High-Frequency Discrete-Interval Binary Sequence in Asynchronous C-VEP-Based BCI for Visual Fatigue Reduction

In code-modulated visual evoked potential (c-VEP) based BCI systems, flickering visual stimuli may result in visual fatigue. Thus, we introduced a discrete-interval binary sequence (DIBS) as visual stimulus modulation, with its power spectrum optimized to emphasize high-frequency components (40 Hz-60 Hz). 8 and 17 subjects participated, respectively, in offline and online experiments on a 4-target asynchronous c-VEP-based BCI system designed to realize a high positive predictive value (PPV), a low false positive rate (FPR) during idle states, and a high true positive rate (TPR) in control states, while minimizing visual fatigue level. Two visual stimuli modulations were introduced and compared: a maximum length sequence (m-sequence) and the high-frequency discrete-interval binary sequence (DIBS). The decoding algorithm was compared among the canonical correlation analysis (CCA), the task-related component analysis (TRCA), and two approaches of sub-band component weight calculation (the traditional method and the proportional method) for FBCCA and FBTRCA. In the online experiments, the average PPV, FPR and TPR achieved, respectively [Formula: see text], [Formula: see text], [Formula: see text] with m-sequence, while [Formula: see text], [Formula: see text] and [Formula: see text] with DIBS. Estimated by objective eye-related metrics and a subjective questionnaire, the visual fatigue in DIBS cases is significantly smaller than that in m-sequence cases. In this study, the feasibility of a novel modulation approach for visual fatigue reduction was proved in an asynchronous c-VEP system, while maintaining comparable performance to existing methods, which provides further insights towards enhancing this field's long-term viability and user-friendliness.

Hybrid optimization assisted channel selection of EEG for deep learning model-based classification of motor imagery task

OBJECTIVES

To design and develop an approach named HC + SMA-SSA scheme for classifying motor imagery task.

METHODS

The offered model employs a new method for classifying motor imagery task. Initially, down sampling is deployed to pre-process the incoming signal. Subsequently, "Modified Stockwell Transform (ST) and common spatial pattern (CSP) based features are extracted". Then, optimal channel selection is made by a novel hybrid optimization model named as Spider Monkey Assisted SSA (SMA-SSA). Here, "Long Short Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BI-GRU)" models are used for final classification, whose outcomes are averaged at the end. At last, the improvement of SMA-SSA based model is proven over different metrics.

RESULTS

A superior sensitivity of 0.939 is noted for HC + SMA-SSA that was higher over HC with no optimization and proposed with traditional ST.

CONCLUSIONS

The proposed method achieved effective classification performance in terms of performance measures.

A Time-Local Weighted Transformation Recognition Framework for Steady State Visual Evoked Potentials Based Brain-Computer Interfaces

Canonical correlation analysis (CCA), Multivariate synchronization index (MSI), and their extended methods have been widely used for target recognition in Brain-computer interfaces (BCIs) based on Steady State Visual Evoked Potentials (SSVEP), and covariance calculation is an important process for these algorithms. Some studies have proved that embedding time-local information into the covariance can optimize the recognition effect of the above algorithms. However, the optimization effect can only be observed from the recognition results and the improvement principle of time-local information cannot be explained. Therefore, we propose a time-local weighted transformation (TT) recognition framework that directly embeds the time-local information into the electroencephalography signal through weighted transformation. The influence mechanism of time-local information on the SSVEP signal can then be observed in the frequency domain. Low-frequency noise is suppressed on the premise of sacrificing part of the SSVEP fundamental frequency energy, the harmonic energy of SSVEP is enhanced at the cost of introducing a small amount of high-frequency noise. The experimental results show that the TT recognition framework can significantly improve the recognition ability of the algorithms and the separability of extracted features. Its enhancement effect is significantly better than the traditional time-local covariance extraction method, which has enormous application potential.

P300 intention recognition based on phase lag index (PLI)-rich-club brain functional network

Brain-computer interface (BCI) technology based on P300 signals has a broad application prospect in the assessment and diagnosis of clinical diseases and game control. The paper of selecting key electrodes to realize a wearable intention recognition system has become a hotspot for scholars at home and abroad. In this paper, based on the rich-club phenomenon that exists in the process of intention generation, a phase lag index (PLI)-rich-club-based intention recognition method for P300 is proposed. The rich-club structure is a network consisting of electrodes that are highly connected with other electrodes in the process of P300 generation. To construct the rich-club network, this paper uses PLI to construct the brain functional network, calculates rich-club coefficients of the network in the range of k degrees, initially identifies rich-club nodes based on the feature of node degree, and then performs a descending order of betweenness centrality and identifies the nodes with larger betweenness centrality as the specific rich-club nodes, extracts the non-linear features and frequency domain features of Rich-club nodes, and finally uses support vector machine for classification. The experimental results show that the range of rich-club coefficients is smaller with intent compared to that without intent. Validation was performed on the BCI Competition III dataset by reducing the number of channels to 17 and 16 for subject A and subject B, with recognition quasi-departure rates of 96.93% and 94.93%, respectively, and on the BCI Competition II dataset by reducing the number of channels to 17 for subjects, with a recognition accuracy of 95.50%.

Effect of motion state variability on error-related potentials during continuous feedback paradigms and their consequences for classification

BACKGROUND

An erroneous motion would elicit the error-related potential (ErrP) when humans monitor the behavior of the external devices. This EEG modality has been largely applied to brain-computer interface in an active or passive manner with discrete visual feedback. However, the effect of variable motion state on ErrP morphology and classification performance raises concerns when the interaction is conducted with continuous visual feedback.

NEW METHOD

In the present study, we designed a cursor control experiment. Participants monitored a continuously moving cursor to reach the target on one side of the screen. Motion state varied multiple times with two factors: (1) motion direction and (2) motion speed. The effects of these two factors on the morphological characteristics and classification performance of ErrP were analyzed. Furthermore, an offline simulation was performed to evaluate the effectiveness of the proposed extended ErrP-decoder in resolving the interference by motion direction changes.

RESULTS

The statistical analyses revealed that motion direction and motion speed significantly influenced the amplitude of feedback-ERN and frontal-Pe components, while only motion direction significantly affected the classification performance.

COMPARISON WITH EXISTING METHODS

Significant deviation was found in ErrP detection utilizing classical correct-versus-erroneous event training. However, this bias can be alleviated by 16% by the extended ErrP-decoder.

