High-speed spelling with a noninvasive brain-computer interface

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.

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.

EEG Measurement for Suppression in Refractive Amblyopia and Push-pull Perception Efficacy

In order to evaluate refractive amblyopia suppression and understand the neural mechanism of amblyopia suppression and push-pull perception training, we recorded the EEG of refractive amblyopia children before, during, and after push-pull perception training. We compared the brain activity in different states through the steady-state visual evoked potentials (SSVEPs) response and power topography and compared them with normal children. We found that amblyopic and fellow eyes have different performances in fundamental and harmonic frequency responses. They also show different characteristics when be masked. Push-pull perception training improved the SSVEP performance of amblyopia children by reducing the SSVEP response difference between eyes and improving the intermodulation frequency response. The result of topography showed that push-pull perception reduced the alpha power of occipital and temporal lobes, which was conducive to improving binocular function. The changes of intermodulation response and occipital alpha power were significantly correlated with the clinical indicator. Thus, EEG is a potential method to measure amblyopia suppression and the efficacy of push-pull perception.

Task-Related Component Analysis Combining Paired Character Decoding for Miniature Asymmetric Visual Evoked Potentials

Brain-computer interface (BCI) technology based on event-related potentials (ERP) of electroencephalography (EEG) is widely used in daily life and medical treatment. However, the research of identifying the miniature and more informative asymmetric visual evoked potentials (aVEPs), which belongs to ERP, needs further exploration. Herein, a task-related component analysis combining paired character decoding (TRCA-PCD) method, which can enhance reproducibility of aVEPs in multiple trials and strengthen the features of different samples, was designed to realize fast decoding of aVEPs. The BCI performance and the influence of repetition times between the TRCA-PCD method, the discriminative canonical pattern matching (DCPM) method and traditional task-related component analysis (TRCA) method were compared using a 32-class aVEPs dataset recorded from 32 subjects. The highest average recognition accuracy and information transfer rate (ITR) of TRCA-PCD after parameter selection were 70.37 ± 2.49% (DCPM: 64.91 ± 2.81%, TRCA: 44.01 ± 3.25%) with the peak value of 97.92% and 28.90 ± 3.83 bits/min (DCPM: 21.29 ± 3.35 bits/min, TRCA: 11.54 ± 2.81 bits/min) with the peak value of 94.55 bits/min respectively. Statistical analysis indicated that the highest average recognition rate could be obtained when the repetition time was six, and the highest ITR could be obtained when the repetition time was one. Overall, the results verified the effectiveness and superiority of TRCA-PCD in recognition of aVEPs and provided a reference for parameter selection. Therefore, the TRCA-PCD method can promote the further application of aVEPs in the BCI speller field.

Cross-subject spatial filter transfer method for SSVEP-EEG feature recognition

Objective.Steady-state visual evoked potential (SSVEP) is an important control method of the brain-computer interface (BCI) system. The development of an efficient SSVEP feature decoding algorithm is the core issue in SSVEP-BCI. It has been proposed to use user training data to reduce the spontaneous electroencephalogram activity interference on SSVEP response, thereby improving the feature recognition accuracy of the SSVEP signal. Nevertheless, the tedious data collection process increases the mental fatigue of the user and severely affects the applicability of the BCI system.Approach.A cross-subject spatial filter transfer (CSSFT) method that transfer the existing user model with good SSVEP response to the new user test data without collecting any training data from the new user is proposed.Main results.Experimental results demonstrate that the transfer model increases the distinction of the feature discriminant coefficient between the gaze following target and the non-gaze following target and accurately identifies the wrong target in the fundamental algorithm model. The public datasets show that the CSSFT method significantly increases the recognition performance of canonical correlation analysis (CCA) and filter bank CCA. Additionally, when the data used to calculate the transfer model contains one data block only, the CSSFT method retains its effective feature recognition capabilities.Significance.The proposed method requires no tedious data calibration process for new users, provides an effective technical solution for the transfer of the cross-subject model, and has potential application value for promoting the application of the BCI system.

An MVMD-CCA Recognition Algorithm in SSVEP-Based BCI and Its Application in Robot Control

This article proposes a novel recognition algorithm for the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) system. By combining the advantages of multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA), an MVMD-CCA algorithm is investigated to improve the detection ability of SSVEP electroencephalogram (EEG) signals. In comparison with the classical filter bank canonical correlation analysis (FBCCA), the nonlinear and non-stationary EEG signals are decomposed into a fixed number of sub-bands by MVMD, which can enhance the effect of SSVEP-related sub-bands. The experimental results show that MVMD-CCA can effectively reduce the influence of noise and EEG artifacts and improve the performance of SSVEP-based BCI. The offline experiments show that the average accuracies of MVMD-CCA in the training dataset and testing dataset are improved by 3.08% and 1.67%, respectively. In the SSVEP-based online robotic manipulator grasping experiment, the recognition accuracies of the four subjects are 92.5%, 93.33%, 90.83%, and 91.67%, respectively.

The effect of stimulus number on the recognition accuracy and information transfer rate of SSVEP-BCI in augmented reality

OBJECTIVE

The biggest advantage of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) lies in its large command set and high information transfer rate (ITR). Almost all current SSVEP-BCIs use a computer screen (CS) to present flickering visual stimuli, which limits its flexible use in actual scenes. Augmented reality (AR) technology provides the ability to superimpose visual stimuli on the real world, and it considerably expands the application scenarios of SSVEP-BCI. However, whether the advantages of SSVEP-BCI can be maintained when moving the visual stimuli to AR glasses is not known. This study investigated the effects of the stimulus number for SSVEP-BCI in an AR context.

APPROACH

We designed SSVEP flickering stimulation interfaces with four different numbers of stimulus targets and put them in AR glasses and a CS to display. Three common recognition algorithms were used to analyze the influence of the stimulus number and stimulation time on the recognition accuracy and ITR of AR-SSVEP and CS-SSVEP.

MAIN RESULTS

The amplitude spectrum and signal-to-noise ratio of AR-SSVEP were not significantly different from CS-SSVEP at the fundamental frequency but were significantly lower than CS-SSVEP at the second harmonic. SSVEP recognition accuracy decreased as the stimulus number increased in AR-SSVEP but not in CS-SSVEP. When the stimulus number increased, the maximum ITR of CS-SSVEP also increased, but not for AR-SSVEP. When the stimulus number was 25, the maximum ITR (142.05 bits/min) was reached at 400 ms. The importance of stimulation time in SSVEP was confirmed. When the stimulation time became longer, the recognition accuracy of both AR-SSVEP and CS-SSVEP increased. The peak value was reached at 3 s. The ITR increased first and then slowly decreased after reaching the peak value.

