off, respectively. Five healthy participants and five individuals with paralysis completed the system control experiment. The average target selection accuracy reached 97.5% and 83.6% in healthy participants and patients, while the confirmation accuracy in each group reached 97.6% and 96.9%, respectively. When SSVEPs were combined with EMG signals from occlusal movement to confirm the target selection, the actual control accuracy was maximized to 100%, and the information transmission rate (ITR) reached 45 bit/min among patients. Operation of the hBCI-based smart home control system did not cause higher mental or physical workload in patients compared to healthy participants. Our findings indicate that combining SSVEP and EMG signals effectively enhances the safety and interactivity of hBCI-based smart home systems.

Error Correction Regression Framework for Enhancing the Decoding Accuracies of Ear-EEG Brain-Computer Interfaces.

Ear-electroencephalography (EEG) is a promising tool for practical brain-computer interface (BCI) applications because it is more unobtrusive, comfortable, and mobile than a typical scalp-EEG system. However, an ear-EEG has a natural constraint of electrode location (e.g., limited in or around the ear) for acquiring informative brain signals sufficiently. Achieving reliable performance of ear-EEG in specific BCI paradigms that do not utilize brain signals on the temporal lobe around the ear is difficult. For example, steady-state visual evoked potentials (SSVEPs), which are mainly generated in the occipital area, have a significantly attenuated and distorted amplitude in ear-EEG. Therefore, preserving the high level of decoding accuracy is challenging and essential for SSVEP BCI based on ear-EEG. In this paper, we first investigate linear and nonlinear regression methods to increase the decoding accuracy of ear-EEG regarding SSVEP paradigm by utilizing the estimated target EEG signals on the occipital area. Then, we investigate an ensemble method to consider the prediction variability of the regression methods. Finally, we propose an error correction regression (ECR) framework to reduce the prediction errors by adding an additional nonlinear regression process (i.e., kernel ridge regression). We evaluate the ECR framework in terms of single session, session-to-session transfer, and subject-transfer decoding. We also validate the online decoding ability of the proposed framework with a short-time window size. The average accuracies are observed to be 91.11±9.14%, 90.52±8.67%, 86.96±12.13%, and 78.79±12.59%. This paper demonstrates that SSVEP BCI based on ear-EEG can achieve reliable performance with the proposed ECR framework.

A Radial Zoom Motion-Based Paradigm for Steady State Motion Visual Evoked Potentials.

Background: In steady state visual evoked potential (SSVEP)-based brain-computer interfaces, prolonged repeated flicker stimulation would reduce the system performance. To reduce the visual discomfort and fatigue, while ensuring recognition accuracy, and information transmission rate (ITR), a novel motion paradigm based on the steady-state motion visual evoked potentials (SSMVEPs) is proposed.Methods: The novel SSMVEP paradigm of the radial zoom motion was realized using the sinusoidal form to modulate the size of the stimuli. The radial zoom motion-based SSMVEP paradigm was compared with the flicker-based SSVEP paradigm and the SSMVEP paradigm based on Newton's ring motion. The canonical correlation analysis was used to identify the frequency of the eight targets, the recognition accuracy of different paradigms with different stimulation frequencies, and the ITR under different stimulation durations were calculated. The subjective comfort scores and fatigue scores, and decrease in the accuracy due to fatigue was evaluated.Results: The average recognition accuracy of the novel radial zoom motion-based SSMVEP paradigm was 93.4%, and its ITR reached 42.5 bit/min, which was greater than the average recognition accuracy of the SSMVEP paradigm based on Newton's ring motion. The comfort score of the novel paradigm was greater than both the flicker-based SSVEP paradigm and SSMVEP paradigm based on Newton's ring motion. The decrease in the recognition accuracy due to fatigue was less than that of the SSSMVEP paradigm based on Newton's ring motion.Conclusion: The SSMVEP paradigm based on radial zoom motion has high recognition accuracy and ITR with low visual discomfort and fatigue scores. The method has potential advantages in overcoming the performance decline caused by fatigue.

Differentiation of Types of Visual Agnosia Using EEG.

Visual recognition deficits are the hallmark symptom of visual agnosia, a neuropsychological disorder typically associated with damage to the visual system. Most research into visual agnosia focuses on characterizing the deficits through detailed behavioral testing, and structural and functional brain scans are used to determine the spatial extent of any cortical damage. Although the hierarchical nature of the visual system leads to clear predictions about the temporal dynamics of cortical deficits, there has been little research on the use of neuroimaging methods with high temporal resolution to characterize the temporal profile of agnosia deficits. Here, we employed high-density electroencephalography (EEG) to investigate alterations in the temporal dynamics of the visual system in two individuals with visual agnosia. In the context of a steady state visual evoked potential paradigm (SSVEP), individuals viewed pattern-reversing checkerboards of differing spatial frequency, and we assessed the responses of the visual system in the frequency and temporal domain. JW, a patient with early visual cortex damage, showed impaired SSVEP response relative to a control group and to the second patient (SM) who had right temporal lobe damage. JW also showed lower decoding accuracy for early visual responses (around 100 ms). SM, whose lesion is more anterior in the visual system, showed good decoding accuracy initially but low decoding after 500 ms. Overall, EEG and multivariate decoding methods can yield important insights into the temporal dynamics of visual responses in individuals with visual agnosia.

Decoding of top-down cognitive processing for SSVEP-controlled BMI.