CONCLUSION

The morphology and classification performance of ErrP signal can be affected by motion state variability during continuous feedback paradigms. The results enhance the comprehension of ErrP morphological components and shed light on the detection of BCI's error behavior in practical continuous control.

Attentional State Classification Using Amplitude and Phase Feature Extraction Method Based on Filter Bank and Riemannian Manifold

As a significant aspect of cognition, attention has been extensively studied and numerous measurements have been developed based on brain signal processing. Although existing attentional state classification methods have achieved good accuracy by extracting a variety of handcrafted features, spatial features have not been fully explored. This paper proposes an attentional state classification method based on Riemannian manifold to utilize spatial information. Based on the concept of Riemannian manifold of symmetric positive definite (SPD) matrix, the proposed method exploits the structure of covariance matrix to extract spatial features instead of using spatial filters. Specifically, Riemannian distances from intra-class Riemannian means are extracted as features for their robustness. To fully extend the potential of electroencephalograph (EEG) signal, both amplitude and phase information is utilized. In addition, to solve the variance of frequency bands, a filter bank is employed to process the signal of different frequency bands separately. Finally, features are fed into a support vector machine with a polynomial kernel to obtain classification results. The proposed attentional state classification using amplitude and phase feature extraction method based on filter bank and Riemannian manifold (AP-FBRM) method is evaluated on two open datasets including EEG data of 29 and 26 subjects. According to the experimental results, the optimal set of filter bank and the optimal technique to extract features containing both amplitude and phase information are determined. The proposed method respectively achieves accuracies of 88.06% and 80.00% and outperforms 8 baseline methods, which manifests that the proposed method creates an efficient way to recognize attentional state.

Review of Performance Improvement of a Noninvasive Brain-computer Interface in Communication and Motor Control for Clinical Applications

Brain-computer interfaces (BCI) enable direct communication between the brain and a computer or other external devices. They can extend a person's degree of freedom by either strengthening or substituting the human peripheral working capacity. Moreover, their potential clinical applications in medical fields include rehabilitation, affective computing, communication, and control. Over the last decade, noninvasive BCI systems such as electroencephalogram (EEG) have progressed from simple statistical models to deep learning models, with performance improvement over time and enhanced computational power. However, numerous challenges pertaining to the clinical use of BCI systems remain, e.g., the lack of sufficient data to learn more possible features for robust and reliable classification. However, compared with fields such as computer vision and speech recognition, the training samples in the medical BCI field are limited as they target patients who face difficulty generating EEG data compared with healthy control. Because deep learning models incorporate several parameters, they require considerably more data than other conventional methods. Thus, deep learning models have not been thoroughly leveraged in medical BCI. This study summarizes the state-of-the-art progress of the BCI system over the last decade, highlighting critical challenges and solutions.

The Effects of Directional and Non-Directional Stimuli during a Visuomotor Task and Their Correlation with Reaction Time: An ERP Study

Different visual stimuli can capture and shift attention into different directions. Few studies have explored differences in brain response due to directional (DS) and non-directional visual stimuli (nDS). To explore the latter, event-related potentials (ERP) and contingent negative variation (CNV) during a visuomotor task were evaluated in 19 adults. To examine the relation between task performance and ERPs, the participants were divided into faster (F) and slower (S) groups based on their reaction times (RTs). Moreover, to reveal ERP modulation within the same subject, each recording from the single participants was subdivided into F and S trials based on the specific RT. ERP latencies were analysed between conditions ((DS, nDS); (F, S subjects); (F, S trials)). Correlation was analysed between CNV and RTs. Our results reveal that the ERPs' late components are modulated differently by DS and nDS conditions in terms of amplitude and location. Differences in ERP amplitude, location and latency, were also found according to subjects' performance, i.e., between F and S subjects and trials. In addition, results show that the CNV slope is modulated by the directionality of the stimulus and contributes to motor performance. A better understanding of brain dynamics through ERPs could be useful to explain brain states in healthy subjects and to support diagnoses and personalized rehabilitation in patients with neurological diseases.

Transfer learning of an ensemble of DNNs for SSVEP BCI spellers without user-specific training

Objective.Steady-state visually evoked potentials (SSVEPs), measured with electroencephalogram (EEG), yield decent information transfer rates (ITRs) in brain-computer interface (BCI) spellers. However, the current high performing SSVEP BCI spellers in the literature require an initial lengthy and tiring user-specific training for each new user for system adaptation, including data collection with EEG experiments, algorithm training and calibration (all are before the actual use of the system). This impedes the widespread use of BCIs. To ensure practicality, we propose a novel target identification method based on an ensemble of deep neural networks (DNNs), which does not require any sort of user-specific training.Approach.We exploit already-existing literature datasets from participants of previously conducted EEG experiments to train a global target identifier DNN first, which is then fine-tuned to each participant. We transfer this ensemble of fine-tuned DNNs to the new user instance, determine thekmost representative DNNs according to the participants' statistical similarities to the new user, and predict the target character through a weighted combination of the ensemble predictions.Main results.The proposed method significantly outperforms all the state-of-the-art alternatives for all stimulation durations in [0.2-1.0] s on two large-scale benchmark and BETA datasets, and achieves impressive 155.51 bits/min and 114.64 bits/min ITRs. Code is available for reproducibility:https://github.com/osmanberke/Ensemble-of-DNNs.Significance.Our Ensemble-DNN method has the potential to promote the practical widespread deployment of BCI spellers in daily lives as we provide the highest performance while enabling the immediate system use without any user-specific training.

TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification

Electroencephalogram (EEG) based seizure subtype classification is very important in clinical diagnostics. However, manual seizure subtype classification is expensive and time-consuming, whereas automatic classification usually needs a large number of labeled samples for model training. This paper proposes an EEGNet-based slim deep neural network, which relieves the labeled data requirement in EEG-based seizure subtype classification. A temporal information enhancement module with sinusoidal encoding is used to augment the first convolution layer of EEGNet. A training strategy for automatic hyper-parameter selection is also proposed. Experiments on the public TUSZ dataset and our own CHSZ dataset with infants and children demonstrated that our proposed TIE-EEGNet outperformed several traditional and deep learning models in cross-subject seizure subtype classification. Additionally, it also achieved the best performance in a challenging transfer learning scenario. Both our code and the CHSZ dataset are publicized.