SIGNIFICANCE

Our study indicates that the conclusions based on CS-SSVEP cannot be simply applied to AR-SSVEP, and it is not advisable to set too many stimulus targets in the AR display device.

Toward a Brain-Computer Interface- and Internet of Things-Based Smart Ward Collaborative System Using Hybrid Signals

This study proposes a brain-computer interface (BCI)- and Internet of Things (IoT)-based smart ward collaborative system using hybrid signals. The system is divided into hybrid asynchronous electroencephalography (EEG)-, electrooculography (EOG)- and gyro-based BCI control system and an IoT monitoring and management system. The hybrid BCI control system proposes a GUI paradigm with cursor movement. The user uses the gyro to control the cursor area selection and uses blink-related EOG to control the cursor click. Meanwhile, the attention-related EEG signals are classified based on a support-vector machine (SVM) to make the final judgment. The judgment of the cursor area and the judgment of the attention state are reduced, thereby reducing the false operation rate in the hybrid BCI system. The accuracy in the hybrid BCI control system was 96.65 ± 1.44%, and the false operation rate and command response time were 0.89 ± 0.42 events/min and 2.65 ± 0.48 s, respectively. These results show the application potential of the hybrid BCI control system in daily tasks. In addition, we develop an architecture to connect intelligent things in a smart ward based on narrowband Internet of Things (NB-IoT) technology. The results demonstrate that our system provides superior communication transmission quality.

A Personalized Compression Method for Steady-State Visual Evoked Potential EEG Signals

As an informative electroencephalogram (EEG) signal, steady-state visual evoked potential (SSVEP) stands out from many paradigms for application in wireless wearable devices. However, its data are usually enormous, occupy too many bandwidth sources and require immense power when transmitted in the raw data form, so it is necessary to compress the signal. This paper proposes a personalized EEG compression and reconstruction algorithm for the SSVEP application. In the algorithm, to realize personalization, a primary artificial neural network (ANN) model is first pre-trained with the open benchmark database towards BCI application (BETA). Then, an adaptive ANN model is generated with incremental learning for each subject to compress their individual data. Additionally, a personalized, non-uniform quantization method is proposed to reduce the errors caused by compression. The recognition accuracy only decreases by 3.79% when the compression rate is 12.7 times, and is tested on BETA. The proposed algorithm can reduce signal loss by from 50.43% to 81.08% in the accuracy test compared to the case without ANN and uniform quantization.

The science and engineering behind sensitized brain-controlled bionic hands

Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.

Ten-Hour Stable Noninvasive Brain-Computer Interface Realized by Semidry Hydrogel-Based Electrodes

Noninvasive brain-computer interface (BCI) has been extensively studied from many aspects in the past decade. In order to broaden the practical applications of BCI technique, it is essential to develop electrodes for electroencephalogram (EEG) collection with advanced characteristics such as high conductivity, long-term effectiveness, and biocompatibility. In this study, we developed a silver-nanowire/PVA hydrogel/melamine sponge (AgPHMS) semidry EEG electrode for long-lasting monitoring of EEG signal. Benefiting from the water storage capacity of PVA hydrogel, the electrolyte solution can be continuously released to the scalp-electrode interface during used. The electrolyte solution can infiltrate the stratum corneum and reduce the scalp-electrode impedance to 10 kΩ-15 kΩ. The flexible structure enables the electrode with mechanical stability, increases the wearing comfort, and reduces the scalp-electrode gap to reduce contact impedance. As a result, a long-term BCI application based on measurements of motion-onset visual evoked potentials (mVEPs) shows that the 3-hour BCI accuracy of the new electrode (77% to 100%) is approximately the same as that of conventional electrodes supported by a conductive gel during the first hour. Furthermore, the BCI system based on the new electrode can retain low contact impedance for 10 hours on scalp, which greatly improved the ability of BCI technique.

Security Requirements and Challenges of 6G Technologies and Applications

After implementing 5G technology, academia and industry started researching 6th generation wireless network technology (6G). 6G is expected to be implemented around the year 2030. It will offer a significant experience for everyone by enabling hyper-connectivity between people and everything. In addition, it is expected to extend mobile communication possibilities where earlier generations could not have developed. Several potential technologies are predicted to serve as the foundation of 6G networks. These include upcoming and current technologies such as post-quantum cryptography, artificial intelligence (AI), machine learning (ML), enhanced edge computing, molecular communication, THz, visible light communication (VLC), and distributed ledger (DL) technologies such as blockchain. From a security and privacy perspective, these developments need a reconsideration of prior security traditional methods. New novel authentication, encryption, access control, communication, and malicious activity detection must satisfy the higher significant requirements of future networks. In addition, new security approaches are necessary to ensure trustworthiness and privacy. This paper provides insights into the critical problems and difficulties related to the security, privacy, and trust issues of 6G networks. Moreover, the standard technologies and security challenges per each technology are clarified. This paper introduces the 6G security architecture and improvements over the 5G architecture. We also introduce the security issues and challenges of the 6G physical layer. In addition, the AI/ML layers and the proposed security solution in each layer are studied. The paper summarizes the security evolution in legacy mobile networks and concludes with their security problems and the most essential 6G application services and their security requirements. Finally, this paper provides a complete discussion of 6G networks’ trustworthiness and solutions.

Novel Hybrid Brain-Computer Interface for Virtual Reality Applications Using Steady-State Visual-Evoked Potential-Based Brain-Computer Interface and Electrooculogram-Based Eye Tracking for Increased Information Transfer Rate

Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have recently attracted increasing attention in virtual reality (VR) applications as a promising tool for controlling virtual objects or generating commands in a "hands-free" manner. Video-oculography (VOG) has been frequently used as a tool to improve BCI performance by identifying the gaze location on the screen, however, current VOG devices are generally too expensive to be embedded in practical low-cost VR head-mounted display (HMD) systems. In this study, we proposed a novel calibration-free hybrid BCI system combining steady-state visual-evoked potential (SSVEP)-based BCI and electrooculogram (EOG)-based eye tracking to increase the information transfer rate (ITR) of a nine-target SSVEP-based BCI in VR environment. Experiments were repeated on three different frequency configurations of pattern-reversal checkerboard stimuli arranged in a 3 × 3 matrix. When a user was staring at one of the nine visual stimuli, the column containing the target stimulus was first identified based on the user's horizontal eye movement direction (left, middle, or right) classified using horizontal EOG recorded from a pair of electrodes that can be readily incorporated with any existing VR-HMD systems. Note that the EOG can be recorded using the same amplifier for recording SSVEP, unlike the VOG system. Then, the target visual stimulus was identified among the three visual stimuli vertically arranged in the selected column using the extension of multivariate synchronization index (EMSI) algorithm, one of the widely used SSVEP detection algorithms. In our experiments with 20 participants wearing a commercial VR-HMD system, it was shown that both the accuracy and ITR of the proposed hybrid BCI were significantly increased compared to those of the traditional SSVEP-based BCI in VR environment.

