Observing the steady-state visual evoked potentials with a compact quad-channel spin exchange relaxation-free magnetometer

We observed the steady-state visually evoked potential(SSVEP) from a healthy subject using a compact quad-channel potassium spin exchange relaxation-free(SERF) optically pumped magnetometer(OPM). To this end, 30 s of data were collected, and SSVEP-related magnetic responses with signal intensity ranging from 150 fT to 300 f T were observed for all four channels. The corresponding signal to noise ratio(SNR) was in the range of 3.5–5.5. We then used different channels to operate the sensor as a gradiometer. In the specific case of detecting SSVEP, we noticed that the short channel separation distance led to a strongly diminished gradiometer signal. Although not optimal for the case of SSVEP detection, this set-up can prove to be highly useful for other magnetoencephalography(MEG) paradigms that require good noise cancellation.Considering its compactness, low cost, and good performance, the K-SERF sensor has great potential for biomagnetic field measurements and brain-computer interfaces(BCI).

Influence of stimuli color on steady-state visual evoked potentials based BCI wheelchair control

In recent years, Brain Computer Interface (BCI) systems based on Steady-State Visual Evoked Potential (SSVEP) have received much attention. This study tries to develop a SSVEP based BCI system that can control a wheelchair prototype in five different positions including stop position. In this study four different flickering frequencies in low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using Lab-VIEW. Four stimuli colors, green, red, blue and violet were used to investigate the color influence in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital region were segmented into 1 second window and features were extracted by using Fast Fourier Transform (FFT). One-Against-All (OAA), a popular strategy for multiclass SVM, is used to classify SSVEP signals. During stimuli color comparison SSVEP with violet color showed higher accuracy than that with green, red and blue stimuli.

A Secure Cryptographic System Based on Steady-State Visual Evoked Potential Brain-Computer Interface Technology

Addressing the vulnerability of contact-based keyboard password systems to disclosure,this paper proposes and validates the feasibility of a non-contact secure password system based on brain-computer interface(BCI)technology that detects steady-state visual evoked potential(SSVEP)signals.The system first lets a testee look at a digital stimulus source flashing at a specific frequency,and uses a wearable dry electrode sensor to collect the SSVEP signal.Secondly,a canonical correlation analysis method is applied to analyze the frequency of the stimulus source that the testee is looking at,and feeds back a code result through headphones.Finally,after all password codes are input,the system makes a judgment and provides visual feedback to the testee.Experiments were conducted to test the accuracy of the system,where twelve stimulus target frequencies between 10-16Hz were selected within the easily recognizable flicker frequency range of human brain,and each of them was tested for 12 times.The results demonstrate that this SSVEP-BCI-based system is feasible,achieving an average accuracy rate of 97.2%,and exhibits promising applications in various domains such as financial transactions and identity recognition.

Review of brain-computer interface based on steady-state visual evoked potential

The brain-computer interface(BCI)technology has received lots of attention in the field of scientific research because it can help disabled people improve their quality of life.Steady-state visual evoked potential(SSVEP)is the most researched BCI experimental paradigm,which offers the advantages of high signal-to-noise ratio and short training-time requirement by users.In a complete BCI system,the two most critical components are the experimental paradigm and decoding algorithm.However,a systematic combination of the SSVEP experimental paradigm and decoding algorithms is missing in existing studies.In the present study,the transient visual evoked potential,SSVEP,and various improved SSVEP paradigms are compared and analyzed,and the problems and development bottlenecks in the experimental paradigm are finally pointed out.Subsequently,the canonical correlation analysis and various improved decoding algorithms are introduced,and the opportunities and challenges of the SSVEP decoding algorithm are discussed.

Studying the Effect of the Pre-Stimulation Paradigm on Steady-State Visual Evoked Potentials with Dynamic Models Based on the Zero-Pole Analytical Method

This study explored methods for improving the performance of Steady-State Visual Evoked Potential(SSVEP)-based Brain-Computer Interfaces(BCI), and introduced a new analytical method to quantitatively analyze and reflect the characteristics of SSVEP. We focused on the effect of the pre-stimulation paradigm on the SSVEP dynamic models and the dynamic response process of SSVEP, and performed a comparative analysis of three pre-stimulus paradigms(black, gray, and white). Four dynamic models with different orders(second-and third-order)and with and without a zero point were used to fit the SSVEP envelope. The zero-pole analytical method was adopted to conduct quantitative analysis on the dynamic models, and the response characteristics of SSVEP were represented by zero-pole distribution characteristics. The results of this study indicated that the pre-stimulation paradigm affects the characteristics of SSVEP, and the dynamic models had good fitting abilities with SSVEPs under various types of pre-stimulation. Furthermore, the zero-pole characteristics of the models effectively characterize the damping coefficient, oscillation period, and other SSVEP characteristics. The comparison of zeros and poles indicated that the gray pre-stimulation condition corresponds to a lower damping coefficient, thus showing its potential to improve the performance of SSVEP-BCIs.