Riemannian distance based channel selection and feature extraction combining discriminative time-frequency bands and Riemannian tangent space for MI-BCIs

OBJECTIVE

Motor imagery-based brain computer interfaces (MI-BCIs) have been widely researched because they do not demand external stimuli and have a high degree of maneuverability. In most scenarios, superabundant selected channels, fixed time windows, and frequency bands would certainly affect the performance of MI-BCIs due to the neurophysiological diversities between different individuals. In this study, we tended to effectively use the Riemannian geometry of spatial covariance matrix to extract more robust features and thus enhance the decoding efficiency.

APPROACH

First, we propose a Riemannian distance-based EEG channel selection method (RDCS), which preliminarily reduces the information redundancy in the first stage. Second, we extract discriminative Riemannian Tangent Space features of EEG signals of selected channels from the most discriminant time-frequency bands (DTFRTS) to further enhance decoding accuracy for MI-BCIs. Finally, we trained a support vector machine (SVM) model with a linear kernel to classify our extracted discriminative Riemannian features and evaluated our proposed method using publicly available BCI Competition Ⅳ dataset Ⅰ (DS1) and Competition Ⅲ dataset Ⅲa (DS2).

MAIN RESULTS

The experimental results showed that the average classification accuracy with the selected 10-channel EEG signals of our method is 88.1% and 91.6% in DS1 and DS2 respectively. The average improvements are 24.3% & 27.1% on DS1 and 4.4% & 14.2% on DS2 for 10 & 20 selected channels, respectively.

SIGNIFICANCE

These results showed that our proposed method is a promising candidate for performance improvement of MI-BCIs.

Event-Related Potential-Based Brain-Computer Interface Using the Thai Vowels' and Numerals' Auditory Stimulus Pattern

Herein, we developed an auditory stimulus pattern for an event-related potential (ERP)-based brain-computer interface (BCI) system to improve control and communication in quadriplegia with visual impairment. Auditory stimulus paradigms for multicommand electroencephalogram (EEG)-based BCIs and audio stimulus patterns were examined. With the proposed auditory stimulation, using the selected Thai vowel, similar to the English vowel, and Thai numeral sounds, as simple target recognition, we explored the ERPs' response and classification efficiency from the suggested EEG channels. We also investigated the use of single and multi-loudspeakers for auditory stimuli. Four commands were created using the proposed paradigm. The experimental paradigm was designed to observe ERP responses and verify the proposed auditory stimulus pattern. The conventional classification method produced four commands using the proposed auditory stimulus pattern. The results established that the proposed auditory stimulation with 20 to 30 trials of stream stimuli could produce a prominent ERP response from Pz channels. The vowel stimuli could achieve higher accuracy than the proposed numeral stimuli for two auditory stimuli intervals (100 and 250 ms). Additionally, multi-loudspeaker patterns through vowel and numeral sound stimulation provided an accuracy greater than 85% of the average accuracy. Thus, the proposed auditory stimulation patterns can be implemented as a real-time BCI system to aid in the daily activities of quadratic patients with visual and tactile impairments. In future, practical use of the auditory ERP-based BCI system will be demonstrated and verified in an actual scenario.

EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review

Recently, a novel electroencephalogram-based brain-computer interface (EVE-BCI) using the vibrotactile stimulus shows great potential for an alternative to other typical motor imagery and visual-based ones. (i) Objective: in this review, crucial aspects of EVE-BCI are extracted from the literature to summarize its key factors, investigate the synthetic evidence of feasibility, and generate recommendations for further studies. (ii) Method: five major databases were searched for relevant publications. Multiple key concepts of EVE-BCI, including data collection, stimulation paradigm, vibrotactile control, EEG signal processing, and reported performance, were derived from each eligible article. We then analyzed these concepts to reach our objective. (iii) Results: (a) seventy-nine studies are eligible for inclusion; (b) EEG data are mostly collected among healthy people with an embodiment of EEG cap in EVE-BCI development; (c) P300 and Steady-State Somatosensory Evoked Potential are the two most popular paradigms; (d) only locations of vibration are heavily explored by previous researchers, while other vibrating factors draw little interest. (e) temporal features of EEG signal are usually extracted and used as the input to linear predictive models for EVE-BCI setup; (f) subject-dependent and offline evaluations remain popular assessments of EVE-BCI performance; (g) accuracies of EVE-BCI are significantly higher than chance levels among different populations. (iv) Significance: we summarize trends and gaps in the current EVE-BCI by identifying influential factors. A comprehensive overview of EVE-BCI can be quickly gained by reading this review. We also provide recommendations for the EVE-BCI design and formulate a checklist for a clear presentation of the research work. They are useful references for researchers to develop a more sophisticated and practical EVE-BCI in future studies.

Towards Assessment of Sound Localization in Disorders of Consciousness Using a Hybrid Audiovisual Brain-Computer Interface

Behavioral assessment of sound localization in the Coma Recovery Scale-Revised (CRS-R) poses a significant challenge due to motor disability in patients with disorders of consciousness (DOC). Brain-computer interfaces (BCIs), which can directly detect brain activities related to external stimuli, may thus provide an approach to assess DOC patients without the need for any physical behavior. In this study, a novel audiovisual BCI system was developed to simulate sound localization evaluation in CRS-R. Specifically, there were two alternatively flashed buttons on the left and right sides of the graphical user interface, one of which was randomly chosen as the target. The auditory stimuli of bell sounds were simultaneously presented by the ipsilateral loudspeaker during the flashing of the target button, which prompted patients to selectively attend to the target button. The recorded electroencephalography data were analyzed in real time to detect event-related potentials evoked by the target and further to determine whether the target was attended to or not. A significant BCI accuracy for a patient implied that he/she had sound localization. Among eighteen patients, eleven and four showed sound localization in the BCI and CRS-R, respectively. Furthermore, all patients showing sound localization in the CRS-R were among those detected by our BCI. The other seven patients who had no sound localization behavior in CRS-R were identified by the BCI assessment, and three of them showed improvements in the second CRS-R assessment after the BCI experiment. Thus, the proposed BCI system is promising for assisting the assessment of sound localization and improving the clinical diagnosis of DOC patients.