Effects of inter-stimulus intervals on concurrent P300 and SSVEP features for hybrid Brain-computer interfaces

BACKGROUND

Recently, we have implemented a high-speed brain-computer interface (BCI) system with a large instruction set using the concurrent P300 and steady-state visual evoked potential (SSVEP) features (also known as hybrid features). However, it remains unclear how to select inter-stimulus interval (ISI) for the proposed BCI system to balance the encoding efficiency and decoding performance.

NEW METHOD

This study developed a 6⁎9 hybrid P300-SSVEP BCI system and investigated a series of ISIs ranged from -175ms to 0ms with a step of 25ms. The influence of ISI on the hybrid features was analyzed from several aspects, including the amplitude of the induced features, classification accuracy, information transfer rate (ITR). Twelve naive subjects were recruited for the experiment.

RESULTS

The results showed the ISI factor had a significant impact on the hybrid features. Specifically, as the values of ISI decreased, the amplitudes of the induced features and accuracies decreased gradually, while the ITRs increased rapidly. It's achieved the highest ITR of 158.50 bits/min when ISI equal to -175ms.

COMPARISON WITH EXISTING METHOD

The optimal ISI in this study achieved superior performance in comparison with the one we used in the previous study.

CONCLUSIONS

The ISI can exert an important influence on the P300-SSVEP BCI system and its optimal value is -175ms in this study, which is significant for developing the high-speed BCI system with larger instruction sets in the future.

A Hybrid Brain-Computer Interface Based on Visual Evoked Potential and Pupillary Response

Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has been widely studied due to the high information transfer rate (ITR), little user training, and wide subject applicability. However, there are also disadvantages such as visual discomfort and “BCI illiteracy.” To address these problems, this study proposes to use low-frequency stimulations (12 classes, 0.8–2.12 Hz with an interval of 0.12 Hz), which can simultaneously elicit visual evoked potential (VEP) and pupillary response (PR) to construct a hybrid BCI (h-BCI) system. Classification accuracy was calculated using supervised and unsupervised methods, respectively, and the hybrid accuracy was obtained using a decision fusion method to combine the information of VEP and PR. Online experimental results from 10 subjects showed that the averaged accuracy was 94.90 ± 2.34% (data length 1.5 s) for the supervised method and 91.88 ± 3.68% (data length 4 s) for the unsupervised method, which correspond to the ITR of 64.35 ± 3.07 bits/min (bpm) and 33.19 ± 2.38 bpm, respectively. Notably, the hybrid method achieved higher accuracy and ITR than that of VEP and PR for most subjects, especially for the short data length. Together with the subjects’ feedback on user experience, these results indicate that the proposed h-BCI with the low-frequency stimulation paradigm is more comfortable and favorable than the traditional SSVEP-BCI paradigm using the alpha frequency range.

SSVEP-EEG Feature Enhancement Method Using an Image Sharpening Filter

Steady-state visual evoked potential (SSVEP) is widely used in brain computer interface (BCI), medical detection, and neuroscience, so there is significant interest in enhancing SSVEP features via signal processing for better performance. In this study, an image processing method was combined with brain signal analysis and a sharpening filter was used to extract image details and features for the enhancement of SSVEP features. The results demonstrated that sharpening filter could eliminate the SSVEP signal trend term and suppress its low-frequency component. Meanwhile, sharpening filter effectively enhanced the signal-to-noise ratios (SNRs) of the single-channel and multi-channel fused signals. Image sharpening filter also significantly improved the recognition accuracy of canonical correlation analysis (CCA), filter bank canonical correlation analysis (FBCCA), and task-related component analysis (TRCA). The tools developed here effectively enhanced the SSVEP signal features, suggesting that image processing methods can be considered for improved brain signal analysis.

GABAB receptor modulation of visual sensory processing in adults with and without autism spectrum disorder

[Figure: see text].

A BCI-Based Study on the Relationship Between the SSVEP and Retinal Eccentricity in Overt and Covert Attention

Covert attention aids us in monitoring the environment and optimizing performance in visual tasks. Past behavioral studies have shown that covert attention can enhance spatial resolution. However, electroencephalography (EEG) activity related to neural processing between central and peripheral vision has not been systematically investigated. Here, we conducted an EEG study with 25 subjects who performed covert attentional tasks at different retinal eccentricities ranging from 0.75° to 13.90°, as well as tasks involving overt attention and no attention. EEG signals were recorded with a single stimulus frequency to evoke steady-state visual evoked potentials (SSVEPs) for attention evaluation. We found that the SSVEP response in fixating at the attended location was generally negatively correlated with stimulus eccentricity as characterized by Euclidean distance or horizontal and vertical distance. Moreover, more pronounced characteristics of SSVEP analysis were also acquired in overt attention than in covert attention. Furthermore, offline classification of overt attention, covert attention, and no attention yielded an average accuracy of 91.42%. This work contributes to our understanding of the SSVEP representation of attention in humans and may also lead to brain-computer interfaces (BCIs) that allow people to communicate with choices simply by shifting their attention to them.