Effect of background luminance of visual stimulus on elicited steady-state visual evoked potentials

Steady-state visual evoked potential(SSVEP)-based brain-computer interfaces(BCIs)have been widely studied.Considerable progress has been made in the aspects of stimulus coding,electroencephalogram processing,and recognition algorithms to enhance system performance.The properties of SSVEP have been demonstrated to be highly sensitive to stimulus luminance.However,thus far,there have been very few reports on the impact of background luminance on the system performance of SSVEP-based BCIs.This study investigated the impact of stimulus background luminance on SSVEPs.Specifically,this study compared two types of background luminance,i.e.,(1)black luminance[red,green,blue(rgb):(0,0,0)]and(2)gray luminance[rgb:(128,128,128)],and determined their effect on the classification performance of SSVEPs at the stimulus frequencies of 9,11,13,and 15 Hz.The offline results from nine healthy subjects showed that compared with the gray background luminance,the black background luminance induced larger SSVEP amplitude and larger signal-to-noise ratio,resulting in a better classification accuracy.These results suggest that the background luminance of visual stimulus has a considerable effect on the SSVEP and therefore has a potential to improve the BCI performance.

Brain-computer control interface design for virtual household appliances based on steady-state visually evoked potential recognition

Brain–computer interface is a new form of interaction between humans and machines.This interaction helps the human brain control or operate external devices directly using electroencephalograph(EEG)signals.In this study,we first adopt a canonical correlation analysis method to find the stimulation frequency by calculating the correlation coefficient between the EEG data and multiple sets of harmonics with different frequencies.Then,we select the maximum correlation coefficient as the stimulus frequency and consequently identify steady-state visual evoked potentials.Afterward,we introduce power spectral density to adjust the stimulus frequency and a voting mechanism to reduce the false activation rate.Finally,we build a virtual household electrical appliance brain–computer control interface,which achieves over 72.84%accuracy for three classification problems.

A Hybrid Brain-Computer Interface for Closed-Loop Position Control of a Robot Arm

Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most of the existing schemes employ open-loop control,and thus are unable to track positional errors,resulting in failures in taking necessary online corrective actions.There are examples of a few works dealing with closed-loop electroencephalography(EEG)-based position control.These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule,which often creates a bottleneck preventing time-efficient control.Second,the existing brain-induced position controllers are designed to generate a position response like a traditional firstorder system,resulting in a large steady-state error.This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential(SSVEP)induced linkselection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots/undershoots to reduce steady-state errors.Other than the above,the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors.Experiments undertaken reveal that the steady-state error is reduced to 0.2%.The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique.

A User-Friendly SSVEP-Based BCI Using Imperceptible Phase-Coded Flickers at 60Hz

A brain-computer interface(BCI)system based on steady-state visual evoked potentials(SSVEP)was developed by four-class phase-coded stimuli.SSVEPs elicited by flickers at 60Hz,which is higher than the critical fusion frequency(CFF),were compared with those at 15Hz and 30Hz.SSVEP components in electroencephalogram(EEG)were detected using task related component analysis(TRCA)method.Offline analysis with 17 subjects indicated that the highest information transfer rate(ITR)was 29.80±4.65bpm with 0.5s data length for 60Hz and the classification accuracy was 70.07±4.15%.The online BCI system reached an averaged classification accuracy of 87.75±3.50%at 60Hz with 4s,resulting in an ITR of 16.73±1.63bpm.In particular,the maximum ITR for a subject was 80bpm with 0.5s at 60Hz.Although the BCI performance of 60Hz was lower than that of 15Hz and 30Hz,the results of the behavioral test indicated that,with no perception of flicker,the BCI system with 60Hz was more comfortable to use than 15Hz and 30Hz.Correlation analysis revealed that SSVEP with higher signal-to-noise ratio(SNR)corresponded to better classification performance and the improvement in comfortableness was accompanied by a decrease in performance.This study demonstrates the feasibility and potential of a user-friendly SSVEP-based BCI using imperceptible flickers.