Evaluation of Spatial Directional Guidance Using Cheek Haptic Stimulation in a Virtual Environment

Spatial cues play an important role in navigating people in both physical and virtual spaces. In spatial navigation, visual information with additional cues, such as haptic cues, enables effective guidance. Most haptic devices are applied to various body parts to make mechanical stimuli, while few devices stimulate a head despite the excellent sensitivity. This article presents Virtual Whiskers, a spatial directional guidance technique by cheek haptic stimulation using tiny robot arms attached to a Head-Mounted Display (HMD). The tip of the robotic arm has photo reflective sensors to detect the distance between the tip and the cheek surface. Using the robot arms, we stimulate a point on the cheek obtained by calculating an intersection between the cheek surface and the target direction. In the directional guidance experiment, we investigated how accurately participants identify the target direction provided by our guidance method. We evaluated an error between the actual target direction and the participant's pointed direction. The experimental result shows that our method achieves the average absolute directional error of 2.54° in the azimuthal plane and 6.54° in the elevation plane. We also conducted a spatial guidance experiment to evaluate task performance in a target search task. We compared the condition of visual information, visual and audio information, and visual information and cheek haptics for task completion time, System Usability Scale (SUS) score, NASA-TLX score. The averages of task completion time were M = 6.39 s, SD = 3.34 s, and M = 5.62 s, SD = 3.12 s, and M = 4.35 s, SD = 2.26 s, in visual-only condition, visual+audio condition, and visual+haptic condition, respectively. In terms of the SUS score, visual condition, visual+audio condition, and visual+haptic condition achieved M = 55.83, SD = 20.40, and M = 47.78, SD = 20.09, and M = 80.42, SD = 10.99, respectively. As for NASA-TLX score, visual condition, visual+audio condition, and visual+haptic condition resulted in M = 75.81, SD = 16.89, and M = 67.57, SD = 14.96, and M = 38.83, SD = 18.52, respectively. Statistical tests revealed significant differences in task completion time, SUS score, and NASA-TLX score between the visual and the visual+haptic condition and the visual+audio and the visual+haptic condition.

Event-Related Potentials Analysis of the Effects of Discontinuous Short-Term Fine Motor Imagery on Motor Execution

In this study, event-related potentials and neurobehavioral measurements were used to investigate the effects of discontinuous short-term fine motor imagery (MI), a paradigm of finger sequential MI training interspersed with no-MI that occurs within 1 hr, on fine finger motor execution. The event-related potentials revealed that there were significant differences in the P300 between the fine MI training and the no-MI training. There were also significant changes in the P200 between fine motor execution of familiar tasks after MI training and fine motor execution of unfamiliar tasks without MI training. Neurobehavioral data revealed that the fine MI enhanced fine motor execution. These findings may suggest that discontinuous short-term fine MI could be useful in improving fine motor skills.

Reconfiguration of Cortical Brain Network from Searching to Spotting for Dynamic Visual Targets

BACKGROUND

Detecting dynamic targets from complex visual scenes is an important problem in real world. However, the cognitive mechanism accounting for dynamic visual target detection remains unclear.

NEW METHOD

Herein, we aim to explore the cognitive process of dynamic visual target detection from searching to spotting and provide more concrete evidence for cognitive studies related to target detection. Cortical source responses with high spatiotemporal resolution were reconstructed from scalp EEG signals. Then, time-varying cortical networks were built using adaptive directed transfer function to explore the cognitive processes while detecting the dynamic visual target.

RESULTS

The experimental results demonstrated that the dynamic visual target detection enhanced the activation in both the visual and attention networks. Specially, the information flow from the middle occipital gyrus (MOG) mainly contributed to the position function, whereas the activation of the prefrontal cortex (PFC) reflected spatial attention maintenance.

CONCLUSION

The left "frontal-central-parietal" network played as a leading information source in dynamic target detection tasks. These findings provide new insights into cognitive processes of dynamic visual target detection.

DATA AVAILABILITY STATEMENT

The datasets in this study are available on request to the corresponding author.

Feature Extraction Method Based on Filter Banks and Riemannian Tangent Space in Motor-Imagery BCI

Optimal feature extraction for multi-category motor imagery brain-computer interfaces (MI-BCIs) is a research hotspot. The common spatial pattern (CSP) algorithm is one of the most widely used methods in MI-BCIs. However, its performance is adversely affected by variance in the operational frequency band and noise interference. Furthermore, the performance of CSP is not satisfactory when addressing multi-category classification problems. In this work, we propose a fusion method combining Filter Banks and Riemannian Tangent Space (FBRTS) in multiple time windows. FBRTS uses multiple filter banks to overcome the problem of variance in the operational frequency band. It also applies the Riemannian method to the covariance matrix extracted by the spatial filter to obtain more robust features in order to overcome the problem of noise interference. In addition, we use a One-Versus-Rest support vector machine (OVR-SVM) model to classify multi-category features. We evaluate our FBRTS method using BCI competition IV dataset 2a and 2b. The experimental results show that the average classification accuracy of our FBRTS method is 77.7% and 86.9% in datasets 2a and 2b respectively. By analyzing the influence of the different numbers of filter banks and time windows on the performance of our FBRTS method, we can identify the optimal number of filter banks and time windows. Additionally, our FBRTS method can obtain more distinctive features than the filter banks common spatial pattern (FBCSP) method in two-dimensional embedding space. These results show that our proposed method can improve the performance of MI-BCIs.

Multi-view optimization of time-frequency common spatial patterns for brain-computer interfaces

BACKGROUND

Common spatial pattern (CSP) is a prevalent method applied to feature extraction in motor imagery (MI)-based brain-computer interfaces (BCIs) recorded by electroencephalogram (EEG). The selection of time windows and frequency bands prominently affects the performance of CSP algorithms. Concerning the joint optimization of these two parameters, several studies have utilized a unified framework based on different feature selection strategies and achieved considerable improvement. However, during the feature selection process, useful information could be discarded inevitably and the underlying internal structure of features could be neglected.

NEW METHOD

In this paper, we proposed a novel framework termed time window filter bank common spatial pattern with multi-view optimization (TWFBCSP-MVO) to further boost the decoding of MI tasks. Concretely, after extracting CSP features from different time-frequency decompositions of EEG signals, a preliminary screening strategy based on variance ratio was devised to filter out the unrelated spatial patterns. We then introduced a multi-view learning strategy for the simultaneous optimization of time windows and frequency bands. A support vector machine classifier was trained to determine the output of the brain.

RESULTS

An experimental study was conducted on two public datasets to verify the effectiveness of TWFBCSP-MVO. Results showed that the proposed TWFBCSP-MVO could help improve the performance of MI classification.

COMPARISON WITH EXISTING METHODS

In comparison to other competing methods, the proposed method performed significantly better (p<0.01).