Optimizing a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear

Objective.Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has the characteristics of fast communication speed, high stability, and wide applicability, thus it has been widely studied. With the rapid development in paradigm, algorithm, and system design, SSVEP-BCI is gradually applied in clinical and real-life scenarios. In order to improve the ease of use of the SSVEP-BCI system, many studies have been focusing on developing it on the hairless area, but due to the lack of redesigning the stimulation paradigm to better adapt to the new area, the electroencephalography response in the hairless area is worse than occipital region.Approach. This study first proposed a phase difference estimation method based on stimulating the left and right visual field separately, then developed and optimized a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear.Main results.In the 12-target online experiment, after a short model estimation training, all 16 subjects used their best stimulus condition. The paradigm designed in this study can increase the proportion of applicable subjects for the behind-ear SSVEP-BCI system from 58.3% to 75% and increase the accuracy from 74.6 ± 20.0% (the existing best SSVEP stimulus with hairless region behind the ear) to 84.2±14.7%, and the information transfer rate from 14.2±6.4 bits min^-1to 17.8±5.7 bits min^-1.Significance.These results demonstrated that the proposed paradigm can effectively improve the BCI performance using the signal from the hairless region behind the ear, compared with the standard SSVEP stimulation paradigm.

tACS facilitates flickering driving by boosting steady-state visual evoked potentials

Objective.There has become of increasing interest in transcranial alternating current stimulation (tACS) since its inception nearly a decade ago. tACS in modulating brain state is an active area of research and has been demonstrated effective in various neuropsychological and clinical domains. In the visual domain, much effort has been dedicated to brain rhythms and rhythmic stimulation, i.e. tACS. However, less is known about the interplay between the rhythmic stimulation and visual stimulation.Approach.Here, we used steady-state visual evoked potential (SSVEP), induced by flickering driving as a widely used technique for frequency-tagging, to investigate the aftereffect of tACS in healthy human subjects. Seven blocks of 64-channel electroencephalogram were recorded before and after the administration of 20min 10Hz tACS, while subjects performed several blocks of SSVEP tasks. We characterized the physiological properties of tACS aftereffect by comparing and validating the temporal, spatial, spatiotemporal and signal-to-noise ratio (SNR) patterns between and within blocks in real tACS and sham tACS.Main results.Our result revealed that tACS boosted the 10Hz SSVEP significantly. Besides, the aftereffect on SSVEP was mitigated with time and lasted up to 5 min.Significance.Our results demonstrate the feasibility of facilitating the flickering driving by external rhythmic stimulation and open a new possibility to alter the brain state in a direction by noninvasive transcranial brain stimulation.

An autonomous hybrid brain-computer interface system combined with eye-tracking in virtual environment

BACKGROUND

Brain-computer interface (BCI) has become an effective human-machine interactive way. However, the performance of the traditional BCI system needs to be further improved, such as flexibility, robustness, and accuracy. We aim to develop an autonomous hybrid BCI system combined with eye-tracking for the control tasks in the virtual environment.

NEW METHOD

This work developed an autonomous control strategy and proposed an effective fusion method for electroencephalogram (EEG) and eye tracking. For the autonomous control, the sliding window method was adopted to analyze the user's eye-gaze data. When the variance of eye-gaze data was less than the threshold, target recognition was triggered. EEG and eye-gaze data were synchronously collected and fused for classification. In addition, a fusion method based on particle swarm optimization (PSO) was proposed, which can find the best fusion weights to adapt to the differences of single modalities.

RESULTS

EEG data and eye-gaze data of 15 subjects in steady-state visual evoked potentials (SSVEP) tasks were collected to evaluate the effectiveness of the hybrid BCI system. The results showed that the PSO fusion method performed best in all fusion methods. And the proposed hybrid BCI system obtained higher accuracy and information transfer rate (ITR) than the single-modality.

COMPARISON WITH EXISTING METHODS

The PSO fusion method was compared with average weighting fusion, prior weighting fusion, support vector machine, decision tree, random forest, and extreme random tree.

CONCLUSION

The proposed methods of autonomous control and dual-modal fusion can improve the flexibility, robustness and classification performance of the hybrid BCI system.

Online Adaptation Boosts SSVEP-Based BCI Performance

OBJECTIVE

A user-friendly steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) prefers no calibration for its target recognition algorithm, however, the existing calibration-free schemes perform still far behind their calibration-based counterparts. To tackle this issue, learning online from the subject's unlabeled data is investigated as a potential approach to boost the performance of the calibration-free SSVEP-based BCIs.

METHODS

An online adaptation scheme is developed to tune the spatial filters using the online unlabeled data from previous trials, and then developing the online adaptive canonical correlation analysis (OACCA) method.

RESULTS

A simulation study on two public SSVEP datasets (Dataset I and II) with a total of 105 subjects demonstrated that the proposed online adaptation scheme can boost the CCA's averaged information transfer rate (ITR) from 94.60 to 158.87 bits/min in Dataset I and from 85.80 to 123.91 bits/min in Dataset II. Furthermore, in our online experiment it boosted the CCA's ITR from 55.81 bits/min to 95.73 bits/min. More importantly, this online adaptation scheme can be easily combined with any spatial filtering-based algorithms to achieve online learning.

CONCLUSION

By online adaptation, the proposed OACCA performed much better than the calibration-free CCA, and comparable to the calibration-based algorithms.

SIGNIFICANCE

This work provides a general way for the SSVEP-based BCIs to learn online from unlabeled data and thus avoid calibration.

Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter

Electroencephalogram (EEG) modeling in brain-computer interface (BCI) provides a theoretical foundation for its development. However, limited by the lack of guidelines in model parameter selection and the inability to obtain personal tissue information in practice, EEG modeling in BCI is mainly focused on the theoretical qualitative level which shows a gap between the theory and its application. Based on such problems, this work combined the surface EEG simulation with a converter based on the generative adversarial network (GAN), to establish the connection from simulated EEG to its application in BCI classification. For the scalp EEGs modeling, a mathematical model was built according to the physics of surface EEG, which consisted of the parallel 3-population neural mass model, the equivalent dipole, and the forward computation. For application, a converter based on the conditional GAN was designed, to transfer the simulated theoretical-only EEG to its practical version, in the lack of individual bio-information. To verify the feasibility, based on the latest microexpression-assisted BCI paradigm proposed by our group, the converted simulated EEGs were used in the training of BCI classifiers. The results indicated that, compared with training with insufficient real data, by adding the simulated EEGs, the overall performance showed a significant improvement (P = 0.04 < 0.05), and the test performance can be improved by 2.17% ± 4.23, in which the largest increase was up to 12.60% ± 1.81. Through this work, the link from theoretical EEG simulation to BCI classification has been initially established, providing an enhanced novel solution for the application of EEG modeling in BCI.

Filter Bank Convolutional Neural Network for Short Time-Window Steady-State Visual Evoked Potential Classification

Convolutional neural network (CNN) has been gradually applied to steady-state visual evoked potential (SSVEP) of the brain-computer interface (BCI). Frequency-domain features extracted by fast Fourier Transform (FFT) or time-domain signals are used as network input. In the frequency-domain diagram, the features at the short time-window are not obvious and the phase information of each electrode channel may be ignored as well. Hence we propose a time-domain-based CNN method (tCNN), using the time-domain signal as network input. And the filter bank tCNN (FB-tCNN) is further proposed to improve its performance in the short time-window. We compare FB-tCNN with the canonical correlation analysis (CCA) methods and other CNN methods in our dataset and public dataset. And FB-tCNN shows superior performance at the short time-window in the intra-individual test. At the 0.2 s time-window, the accuracy of our method reaches 88.36 ± 4.89 % in our dataset, 77.78 ± 2.16 % and 79.21 ± 1.80 % respectively in the two sessions of the public dataset, which is higher than other methods. The impacts of training-subject number and data length in inter-individual or cross-individual are studied. FB-tCNN shows the potential in implementing inter-individual BCI. Further analysis shows that the deep learning method is easier in terms of the implementation of the asynchronous BCI system than the training data-driven CCA. The code is available for reproducibility at https://github.com/DingWenl/FB-tCNN.