An extended binary subband canonical correlation analysis detection algorithm oriented to the radial contraction-expansion motion steady- state visual evoked paradigm

The radial contraction-expansion motion paradigm is a novel steady-state visual evoked experimental paradigm,and the electroencephalography(EEG)evoked potential is different from the traditional luminance modulation paradigm.The signal energy is concentrated chiefly in the fundamental frequency,while the higher harmonic power is lower.Therefore,the conventional steady-state visual evoked potential recognition algorithms optimizing multiple harmonic response components,such as the extended canonical correlation analysis(eCCA)and task-related component analysis(TRCA)algorithm,have poor recognition performance under the radial contraction-expansion motion paradigm.This paper proposes an extended binary subband canonical correlation analysis(eBSCCA)algorithm for the radial contraction-expansion motion paradigm.For the radial contraction-expansion motion paradigm,binary subband filtering was used to optimize the weighting coefficients of different frequency response signals,thereby improving the recognition performance of EEG signals.The results of offline experiments involving 13 subjects showed that the eBSCCA algorithm exhibits a better performance than the eCCA and TRCA algorithms under the stimulation of the radial contraction-expansion motion paradigm.In the online experiment,the average recognition accuracy of 13 subjects was 88.68%±6.33%,and the average information transmission rate(ITR)was 158.77±43.67 bits/min,which proved that the algorithm had good recognition effect signals evoked by the radial contraction-expansion motion paradigm.

Multimodal collaborative BCI system based on the improved CSP feature extraction algorithm

Background As a novel approach for people to directly communicate with an external device,the study of brain-computer interfaces(BCIs)has become well-rounded.However,similar to the real-world scenario,where individuals are expected to work in groups,the BCI systems should be able to replicate group attributes.Methods We proposed a 4-order cumulants feature extraction method(CUM4-CSP)based on the common spatial patterns(CSP)algorithm.Simulation experiments conducted using motion visual evoked potentials(mVEP)EEG data verified the robustness of the proposed algorithm.In addition,to freely choose paradigms,we adopted the mVEP and steady-state visual evoked potential(SSVEP)paradigms and designed a multimodal collaborative BCI system based on the proposed CUM4-CSP algorithm.The feasibility of the proposed multimodal collaborative system framework was demonstrated using a multiplayer game controlling system that simultaneously facilitates the coordination and competitive control of two users on external devices.To verify the robustness of the proposed scheme,we recruited 30 subjects to conduct online game control experiments,and the results were statistically analyzed.Results The simulation results prove that the proposed CUM4-CSP algorithm has good noise immunity.The online experimental results indicate that the subjects could reliably perform the game confrontation operation with the selected BCI paradigm.Conclusions The proposed CUM4-CSP algorithm can effectively extract features from EEG data in a noisy environment.Additionally,the proposed scheme may provide a new solution for EEG-based group BCI research.

Navigation in virtual and real environment using brain computer interface:a progress report

A brain-computer interface (BCI) facilitates bypassing the peripheral nervous system and directly communicating with surrounding devices. Navigation technology using BCI has developed-from exploring the prototype paradigm in the virtual environment (VE) to accurately completing the locomotion intention of the operator in the form of a powered wheelchair or mobile robot in a real environment. This paper summarizes BCI navigation applications that have been used in both real and VEs in the past 20 years. Horizontal comparisons were conducted between various paradigms applied to BCI and their unique signal-processing methods. Owing to the shift in the control mode from synchronous to asynchronous, the development trend of navigation applications in the VE was also reviewed. The contrast between high level commands and low-level commands is introduced as the main line to review the two major applications of BCI navigation in real environments: mobile robots and unmanned aerial vehicles (UAVs). Finally, applications of BCI navigation to scenarios outside the laboratory;research challenges, including human factors in navigation application interaction design;and the feasibility of hybrid BCI for BCI navigation are discussed in detail.