CONCLUSIONS

The proposed method is a promising contestant to improve the performance of practical MI-based BCIs.

A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery based Brain-Computer Interface

The motor imagery (MI) based brain-computer interfaces (BCIs) have been proposed as a potential physical rehabilitation technology. However, the low classification accuracy achievable with MI tasks is still a challenge when building effective BCI systems. We propose a novel MI classification model based on measurement of functional connectivity between brain regions and graph theory. Specifically, motifs describing local network structures in the brain are extracted from functional connectivity graphs. A graph embedding model called Ego-CNNs is then used to build a classifier, which can convert the graph from a structural representation to a fixed-dimensional vector for detecting critical structure in the graph. We validate our proposed method on four datasets, and the results show that our proposed method produces high classification accuracies in two-class classification tasks (92.8% for dataset 1, 93.4% for dataset 2, 96.5% for dataset 3, and 80.2% for dataset 4) and multiclass classification tasks (90.33% for dataset 1). Our proposed method achieves a mean Kappa value of 0.88 across nine participants, which is superior to other methods we compared it to. These results indicate that there is a local structural difference in functional connectivity graphs extracted under different motor imagery tasks. Our proposed method has great potential for motor imagery classification in future studies.

Different Patterns of Attention Modulation in Early N140 and Late P300 sERPs Following Ipsilateral vs. Contralateral Stimulation at the Fingers and Cheeks

Intra-hemispheric interference has been often observed when body parts with neighboring representations within the same hemisphere are stimulated. However, patterns of interference in early and late somatosensory processing stages due to the stimulation of different body parts have not been explored. Here, we explore functional similarities and differences between attention modulation of the somatosensory N140 and P300 elicited at the fingers vs. cheeks. In an active oddball paradigm, 22 participants received vibrotactile intensity deviant stimulation either ipsilateral (within-hemisphere) or contralateral (between-hemisphere) at the fingers or cheeks. The ipsilateral deviant always covered a larger area of skin than the contralateral deviant. Overall, both N140 and P300 amplitudes were higher following stimulation at the cheek and N140 topographies differed between fingers and cheek stimulation. For the N140, results showed higher deviant ERP amplitudes following contralateral than ipsilateral stimulation, regardless of the stimulated body part. N140 peak latency differed between stimulated body parts with shorter latencies for the stimulation at the fingers. Regarding P300 amplitudes, contralateral deviant stimulation at the fingers replicated the N140 pattern, showing higher responses and shorter latencies than ipsilateral stimulation at the fingers. For the stimulation at the cheeks, ipsilateral deviants elicited higher P300 amplitudes and longer latencies than contralateral ones. These findings indicate that at the fingers ipsilateral deviant stimulation leads to intra-hemispheric interference, with significantly smaller ERP amplitudes than in contralateral stimulation, both at early and late processing stages. By contrast, at the cheeks, intra-hemispheric interference is selective for early processing stages. Therefore, the mechanisms of intra-hemispheric processing differ from inter-hemispheric ones and the pattern of intra-hemispheric interference in early and late processing stages is body-part specific.

Feature selection method based on Menger curvature and LDA theory for a P300 brain-computer interface

Brain-computer interface (BCI) systems decode electroencephalogram signals to establish a channel for direct interaction between the human brain and the external world without the need for muscle or nerve control. The P300 speller, one of the most widely used BCI applications, presents a selection of characters to the user and performs character recognition by identifying P300 event-related potentials from the EEG. Such P300-based BCI systems can reach good levels of accuracy but are difficult to use in day-to-day life due to redundancy and noisy signal. A room for improvement should be considered. We propose a novel hybrid feature selection method for the P300-based BCI system to address the problem of feature redundancy, which combines the Menger curvature and linear discriminant analysis. First, selected strategies are applied separately to a given dataset to estimate the gain for application to each feature. Then, each generated value set is ranked in descending order and judged by a predefined criterion to be suitable in classification models. The intersection of the two approaches is then evaluated to identify an optimal feature subset. The proposed method is evaluated using three public datasets, i.e., BCI Competition III dataset II, BNCI Horizon dataset, and EPFL dataset. Experimental results indicate that compared with other typical feature selection and classification methods, our proposed method has better or comparable performance. Additionally, our proposed method can achieve the best classification accuracy after all epochs in three datasets. In summary, our proposed method provides a new way to enhance the performance of the P300-based BCI speller.

Phase Preservation Neural Network for Electroencephalography Classification in Rapid Serial Visual Presentation Task

Neuroscience studies have demonstrated the phase-locked characteristics of some early event-related potential (ERP) components evoked by stimuli. In this study, we propose a phase preservation neural network (PPNN) to learn phase information to improve the Electroencephalography (EEG) classification in a rapid serial visual presentation (RSVP) task. The PPNN consists of three major modules that can produce spatial and temporal representations with the high discriminative ability of the EEG features for classification. We first adopt a stack of dilated temporal convolution layers to extract temporal dynamics while avoiding the loss of phase information. Considering the intrinsic channel dependence of the EEG data, a spatial convolution layer is then applied to obtain the spatial-temporal representation of the input EEG signal. Finally, a fully connected layer is adopted to extract higher-level features for the final classification. The experiments are conducted on two public and one collected EEG datasets from the RSVP task, in which we evaluated the performance and explored the capability of phase preservation of our PPNN model and visualized the extracted features. The experimental results indicate the superiority of the proposed PPNN when compared with previous methods, suggesting the PPNN is a robust model for EEG classification in RSVP task.

Quantifying the Generation Process of Multi-Level Tactile Sensations via ERP Component Investigation

Humans obtain characteristic information such as texture and weight of external objects, relying on the brain's integration and classification of tactile information; however, the decoding mechanism of multi-level tactile information is relatively elusive from the temporal sequence. In this paper, nonvariant frequency, along with the variant pulse width of electrotactile stimulus, was performed to generate multi-level pressure sensation. Event-related potentials (ERPs) were measured to investigate the mechanism of whole temporal tactile processing. Five ERP components, containing P100-N140-P200-N200-P300, were observed. By establishing the relationship between stimulation parameters and ERP component amplitudes, we found the following: (1) P200 is the most significant component for distinguishing multi-level tactile sensations; (2) P300 is correlated well with the subjective judgment of tactile sensation. The temporal sequence of brain topographies was implemented to clarify the spatiotemporal characteristics of the tactile process, which conformed to the serial processing model in neurophysiology and cortical network response area described by fMRI. Our results can help further clarify the mechanism of tactile sequential processing, which can be applied to improve the tactile BCI performance, sensory enhancement, and clinical diagnosis for doctors to evaluate the tactile process disorders by examining the temporal ERP components.