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions

Brain-Computer Interface (BCI) is a technology that uses electroencephalographic (EEG) signals to control external devices, such as Functional Electrical Stimulation (FES). Visual BCI paradigms based on P300 and Steady State Visually Evoked potentials (SSVEP) have shown high potential for clinical purposes. Numerous studies have been published on P300- and SSVEP-based non-invasive BCIs, but many of them present two shortcomings: (1) they are not aimed for motor rehabilitation applications, and (2) they do not report in detail the artificial intelligence (AI) methods used for classification, or their performance metrics. To address this gap, in this paper the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to prepare a systematic literature review (SLR). Papers older than 10 years, repeated or not related to a motor rehabilitation application, were excluded. Of all the studies, 51.02% referred to theoretical analysis of classification algorithms. Of the remaining, 28.48% were for spelling, 12.73% for diverse applications (control of wheelchair or home appliances), and only 7.77% were focused on motor rehabilitation. After the inclusion and exclusion criteria were applied and quality screening was performed, 34 articles were selected. Of them, 26.47% used the P300 and 55.8% the SSVEP signal. Five applications categories were established: Rehabilitation Systems (17.64%), Virtual Reality environments (23.52%), FES (17.64%), Orthosis (29.41%), and Prosthesis (11.76%). Of all the works, only four performed tests with patients. The most reported machine learning (ML) algorithms used for classification were linear discriminant analysis (LDA) (48.64%) and support vector machine (16.21%), while only one study used a deep learning algorithm: a Convolutional Neural Network (CNN). The reported accuracy ranged from 38.02 to 100%, and the Information Transfer Rate from 1.55 to 49.25 bits per minute. While LDA is still the most used AI algorithm, CNN has shown promising results, but due to their high technical implementation requirements, many researchers do not justify its implementation as worthwile. To achieve quick and accurate online BCIs for motor rehabilitation applications, future works on SSVEP-, P300-based and hybrid BCIs should focus on optimizing the visual stimulation module and the training stage of ML and DL algorithms.

Skilled independent control of individual motor units via a non-invasive neuromuscular-machine interface

Objective.Brain-machine interfaces (BMIs) have the potential to augment human functions and restore independence in people with disabilities, yet a compromise between non-invasiveness and performance limits their relevance.Approach.Here, we hypothesized that a non-invasive neuromuscular-machine interface providing real-time neurofeedback of individual motor units within a muscle could enable independent motor unit control to an extent suitable for high-performance BMI applications.Main results.Over 6 days of training, eight participants progressively learned to skillfully and independently control three biceps brachii motor units to complete a 2D center-out task. We show that neurofeedback enabled motor unit activity that largely violated recruitment constraints observed during ramp-and-hold isometric contractions thought to limit individual motor unit controllability. Finally, participants demonstrated the suitability of individual motor units for powering general applications through a spelling task.Significance.These results illustrate the flexibility of the sensorimotor system and highlight individual motor units as a promising source of control for BMI applications.

A CNN-based multi-target fast classification method for AR-SSVEP

Because an augmented-reality-based brain-computer interface (AR-BCI) is easily disturbed by external factors, the traditional electroencephalograph (EEG) classification algorithms fail to meet the real-time processing requirements with a large number of stimulus targets or in a real environment. We propose a multi-target fast classification method for augmented-reality-based steady-state visual evoked potential (AR-SSVEP), using a convolutional neural network (CNN). To explore the availability and accuracy of high-efficiency multi-target classification methods in AR-SSVEP with a short stimulation duration, a similar stimulus layout was used for a computer screen (PC) and an optical see-through head-mounted display (OST-HMD) device (HoloLens). The experiment included nine flicker stimuli of different frequencies, and a multi-target fast classification method based on a CNN was constructed to complete nine classification tasks, for which the average accuracy of AR-BCI in our CNN model at 0.5- and 1-s stimulus duration was 67.93% and 80.83%, respectively. These results verified the efficacy of the proposed model for processing multi-target classification in AR-BCI.

Frequency Superposition - A Multi-Frequency Stimulation Method in SSVEP-based BCIs

The steady-state visual evoked potential (SSVEP) is one of the most widely used modalities in brain-computer interfaces (BCIs) due to its many advantages. However, the existence of harmonics and the limited range of responsive frequencies in SSVEP make it challenging to further expand the number of targets without sacrificing other aspects of the interface or putting additional constraints on the system. This paper introduces a novel multi-frequency stimulation method for SSVEP and investigates its potential to effectively and efficiently increase the number of targets presented. The proposed stimulation method, obtained by the superposition of the stimulation signals at different frequencies, is size-efficient, allows single-step target identification, puts no strict constraints on the usable frequency range, can be suited to self-paced BCIs, and does not require specific light sources. In addition to the stimulus frequencies and their harmonics, the evoked SSVEP waveforms include frequencies that are integer linear combinations of the stimulus frequencies. Results of decoding SSVEPs collected from nine subjects using canonical correlation analysis (CCA) with only the frequencies and harmonics as reference, also demonstrate the potential of using such a stimulation paradigm in SSVEP-based BCIs.

Prediction Deviants with Varying Degrees Induce Separable Error-related EEG Features

Error-related potential (ErrP) usually emerges in the brain when human perceives errors and is believed to be a promising signal for optimizing brain-computer interface (BCI) system. However, most of the ErrP studies only focus on how to distinguish the correct and wrong conditions, which is not enough for the BCI application in real scenarios. Therefore, it is necessary to study the ErrPs induced by the prediction deviants with varying degrees, concurrently test the separability of such EEG features. To this end, electroencephalogram (EEG) data of twelve healthy subjects were recorded when they participated in a direction prediction experiment. There are three prediction -deviant conditions in it, i.e., correct prediction, 90°deviant, 180° deviant. Event-related potential and inter-trial coherence were analyzed. Consequently, the error-related negativity (ERN) and N450 component in FCZ were significantly modulated by the degrees of prediction deviants, especially in the low-frequency band (<13Hz). Moreover, single-trial classification was adopted to test the separability of these features; the averaged accuracies between any two conditions were 87.75%, 85.25%, 64.79%. This study demonstrates the prediction deviants with varying degrees can induce separable ErrP features, which provide a deeper understanding of the ErrP signatures for developing BCIs.