AB053.Oscillatory activity specific to peripheral emotional treatment induced by a visual steady state

Background:Research suggests that the analysis of facial expressions by a healthy brain would take place approximately 170 ms after the presentation of a facial expression in the superior temporal sulcus and the fusiform gyrus,mostly in the right hemisphere.Some researchers argue that a fast pathway through the amygdala would allow automatic and early emotional treatment around 90 ms after stimulation.This treatment would be done subconsciously,even before this stimulus is perceived and could be approximated by presenting the stimuli quickly on the periphery of the fovea.The present study aimed to identify the neural correlates of a peripheral and simultaneous presentation of emotional expressions through a frequency tagging paradigm.Methods:The presentation of emotional facial expressions at a specific frequency induces in the visual cortex a stable and precise response to the presentation frequency[i.e.,a steady-state visual evoked potential(ssVEP)]that can be used as a frequency tag(i.e.,a frequency-tag to follow the cortical treatment of this stimulus.Here,the use of different specific stimulation frequencies allowed us to label the different facial expressions presented simultaneously and to obtain a reliable cortical response being associated with(I)each of the emotions and(II)the different times of presentations repeated(1/0.170 ms=~5.8 Hz,1/0.090 ms=~10.8 Hz).To identify the regions involved in emotional discrimination,we subtracted the brain activity induced by the rapid presentation of six emotional expressions of the activity induced by the presentation of the same emotion(reduced by neural adaptation).The results were compared to the hemisphere in which attention was sought,emotion and frequency of stimulation.Results:The signal-to-noise ratio of the cerebral oscillations referring to the treatment of the expression of fear was stronger in the regions specific to the emotional treatment when they were presented in the subjects peripheral vision,unbeknownst to them.In addition,the peripheral emotional treatment of fear at 10.8 Hz was associated with greater activation within the Gamma 1 and 2 frequency bands in the expected regions(frontotemporal and T6),as well as desynchronization in the Alpha frequency bands for the temporal regions.This modulation of the spectral power is independent of the attentional request.Conclusions:These results suggest that the emotional stimulation of fear presented in the peripheral vision and outside the attentional framework elicit an increase in brain activity,especially in the temporal lobe.The localization of this activity as well as the optimal stimulation frequency found for this facial expression suggests that it is treated by the fast pathway of the magnocellular layers.

A Study on SSVEP-Based BCI

-Brain-computer interface （BCI） can help the deformity person finish some basic activities. In this paper, we concern some critical aspects of SSVEP based BCI, including stimulator selection, method of SSVEP extracting in a short time, stimulating frequency selection, and signal electrode selection. The conclusion is that the stimulator type should be based on the complexity of the BCI system, the method based on wavelet analysis is more valid than the power spectrum method in extracting the SSVEP in a short period, and the selections of stimulating frequency and electrode are important in designing a BCI system. These contents are meaningful for implementing a real SSVEP-based BCI.

Adaptation of Ocular Opponency Neurons Mediates Attention-Induced Ocular Dominance Plasticity

Previous research has shown that ocular dominance can be biased by prolonged attention to one eye.The ocular-opponency-neuron model of binocular rivalry has been proposed as a candidate account for this phenomenon.Yet direct neural evidence is still lacking.By manipulating the contrast of dichoptic testing gratings,here we measured the steady-state visually evoked potentials(SSVEPs)at the intermodulation frequencies to selectively track the activities of ocular-opponency-neurons before and after the“dichoptic-backward-movie”adaptation.One hour of adaptation caused a shift of perceptual and neural ocular dominance towards the unattended eye.More importantly,we found a decrease in the intermodulation SSVEP response after adaptation,which was significantly greater when high-contrast gratings were presented to the attended eye than when they were presented to the unattended eye.These results strongly support the view that the adaptation of ocular-opponency-neurons contributes to the ocular dominance plasticity induced by prolonged eye-based attention.

Study on Robot Grasping System of SSVEP-BCI Based on Augmented Reality Stimulus

Although notable progress has been made in the study of Steady-State Visual Evoked Potential(SSVEP)-based Brain-Computer Interface(BCI),several factors that limit the practical applications of BCIs still exist.One of these factors is the importability of the stimulator.In this study,Augmented Reality(AR)technology was introduced to present the visual stimuli of SSVEP-BCI,while the robot grasping experiment was designed to verify the applicability of the AR-BCI system.The offline experiment was designed to determine the best stimulus time,while the online experiment was used to complete the robot grasping task.The offline experiment revealed that better information transfer rate performance could be achieved when the stimulation time is 2 s.Results of the online experiment indicate that all 12 subjects could control the robot to complete the robot grasping task,which indicates the applicability of the AR-SSVEP-humanoid robot(NAO)system.This study verified the reliability of the AR-BCI system and indicated the applicability of the AR-SSVEP-NAO system in robot grasping tasks.