Evaluation of color modulation in visual P300-speller using new stimulus patterns

Objective The stimulus color of P300-BCI systems has been successfully modified. However, the effects of different color combinations have not been widely investigated. In this study, we designed new stimulus patterns to evaluate the influence of color modulation on the BCI performance and waveforms of the evoked related potential (ERP).Methods Comparison was performed for three new stimulus patterns consisting of red face and colored block-shape, namely, red face with a white rectangle (RFW), red face with a blue rectangle (RFB), and red face with a red rectangle (RFR). Bayesian linear discriminant analysis (BLDA) was used to construct the individual classifier model. Repeated-measures ANOVA and Bonferroni correction were applied for statistical analysis. Results The RFW pattern obtained the highest average online accuracy with 96.94%, and those of RFR and RFB patterns were 93.61% and of 92.22% respectively. Significant differences in online accuracy and information transfer rate (ITR) were found between RFW and RFR patterns (p < 0.05). Conclusion Compared with RFR and RFB patterns, RFW yielded the best performance in P300-BCI. These new stimulus patterns with different color combinations have considerable importance to BCI applications and user-friendliness.

Detection of amyotrophic lateral sclerosis disease from event-related potentials using variational mode decomposition method

This study, it was aimed to contribute to the literature on Amyotrophic lateral sclerosis (ALS) diagnosis and Brain-Computer Interface (BCI) technologies by analyzing the electroencephalography (EEG) signals obtained as a result of visual stimuli and attention from ALS patients and healthy controls. It was observed that the success rate significantly increased both in the occipital and central regions in all classifiers, especially in the entropy features. The most successful classification was obtained with the Naïve Bayes (NB) classifier using the Morphological Features (MF) + Variational Mode Decomposition (VMD) -Entropy features at 88.89% in the occipital region and 94.44% in the central region.

P300 as a Potential Indicator in the Evaluation of Neurocognitive Disorders After Traumatic Brain Injury

Few objective indices can be used when evaluating neurocognitive disorders after a traumatic brain injury (TBI). P300 has been widely studied in mental disorders, cognitive dysfunction, and brain injury. Daily life ability and social function are key indices in the assessment of neurocognitive disorders after a TBI. The present study focused on the correlation between P300 and impairment of daily living activity and social function. We enrolled 234 patients with neurocognitive disorders after a TBI according to ICD-10 and 277 age- and gender-matched healthy volunteers. The daily living activity and social function were assessed by the social disability screening schedule (SDSS) scale, activity of daily living (ADL) scale, and scale of personality change following a TBI. P300 was evoked by a visual oddball paradigm. The results showed that the scores of the ADL scale, SDSS scale, and scale of personality change in the patient group were significantly higher than those in the control group. The amplitudes of Fz, Cz, and Pz in the patient group were significantly lower than those in the control group and were negatively correlated with the scores of the ADL and SDSS scales. In conclusion, a lower P300 amplitude means a greater impairment of daily life ability and social function, which suggested more severity of neurocognitive disorders after a TBI. P300 could be a potential indicator in evaluating the severity of neurocognitive disorders after a TBI.

A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition

An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.

Distinguishable spatial-spectral feature learning neural network framework for motor imagery-based brain-computer interface

Objective.Spatial and spectral features extracted from electroencephalogram (EEG) are critical for the classification of motor imagery (MI) tasks. As prevalently used methods, the common spatial pattern (CSP) and filter bank CSP (FBCSP) can effectively extract spatial-spectral features from MI-related EEG. To further improve the separability of the CSP features, we proposed a distinguishable spatial-spectral feature learning neural network (DSSFLNN) framework for MI-based brain-computer interfaces (BCIs) in this study.Approach.The first step of the DSSFLNN framework was to extract FBCSP features from raw EEG signals. Then two squeeze-and-excitation modules were used to re-calibrate CSP features along the band-wise axis and the class-wise axis, respectively. Next, we used a parallel convolutional neural network module to learn distinguishable spatial-spectral features. Finally, the distinguishable spatial-spectral features were fed to a fully connected layer for classification. To verify the effectiveness of the proposed framework, we compared it with the state-of-the-art methods on BCI competition IV datasets 2a and 2b.Main results.The results showed that the DSSFLNN framework can achieve a mean Cohen's kappa value of 0.7 on two datasets, which outperformed the state-of-the-art methods. Moreover, two additional experiments were conducted and they proved that the combination of band-wise feature learning and class-wise feature learning can achieve significantly better performance than only using either one of them.Significance.The proposed DSSFLNN can effectively improve the decoding performance of MI-based BCIs.

Feature Selection Combining Filter and Wrapper Methods for Motor-Imagery Based Brain-Computer Interfaces

Motor imagery (MI) based brain-computer interfaces help patients with movement disorders to regain the ability to control external devices. Common spatial pattern (CSP) is a popular algorithm for feature extraction in decoding MI tasks. However, due to noise and nonstationarity in electroencephalography (EEG), it is not optimal to combine the corresponding features obtained from the traditional CSP algorithm. In this paper, we designed a novel CSP feature selection framework that combines the filter method and the wrapper method. We first evaluated the importance of every CSP feature by the infinite latent feature selection method. Meanwhile, we calculated Wasserstein distance between feature distributions of the same feature under different tasks. Then, we redefined the importance of every CSP feature based on two indicators mentioned above, which eliminates half of CSP features to create a new CSP feature subspace according to the new importance indicator. At last, we designed the improved binary gravitational search algorithm (IBGSA) by rebuilding its transfer function and applied IBGSA on the new CSP feature subspace to find the optimal feature set. To validate the proposed method, we conducted experiments on three public BCI datasets and performed a numerical analysis of the proposed algorithm for MI classification. The accuracies were comparable to those reported in related studies and the presented model outperformed other methods in literature on the same underlying data.

Enhancing P300 based character recognition performance using a combination of ensemble classifiers and a fuzzy fusion method

BACKGROUND

P300-based brain-computer interfaces provide communication pathways without the need for muscle activity by recognizing electrical signals from the brain. The P300 speller is one of the most commonly used BCI applications, as it is very simple and reliable, and it is capable of reaching satisfactory communication performance. However, as with other BCIs, it remains a challenge to improve the P300 speller's performance to increase its practical usability.