Modulation of brain states on fractal and oscillatory power of EEG in brain-computer interfaces

Objective. Electroencephalogram (EEG) is an objective reflection of the brain activities, which provides potential possibilities for brain state estimation based on EEG characteristics. However, how to mine the effective EEG characteristics is still a distressing problem in brain state monitoring.Approach. The phase-scrambled method was used to generate images with different noise levels. Images were encoded into a rapid serial visual presentation paradigm. N-back working memory method was employed to induce and assess fatigue state. The irregular-resampling auto-spectral analysis method was adopted to extract and parameterize (exponent and offset) the characteristics of EEG fractal components, which were analyzed in the four dimensions: fatigue, sustained attention, visual noise and experimental tasks.Main results. The degree of fatigue and visual noise level had positive effects on exponent and offset in the prefrontal lobe, and the ability of sustained attention negatively affected exponent and offset. Compared with visual stimuli task, rest task induced even larger values of exponent and offset and statistically significant in the most cerebral cortex. In addition, the steady-state visual evoked potential amplitudes were negatively and positively affected by the degree of fatigue and noise levels, respectively.Significance. The conclusions of this study provide insights into the relationship between brain states and EEG characteristics. In addition, this study has the potential to provide objective methods for brain states monitoring and EEG modeling.

Optimizing spatial properties of a new checkerboard-like visual stimulus for user-friendly SSVEP-based BCIs

Objective.Low-frequency steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems with high performance are prone to cause visual discomfort and fatigue. High-frequency SSVEP-based BCI systems can alleviate the discomfort, but always obtain lower performance. This study optimized the spatial properties of a proposed checkerboard-like visual stimulus toward high-performance and user-friendly SSVEP-based BCI systems.Approach.On the one hand, two checkerboard-like stimuli with distinct spatial contrasts (the black- and white-background) were designed to balance the tradeoff between BCI performance and user experience and compared with the traditional flickering stimulus. On the other hand, the impacts of the spatial frequency of the new checkerboard-like stimulus on the flicker perception and the intensity of the elicited SSVEP were clarified. The SSVEP-based BCI systems were implemented based on the checkerboard-like stimuli under low-frequency and high-frequency conditions. The user experience for each stimulation pattern was estimated by questionnaires for subjective evaluation.Main results.The comparison results indicate that the black-background checkerboard-like stimulus with an optimized spatial frequency achieved comparable performance and enhanced visual comfort compared with the flickering stimulus. Furthermore, the online nine-target BCI system using the black-background checkerboard-like stimuli achieved averaged information transfer rates of 124.0 ± 2.3 and 109.0 ± 20.4 bits min^-1with low-frequency and high-frequency stimulation respectively.Significance.The new checkerboard-like stimuli with optimized properties show superiority of system performance and user experience in implementing SSVEP-based BCI, which will promote its practical applications in communication and control.

Information Bottleneck as Optimisation Method for SSVEP-Based BCI

In this study, the information bottleneck method is proposed as an optimisation method for steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). The information bottleneck is an information-theoretic optimisation method for solving problems with a trade-off between preserving meaningful information and compression. Its main practical application in machine learning is in representation learning or feature extraction. In this study, we use the information bottleneck to find optimal classification rule for a BCI. This is a novel application for the information bottleneck. This approach is particularly suitable for BCIs since the information bottleneck optimises the amount of information transferred by the BCI. Steady-state visual evoked potential-based BCIs often classify targets using very simple rules like choosing the class corresponding to the largest feature value. We call this classifier the arg max classifier. It is unlikely that this approach is optimal, and in this study, we propose a classification method specifically designed to optimise the performance measure of BCIs. This approach gives an advantage over standard machine learning methods, which aim to optimise different measures. The performance of the proposed algorithm is tested on two publicly available datasets in offline experiments. We use the standard power spectral density analysis (PSDA) and canonical correlation analysis (CCA) feature extraction methods on one dataset and show that the current approach outperforms most of the related studies on this dataset. On the second dataset, we use the task-related component analysis (TRCA) method and demonstrate that the proposed method outperforms the standard argmax classification rule in terms of information transfer rate when using a small number of classes. To our knowledge, this is the first time the information bottleneck is used in the context of SSVEP-based BCIs. The approach is unique in the sense that optimisation is done over the space of classification functions. It potentially improves the performance of BCIs and makes it easier to calibrate the system for different subjects.

Improving the Performance of Individually Calibrated SSVEP-BCI by Task- Discriminant Component Analysis

A brain-computer interface (BCI) provides a direct communication channel between a brain and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has received increasing attention due to its high information transfer rate, which is accomplished by individual calibration for frequency recognition. Task-related component analysis (TRCA) is a recent and state-of-the-art method for individually calibrated SSVEP-BCIs. However, in TRCA, the spatial filter learned from each stimulus may be redundant and temporal information is not fully utilized. To address this issue, this paper proposes a novel method, i.e., task-discriminant component analysis (TDCA), to further improve the performance of individually calibrated SSVEP-BCI. The performance of TDCA was evaluated by two publicly available benchmark datasets, and the results demonstrated that TDCA outperformed ensemble TRCA and other competing methods by a significant margin. An offline and online experiment testing 12 subjects further validated the effectiveness of TDCA. The present study provides a new perspective for designing decoding methods in individually calibrated SSVEP-BCI and presents insight for its implementation in high-speed brain speller applications.

Waveform feature extraction and signal recovery in single-channel TVEP based on Fitzhugh-Nagumo stochastic resonance

Objective. Transient visual evoked potential (TVEP) can reflect the condition of the visual pathway and has been widely used in brain-computer interface. TVEP signals are typically obtained by averaging the time-locked brain responses across dozens or even hundreds of stimulations, in order to remove different kinds of interferences. However, this procedure increases the time needed to detect the brain status in realistic applications. Meanwhile, long repeated stimuli can vary the evoked potentials and discomfort the subjects. Therefore, a novel unsupervised framework was developed in this study to realize the fast extraction of single-channel TVEP signals with a high signal-to-noise ratio.Approach.Using the principle of nonlinear aperiodic FitzHugh-Nagumo (FHN) model, a fast extraction and signal restoration technology of TVEP waveform based on FHN stochastic resonance is proposed to achieve high-quality acquisition of signal features with less average times.Results:A synergistic effect produced by noise, aperiodic signal and nonlinear system can force the energy of noise to be transferred into TVEP and hence amplifying the useful P100 feature while suppressing multi-scale noise.Significance. Compared with the conventional average and average-singular spectrum analysis-independent component analysis(average-SSA-ICA) method, the average-FHN method has a shorter stimulation time which can greatly improve the comfort of patients in clinical TVEP detection and a better performance of TVEP waveform i.e. a higher accuracy of P100 latency. The FHN recovery method is not only highly correlated with the original signal, but also can better highlight the P100 amplitude, which has high clinical application value.