We present a fast and accurate non-invasive brain-machine interface (BMI) based on demodulating steady-state visual evoked potentials (SSVEPs) in electroencephalography (EEG). Our study reports an SSVEP-BMI that, for the first time, decodes primarily based on top-down and not bottom-up visual information processing. The experimental setup presents a grid-shaped flickering line array that the participants observe while intentionally attending to a subset of flickering lines representing the shape of a letter. While the flickering pixels stimulate the participant’s visual cortex uniformly with equal probability, the participant’s intention groups the strokes and thus perceives a ‘letter Gestalt’. We observed decoding accuracy of 35.81% (up to 65.83%) with a regularized linear discriminant analysis; on average 2.05-fold, and up to 3.77-fold greater than chance levels in multi-class classification. Compared to the EEG signals, an electrooculogram (EOG) did not significantly contribute to decoding accuracies. Further analysis reveals that the top-down SSVEP paradigm shows the most focalised activation pattern around occipital visual areas; Granger causality analysis consistently revealed prefrontal top-down control over early visual processing. Taken together, the present paradigm provides the first neurophysiological evidence for the top-down SSVEP BMI paradigm, which potentially enables multi-class intentional control of EEG-BMIs without using gaze-shifting.

Research on High-Frequency Combination Coding-Based SSVEP-BCIs and Its Signal Processing Algorithms

This study presents a new steady-state visual evoked potential (SSVEP) paradigm for brain computer interface (BCI) systems. The new paradigm is High-Frequency Combination Coding-Based SSVEP (HFCC-SSVEP). The goal of this study is to increase the number of targets using fewer stimulation frequencies, with diminishing subject’s fatigue and reducing the risk of photosensitive epileptic seizures. This paper investigated the HFCC-SSVEP high-frequency response (beyond 25 Hz) for 3 frequencies (25 Hz, 33.33 Hz, and 40 Hz). HFCC-SSVEP producesnnwithnhigh stimulation frequencies through Time Series Combination Code. Furthermore, The Improved Hilbert-Huang Transform (IHHT) is adopted to extract time-frequency feature of the proposed SSVEP response. Lastly, the differentiation combination (DC) method is proposed to select the combination coding sequence in order to increase the recognition rate; as a result, IHHT algorithm and DC method for the proposed SSVEP paradigm in this study increase recognition efficiency so as to improve ITR and increase the stability of the BCI system. Furthermore, SSVEPs evoked by high-frequency stimuli (beyond 25 Hz) minimally diminish subject’s fatigue and prevent safety hazards linked to photo-induced epileptic seizures. This study tests five subjects in order to verify the feasibility of the proposed method.

A new hybrid BCI paradigm based on P300 and SSVEP

BackgroundP300 and steady-state visual evoked potential (SSVEP) approaches have been widely used for brain–computer interface (BCI) systems. However, neither of these approaches can work for all subjects. Some groups have reported that a hybrid BCI that combines two or more approaches might provide BCI functionality to more users. Hybrid P300/SSVEP BCIs have only recently been developed and validated, and very few avenues to improve performance have been explored. New methodThe present study compares an established hybrid P300/SSVEP BCIs paradigm to a new paradigm in which shape changing, instead of color changing, is adopted for P300 evocation to decrease the degradation on SSVEP strength. ResultThe result shows that the new hybrid paradigm presented in this paper yields much better performance than the normal hybrid paradigm. Comparison with existing methodA performance increase of nearly 20% in SSVEP classification is achieved using the new hybrid paradigm in comparison with the normal hybrid paradigm. All the paradigms except the normal hybrid paradigm used in this paper obtain 100% accuracy in P300 classification. ConclusionsThe new hybrid P300/SSVEP BCIs paradigm in which shape changing, instead of color changing, could obtain as high classification accuracy of SSVEP as the traditional SSVEP paradigm and could obtain as high classification accuracy of P300 as the traditional P300 paradigm. P300 did not interfere with the SSVEP response using the new hybrid paradigm presented in this paper, which was superior to the normal hybrid P300/SSVEP paradigm.

Brain–computer interfaces using capacitive measurement of visual or auditory steady-state responses

Objective. Brain–computer interface (BCI) technologies have been intensely studied to provide alternative communication tools entirely independent of neuromuscular activities. Current BCI technologies use electroencephalogram (EEG) acquisition methods that require unpleasant gel injections, impractical preparations and clean-up procedures. The next generation of BCI technologies requires practical, user-friendly, nonintrusive EEG platforms in order to facilitate the application of laboratory work in real-world settings. Approach. A capacitive electrode that does not require an electrolytic gel or direct electrode–scalp contact is a potential alternative to the conventional wet electrode in future BCI systems. We have proposed a new capacitive EEG electrode that contains a conductive polymer-sensing surface, which enhances electrode performance. This paper presents results from five subjects who exhibited visual or auditory steady-state responses according to BCI using these new capacitive electrodes. The steady-state visual evoked potential (SSVEP) spelling system and the auditory steady-state response (ASSR) binary decision system were employed. Main results. Offline tests demonstrated BCI performance high enough to be used in a BCI system (accuracy: 95.2%, ITR: 19.91 bpm for SSVEP BCI (6 s), accuracy: 82.6%, ITR: 1.48 bpm for ASSR BCI (14 s)) with the analysis time being slightly longer than that when wet electrodes were employed with the same BCI system (accuracy: 91.2%, ITR: 25.79 bpm for SSVEP BCI (4 s), accuracy: 81.3%, ITR: 1.57 bpm for ASSR BCI (12 s)). Subjects performed online BCI under the SSVEP paradigm in copy spelling mode and under the ASSR paradigm in selective attention mode with a mean information transfer rate (ITR) of 17.78 ± 2.08 and 0.7 ± 0.24 bpm, respectively. Significance. The results of these experiments demonstrate the feasibility of using our capacitive EEG electrode in BCI systems. This capacitive electrode may become a flexible and non-intrusive tool fit for various applications in the next generation of BCI technologies.