NEW METHODS

In this study, we propose a novel multi-feature subset fuzzy fusion (MSFF) framework for the P300 speller to recognize the users' spelling intention. This method includes two parts: 1) feature selection by the Lasso algorithm and feature division; 2) the construction of ensemble LDA classifiers and the fuzzy fusion of those classifiers to recognize user intention.

RESULTS

The proposed framework is evaluated in three public datasets and achieves an average accuracy of 100% after 4 epochs for BCI Competition II Dataset IIb, 96% for BCI Competition III dataset II and 98.3% for the BNCI Horizon Dataset. It indicates that the proposed MSFF method can make use of temporal information of signals and helps to enhance classification performance.

COMPARISON WITH EXISTING METHODS

The proposed MSFF method yields better or comparable performance than previously reported machine learning algorithms.

CONCLUSIONS

The proposed MSFF method is able to improve the performance of P300-based BCIs.

Optimization of Model Training Based on Iterative Minimum Covariance Determinant In Motor-Imagery BCI

The common spatial patterns (CSP) algorithm is one of the most frequently used and effective spatial filtering methods for extracting relevant features for use in motor imagery brain-computer interfaces (MI-BCIs). However, the inherent defect of the traditional CSP algorithm is that it is highly sensitive to potential outliers, which adversely affects its performance in practical applications. In this work, we propose a novel feature optimization and outlier detection method for the CSP algorithm. Specifically, we use the minimum covariance determinant (MCD) to detect and remove outliers in the dataset, then we use the Fisher score to evaluate and select features. In addition, in order to prevent the emergence of new outliers, we propose an iterative minimum covariance determinant (IMCD) algorithm. We evaluate our proposed algorithm in terms of iteration times, classification accuracy and feature distribution using two BCI competition datasets. The experimental results show that the average classification performance of our proposed method is 12% and 22.9% higher than that of the traditional CSP method in two datasets ([Formula: see text]), and our proposed method obtains better performance in comparison with other competing methods. The results show that our method improves the performance of MI-BCI systems.

Habituation of P300 in the Use of P300-based Brain-Computer Interface Spellers: Individuals With Amyotrophic Lateral Sclerosis Versus Age-Matched Controls

The P300-based brain-computer interface speller can provide motor independent communication to individuals with amyotrophic lateral sclerosis (ALS), a progressive neurodegenerative disorder that affects the motor system. P300 amplitude stability is critical for operation of the P300 speller. The P300 has good long-term stability, but to our knowledge, short-term habituation in the P300 speller has not been studied. In the current study, 15 participants: 8 ALS patients and 7 age-matched healthy volunteers (HVs), used 2 versions of P300 spellers, Face speller and Flash speller, each for 30 minutes. The ALS group performed as well as the HVs in both spellers and HVs did better with the Face speller than Flash speller while the ALS group performed equally well in both spellers. Neither intra-run P300 habituation nor inter-run P300 habituation was found. The P300 speller could be a reliable communication device for individuals with ALS.

Neural mechanism for dynamic distractor processing during video target detection: Insights from time-varying networks in the cerebral cortex

In dynamic video target detection tasks, distractors may suddenly appear due to the dynamicity of the visual scene and the uncertainty of the visual information, strongly influencing participants' attention and target detection performance. Moreover, the neural mechanism that accounts for dynamic distractor processing remains unknown, which makes it difficult to compensate for in EEG-based video target detection. Here, cortical activities with high spatiotemporal resolution were reconstructed using the source localization method. The time-varying networks among important brain regions in different cognitive phases, including information integration, decision-making, and execution, were identified to investigate the neural mechanism of dynamic distractor processing. The experimental results indicated that dynamic distractors could induce a P3-like component. In addition, there was obvious asymmetry between the two hemispheres during video target detection. Specifically, the brain responses induced by dynamic distractors were weak and more concentrated in the left hemisphere during the information integration phase; left superior frontal gyrus activity related to preparation for the presence of distractors was critical, while the attention network and primary visual network, especially in the left visual pathway, were more active for dynamic targets during the decision-making phase. These findings provide guidance for designing an effective EEG-based model for dynamic video target detection.

A P300 Brain-Computer Interface Paradigm Based on Electric and Vibration Simple Command Tactile Stimulation

This paper proposed a novel tactile-stimuli P300 paradigm for Brain-Computer Interface (BCI), which potentially targeted at people with less learning ability or difficulty in maintaining attention. The new paradigm using only two types of stimuli was designed, and different targets were distinguished by frequency and spatial information. The classification algorithm was developed by introducing filters for frequency bands selection and conducting optimization with common spatial pattern (CSP) on the tactile evoked EEG signals. It features a combination of spatial and frequency information, with the spatial information distinguishing the sites of stimuli and frequency information identifying target stimuli and disturbances. We investigated both electrical stimuli and vibration stimuli, in which only one target site was stimulated in each block. The results demonstrated an average accuracy of 94.88% for electrical stimuli and 95.21% for vibration stimuli, respectively.

Target Detection Using Ternary Classification During a Rapid Serial Visual Presentation Task Using Magnetoencephalography Data

Background: The rapid serial visual presentation (RSVP) paradigm is a high-speed paradigm of brain–computer interface (BCI) applications. The target stimuli evoke event-related potential (ERP) activity of odd-ball effect, which can be used to detect the onsets of targets. Thus, the neural control can be produced by identifying the target stimulus. However, the ERPs in single trials vary in latency and length, which makes it difficult to accurately discriminate the targets against their neighbors, the near-non-targets. Thus, it reduces the efficiency of the BCI paradigm.

Methods: To overcome the difficulty of ERP detection against their neighbors, we proposed a simple but novel ternary classification method to train the classifiers. The new method not only distinguished the target against all other samples but also further separated the target, near-non-target, and other, far-non-target samples. To verify the efficiency of the new method, we performed the RSVP experiment. The natural scene pictures with or without pedestrians were used; the ones with pedestrians were used as targets. Magnetoencephalography (MEG) data of 10 subjects were acquired during presentation. The SVM and CNN in EEGNet architecture classifiers were used to detect the onsets of target.