A Deep Neural Network for SSVEP-based Brain-Computer Interfaces

OBJECTIVE

Target identification in brain-computer interface (BCI) spellers refers to the electroencephalogram (EEG) classification for predicting the target character that the subject intends to spell. When the visual stimulus of each character is tagged with a distinct frequency, the EEG records steady-state visually evoked potentials (SSVEP) whose spectrum is dominated by the harmonics of the target frequency. In this setting, we address the target identification and propose a novel deep neural network (DNN) architecture.

METHOD

The proposed DNN processes the multi-channel SSVEP with convolutions across the sub-bands of harmonics, channels, time, and classifies at the fully connected layer. We test with two publicly available large scale (the benchmark and BETA) datasets consisting of in total 105 subjects with 40 characters. Our first stage training learns a global model by exploiting the statistical commonalities among all subjects, and the second stage fine tunes to each subject separately by exploiting the individualities.

RESULTS

Our DNN achieves impressive information transfer rates (ITRs) on both datasets, 265.23 bits/min and 196.59 bits/min, respectively, with only 0.4 seconds of stimulation. The code is available for reproducibility at https://github.com/osmanberke/Deep-SSVEP-BCI.

CONCLUSION

The presented DNN strongly outperforms the state-of-the-art techniques as our accuracy and ITR rates are the highest ever reported performance results on these datasets.

SIGNIFICANCE

Due to its unprecedentedly high speller ITRs and flawless applicability to general SSVEP systems, our technique has great potential in various biomedical engineering settings of BCIs such as communication, rehabilitation and control.

EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.

Enhancing transfer performance across datasets for brain-computer interfaces using a combination of alignment strategies and adaptive batch normalization

Objective. Recently, transfer learning (TL) and deep learning (DL) have been introduced to solve intra- and inter-subject variability problems in brain-computer interfaces (BCIs). However, current TL and DL algorithms are usually validated within a single dataset, assuming that data of the test subjects are acquired under the same condition as that of training (source) subjects. This assumption is generally violated in practice because of different acquisition systems and experimental settings across studies and datasets. Thus, the generalization ability of these algorithms needs further validations in a cross-dataset scenario, which is closer to the actual situation. This study compared the transfer performance of pre-trained deep-learning models with different preprocessing strategies in a cross-dataset scenario.Approach. This study used four publicly available motor imagery datasets, each was successively selected as a source dataset, and the others were used as target datasets. EEGNet and ShallowConvNet with four preprocessing strategies, namely channel normalization, trial normalization, Euclidean alignment, and Riemannian alignment, were trained with the source dataset. The transfer performance of pre-trained models was validated on the target datasets. This study also used adaptive batch normalization (AdaBN) for reducing interval covariate shift across datasets. This study compared the transfer performance of using the four preprocessing strategies and that of a baseline approach based on manifold embedded knowledge transfer (MEKT). This study also explored the possibility and performance of fusing MEKT and EEGNet.Main results. The results show that DL models with alignment strategies had significantly better transfer performance than the other two preprocessing strategies. As an unsupervised domain adaptation method, AdaBN could also significantly improve the transfer performance of DL models. The transfer performance of DL models that combined AdaBN and alignment strategies significantly outperformed MEKT. Moreover, the generalizability of EEGNet models that combined AdaBN and alignment strategies could be further improved via the domain adaptation step in MEKT, achieving the best generalization ability among multiple datasets (BNCI2014001: 0.788, PhysionetMI: 0.679, Weibo2014: 0.753, Cho2017: 0.650).Significance. The combination of alignment strategies and AdaBN could easily improve the generalizability of DL models without fine-tuning. This study may provide new insights into the design of transfer neural networks for BCIs by separating source and target batch normalization layers in the domain adaptation process.

SSVEP-EEG Denoising via Image Filtering Methods

Steady-state visual evoked potential (SSVEP) is widely used in electroencephalogram (EEG) control, medical detection, cognitive neuroscience, and other fields. However, successful application requires improving the detection performance of SSVEP signal frequency characteristics. Most strategies to enhance the signal-to-noise ratio of SSVEP utilize application of a spatial filter. Here, we propose a method for image filtering denoising (IFD) of the SSVEP signal from the perspective of image analysis, as a preprocessing step for signal analysis. Arithmetic mean, geometric mean, Gaussian, and non-local means filtering methods were tested, and the experimental results show that image filtering of SSVEP cannot effectively remove the noise. Thus, we proposed a reverse solution in which the SSVEP noise signal was obtained by image filtering, and then the noise was subtracted from the original signal. Comparison of the recognition accuracy of the SSVEP signal before and after denoising was used to evaluate the denoising performance for stimuli of different duration. After IFD processing, SSVEP exhibited higher recognition accuracy, indicating the effectiveness of this proposed denoising method. Application of this method improves the detection performance of SSVEP signal frequency characteristics, combines image processing and brain signal analysis, and expands the research scope of brain signal analysis for widespread application.

Neurofeedback Training of the Control Network Improves Children's Performance with an SSVEP-based BCI

In the past 20 years, neural engineering has made unprecedented progress in the interpretation of brain information (e.g., brain-computer interfaces) and in neuromodulation (e.g., electromagnetic stimulation and neurofeedback). However, there has been little research aiming to improve the performance of brain-computer interfaces (BCIs) using neuromodulation. The present study presents a novel design for a neurofeedback training (NFT) method to improve the operation of a steady-state visual evoked potential (SSVEP)-based BCI and further explores its underlying mechanisms. The use of NFT to upregulate alpha-band power in the user's parietal lobe is presented in this study as a new neuromodulation method to improve SSVEP-based BCI in this study. After users completed this NFT intervention, the signal-to-noise ratio (SNR), accuracy, and information transfer rate (ITR) of the SSVEP-based BCI were increased by 5.8%, 4.7%, and 15.6%, respectively. However, no improvement was observed in the control group in which the subjects did not participate in NFT. Moreover, a general reinforcement of the information flow from the parietal lobe to the occipital lobe was observed. Evidence from a network analysis and an attention test further indicates that NFT improves attention by developing the control capacity of the parietal lobe and then enhances the above SSVEP indicators. Upregulating the amplitude of parietal alpha oscillations using NFT significantly improves the SSVEP-based BCI performance by modulating the control network. The study validates an effective neuromodulation method and possibly contributes to explaining the function of the parietal lobe in the control network.