Overview of recognition methods for SSVEP-based BCIs in World Robot Contest 2022: MATLAB undergraduate group

The steady-state visual evoked potential(SSVEP)-based speller has emerged as a widely adopted paradigm in current brain–computer interface(BCI) systems due to its rapid processing and consistent performance across different individuals. Calibration-free SSVEP algorithms, as opposed to their calibration-based counterparts, offer clear and intuitive mathematical principles, making them accessible to novice developers. During the World Robot Contest(WRC)2022, participants in the undergraduate category utilized various approaches to accomplish target detection in the calibration-free setting, successfully implementing the algorithms using MATLAB.The winning approach achieved an average information transfer rate of 198.94 bits/min in the final test, which is notably high given the calibration-free scenario. This paper presents an introduction to the underlying principles of the selected methods, accompanied by a comparison of their effectiveness through analysis of results from both the final test and offline experiments. Additionally, we propose that the youth competition of WRC could serve as an ideal starting point for beginners interested in studying and developing their own BCI systems.

Overview of the winning approaches in 2022 World Robot Contest Championship–Asynchronous SSVEP

In recent years,the steady-state visual evoked potential(SSVEP)electroencephalogram paradigm has gained considerable attention owing to its high information transfer rate.Several approaches have been proposed to improve the performance of SSVEP-based brain–computer interface(BCI)systems.In SSVEP-based BCIs,the asynchronous scenario poses a challenge as the subjects stare at the screen without synchronization signals from the system.The algorithm must distinguish whether the subject is being stimulated or not,which presents a significant challenge for accurate classification.In the 2022 World Robot Contest Championship,several effective algorithm frameworks were proposed by participating teams to address this issue in the SSVEP competition.The efficacy of the approaches employed by five teams in the final round is demonstrated in this study,and an overview of their methods is provided.Based on the final score,this paper presents a comparative analysis of five algorithms that propose distinct asynchronous recognition frameworks via diverse statistical methods to differentiate between intentional control state and non-control state based on dynamic window strategies.These algorithms achieve an impressive information transfer rate of 89.833 and a low false positive rate of 0.073.This study provides an overview of the algorithms employed by different teams to address asynchronous scenarios in SSVEP-based BCIs and identifies potential future avenues for research in this area.

Winning algorithms in BCI Controlled Robot Contest in World Robot Contest 2022:BCI Turing Test

The Turing Test is a method of testing whether a machine has human intelligence.A novel brain–computer interface(BCI)Turing Test was proposed in the BCI Controlled Robot Contest in World Robot Contest 2022.Contestants developed algorithms that can distinguish if an instruction is issued by a human.Participants collaborated with an electroencephalogram-based BCI to play a soccer game in a virtual scenario.Participants were asked to perform steady-state visual evoked potential(SSVEP)tasks or motor imagery(MI)tasks to control the robots or be in an idle state to mimic the system giving instructions on behalf of the participants.Several algorithms proposed in this competition are developed based on the concept that the idle state is a category in multiclass classification problems.This paper details the algorithms of the top five teams with the best performance in the final,lists the popular classification models and algorithms for MI and SSVEP,and discusses the effectiveness of each approach in improving classification performance and reducing the computation time.

Improving the performance of SSVEP-BCI contaminated by physiological noise via adversarial training

Brain-computer interface(BCI)based on Steady-State Visual Evoked Potentials(SSVEP)provides an effective method for human-computer communication.In practical application scenarios,SSVEP-BCI systems are easily interfered by physiological noises such as electromyography(EMG)and electrooculography(EOG).The performance of traditional SSVEP recognition methods will degrade in such a noisy environment,which limits their real-world applications.To alleviate the interference of noise,existing works either require additional reference electrodes or are designed for removing background noise such as trend terms rather than physiological noises.In this study,we utilize adversarial training(AT)and neural networks(NNs)to construct a robust recognition method for SSVEP contaminated by physiological noise.During model training,we generate adversarial noises which are most harmful to the current model according to gradients and enforce the model to overcome them.In this way,we strengthen the robustness of the model to potential noises,such as physiological noises.In this study,we recorded a real-world speaking SSVEP dataset and simulated various noisy datasets to conducted comparison experiments on two benchmark models named EEGNet and DeepConvNet.The experimental results demonstrated that AT strategies can help the neural networks get better performance on SSVEP data contaminated by EMG and EOG.We also verified that introducing AT can slightly improve the performance of models under a cross-subject scenario.Our method can be integrated into existing deep learning methods efficiently and will contribute to the real-world applications of SSVEP.