Align and pool for EEG headset domain adaptation (ALPHA) to facilitate dry electrode based SSVEP-BCI

OBJECTIVE

The steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) implemented in dry electrodes is a promising paradigm for alternative and augmentative communication in real-world applications. To improve its performance and reduce the calibration effort for dry-electrode systems, we utilize cross-device transfer learning by exploiting auxiliary individual wet-electrode electroencephalogram (EEG).

METHODS

We proposed a novel transfer learning framework named ALign and Pool for EEG Headset domain Adaptation (ALPHA), which aligns the spatial pattern and the covariance for domain adaptation. To evaluate its efficacy, 75 subjects performed an experiment of 2 sessions involving a 12-target SSVEP-BCI task.

RESULTS

ALPHA significantly outperformed a baseline approach (canonical correlation analysis, CCA) and two competing transfer learning approaches (transfer template CCA, ttCCA and least square transformation, LST) in two transferring directions. When transferring from wet to dry EEG headsets, ALPHA significantly outperformed the fully calibrated approach of task-related component analysis (TRCA).

CONCLUSION

ALPHA advances the frontier of recalibration-free cross-device transfer learning for SSVEP-BCIs and boosts the performance of dry electrode based systems.

SIGNIFICANCE

ALPHA has methodological and practical implications and pushes the boundary of dry electrode based SSVEP-BCI toward real-world applications.

Practical real-time MEG-based neural interfacing with optically pumped magnetometers

BACKGROUND

Brain-computer interfaces decode intentions directly from the human brain with the aim to restore lost functionality, control external devices or augment daily experiences. To combine optimal performance with wide applicability, high-quality brain signals should be captured non-invasively. Magnetoencephalography (MEG) is a potent candidate but currently requires costly and confining recording hardware. The recently developed optically pumped magnetometers (OPMs) promise to overcome this limitation, but are currently untested in the context of neural interfacing.

RESULTS

In this work, we show that OPM-MEG allows robust single-trial analysis which we exploited in a real-time 'mind-spelling' application yielding an average accuracy of 97.7%.

CONCLUSIONS

This shows that OPM-MEG can be used to exploit neuro-magnetic brain responses in a practical and flexible manner, and opens up new avenues for a wide range of new neural interface applications in the future.

Enhancing Detection of SSMVEP Induced by Action Observation Stimuli Based on Task-Related Component Analysis

Action observation (AO)-based brain-computer interface (BCI) is an important technology in stroke rehabilitation training. It has the advantage of simultaneously inducing steady-state motion visual evoked potential (SSMVEP) and activating sensorimotor rhythm. Moreover, SSMVEP could be utilized to perform classification. However, SSMVEP is composed of complex modulation frequencies. Traditional canonical correlation analysis (CCA) suffers from poor recognition performance in identifying those modulation frequencies at short stimulus duration. To address this issue, task-related component analysis (TRCA) was utilized to deal with SSMVEP for the first time. An interesting phenomenon was found: different modulated frequencies in SSMVEP distributed in different task-related components. On this basis, a multi-component TRCA method was proposed. All the significant task-related components were utilized to construct multiple spatial filters to enhance the detection of SSMVEP. Further, a combination of TRCA and CCA was proposed to utilize both advantages. Results showed that the accuracies using the proposed methods were significant higher than that using CCA at all window lengths and significantly higher than that using ensemble-TRCA at short window lengths (≤2 s). Therefore, the proposed methods further validate the induced modulation frequencies and will speed up the application of the AO-based BCI in rehabilitation.

Review of brain encoding and decoding mechanisms for EEG-based brain-computer interface

A brain-computer interface (BCI) can connect humans and machines directly and has achieved successful applications in the past few decades. Many new BCI paradigms and algorithms have been developed in recent years. Therefore, it is necessary to review new progress in BCIs. This paper summarizes progress for EEG-based BCIs from the perspective of encoding paradigms and decoding algorithms, which are two key elements of BCI systems. Encoding paradigms are grouped by their underlying neural meachanisms, namely sensory- and motor-related, vision-related, cognition-related and hybrid paradigms. Decoding algorithms are reviewed in four categories, namely decomposition algorithms, Riemannian geometry, deep learning and transfer learning. This review will provide a comprehensive overview of both modern primary paradigms and algorithms, making it helpful for those who are developing BCI systems.

Spatio-temporal equalization multi-window algorithm for asynchronous SSVEP-based BCI

Objective.Asynchronous brain-computer interfaces (BCIs) show significant advantages in many practical application scenarios. Compared with the rapid development of synchronous BCIs technology, the progress of asynchronous BCI research, in terms of containing multiple targets and training-free detection, is still relatively slow. In order to improve the practicability of the BCI, a spatio-temporal equalization multi-window algorithm (STE-MW) was proposed for asynchronous detection of steady-state visual evoked potential (SSVEP) without the need for acquiring calibration data.Approach.The algorithm used SIE strategy to intercept EEG signals of different lengths through multiple stacked time windows and statistical decisions-making based on Bayesian risk decision-making. Different from the traditional asynchronous algorithms based on the 'non-control state detection' methods, this algorithm was based on the 'statistical inspection-rejection decision' mode and did not require a separate classification of non-control states, so it can be effectively applied to detections for large-scale candidates.Main results.Online experimental results involving 14 healthy subjects showed that, in the continuously input experiments of 40 targets, the algorithm achieved the average recognition accuracy of97.2±2.6%and the average information transfer rate (ITR) of106.3±32.0 bitsmin-1. At the same time, the average false alarm rate in the 240 s resting state test was0.607±0.602 min-1. In the free spelling experiments involving patients with severe amyotrophic lateral sclerosis, the subjects achieved an accuracy of 92.7% and an average ITR of 43.65 bits min^-1in two free spelling experiments.Significance.This algorithm can achieve high-performance, high-precision, and asynchronous detection of SSVEP signals with low algorithm complexity and false alarm rate under multi-targets and training-free conditions, which is helpful for the development of asynchronous BCI systems.