Results: We obtained fairly high target detection scores using SVM and EEGNet classifiers based on MEG data. The proposed ternary classification method showed that the near-non-target samples can be discriminated from others, and the separation significantly increased the ERP detection scores in the EEGNet classifier. Moreover, the visualization of the new method suggested the different underling of SVM and EEGNet classifiers in ERP detection of the RSVP experiment.

Conclusion: In the RSVP experiment, the near-non-target samples contain separable ERP activity. The ERP detection scores can be increased using classifiers of the EEGNet model, by separating the non-target into near- and far-targets based on their delay against targets.

Progress in Brain Computer Interface: Challenges and Opportunities

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

Effects of Skin Friction on Tactile P300 Brain-Computer Interface Performance

Tactile perception, the primary sensing channel of the tactile brain-computer interface (BCI), is a complicated process. Skin friction plays a vital role in tactile perception. This study aimed to examine the effects of skin friction on tactile P300 BCI performance. Two kinds of oddball paradigms were designed, silk-stim paradigm (SSP) and linen-stim paradigm (LSP), in which silk and linen were wrapped on target vibration motors, respectively. In both paradigms, the disturbance vibrators were wrapped in cotton. The experimental results showed that LSP could induce stronger event-related potentials (ERPs) and achieved a higher classification accuracy and information transfer rate (ITR) compared with SSP. The findings indicate that high skin friction can achieve high performance in tactile BCI. This work provides a novel research direction and constitutes a viable basis for the future tactile P300 BCI, which may benefit patients with visual impairments.

EEG-Inception: A Novel Deep Convolutional Neural Network for Assistive ERP-Based Brain-Computer Interfaces

In recent years, deep-learning models gained attention for electroencephalography (EEG) classification tasks due to their excellent performance and ability to extract complex features from raw data. In particular, convolutional neural networks (CNN) showed adequate results in brain-computer interfaces (BCI) based on different control signals, including event-related potentials (ERP). In this study, we propose a novel CNN, called EEG-Inception, that improves the accuracy and calibration time of assistive ERP-based BCIs. To the best of our knowledge, EEG-Inception is the first model to integrate Inception modules for ERP detection, which combined efficiently with other structures in a light architecture, improved the performance of our approach. The model was validated in a population of 73 subjects, of which 31 present motor disabilities. Results show that EEG-Inception outperforms 5 previous approaches, yielding significant improvements for command decoding accuracy up to 16.0%, 10.7%, 7.2%, 5.7% and 5.1% in comparison to rLDA, xDAWN + Riemannian geometry, CNN-BLSTM, DeepConvNet and EEGNet, respectively. Moreover, EEG-Inception requires very few calibration trials to achieve state-of-the-art performances taking advantage of a novel training strategy that combines cross-subject transfer learning and fine-tuning to increase the feasibility of this approach for practical use in assistive applications.

The Influence of Visual Attention on The Performance of A Novel Tactile P300 Brain-Computer Interface with Cheeks-Stim Paradigm

Tactile P300 brain-computer interface (BCI) generally has a worse accuracy and information transfer rate (ITR) than the visual-based BCI. It may be due to the fact that human beings have a relatively poor tactile perception. This study investigated the influence of visual attention on the performance of a tactile P300 BCI. We designed our paradigms based on a novel cheeks-stim paradigm which attached the stimulators on the subject's cheeks. Two paradigms were designed as follows: a paradigm with no visual attention and another paradigm with visual attention to the target position. Eleven subjects were invited to perform the two paradigms. We also recorded and analyzed the eyeball movement data during the paradigm with visual attention to explore whether the eyeball movement would have an effect on the BCI classification. The average online accuracy was 89.09% for the paradigm with visual attention, which was significantly higher than that of the paradigm with no visual attention (70.45%). Significant difference in ITR was also found between the two paradigms ([Formula: see text]). The results demonstrated that visual attention was an effective method to improve the performance of tactile P300 BCI. Our findings suggested that it may be feasible to complete an efficient tactile BCI system by adding visual attention.

Impact of Stimulus Features on the Performance of a Gaze-Independent Brain-Computer Interface Based on Covert Spatial Attention Shifts

Regaining communication abilities in patients who are unable to speak or move is one of the main goals in decoding brain waves for brain-computer interface (BCI) control. Many BCI approaches designed for communication rely on attention to visual stimuli, commonly applying an oddball paradigm, and require both eye movements and adequate visual acuity. These abilities may, however, be absent in patients who depend on BCI communication. We have therefore developed a response-based communication BCI, which is independent of gaze shifts but utilizes covert shifts of attention to the left or right visual field. We recorded the electroencephalogram (EEG) from 29 channels and coregistered the vertical and horizontal electrooculogram. Data-driven decoding of small attention-based differences between the hemispheres, also known as N2pc, was performed using 14 posterior channels, which are expected to reflect correlates of visual spatial attention. Eighteen healthy participants responded to 120 statements by covertly directing attention to one of two colored symbols (green and red crosses for "yes" and "no," respectively), presented in the user's left and right visual field, respectively, while maintaining central gaze fixation. On average across participants, 88.5% (std: 7.8%) of responses were correctly decoded online. In order to investigate the potential influence of stimulus features on accuracy, we presented the symbols with different visual angles, by altering symbol size and eccentricity. The offline analysis revealed that stimulus features have a minimal impact on the controllability of the BCI. Hence, we show with our novel approach that spatial attention to a colored symbol is a robust method with which to control a BCI, which has the potential to support severely paralyzed people with impaired eye movements and low visual acuity in communicating with their environment.

Initial study on changes in activity of brain waves during audio stimulation using noninvasive brain–computer interfaces: choosing the appropriate filtering method

Objectives

In this paper series of experiments were carried out in order to check the influence of various sounds on human concentration during visually stimulated tasks performance.

Methods

The obtained data was filtered. For the study purposes various smoothing filters were tested, including Median and Savitzky–Golay Filters; however, median filter only was applied. Implementation of this filter made the obtained data more legible and useful for potential diagnostics purposes. The tests were carried out with the implementation of the Emotiv Flex EEG headset.

Results

The obtained results were promising and complied with the initial assumptions, which stated that the “relax”-phase, despite relaxing sounds stimuli, is strongly affected with the “focus”-phase with distracting sounds, which is clearly visible in the shape of the recorded EEG data.

Conclusions

Further investigations with broader range of subjects is being currently carried out in order to confirm the already obtained results.

The Time Course of Perceptual Closure of Incomplete Visual Objects: An Event-Related Potential Study

Perceptual organization is an important part of visual and auditory information processing. In the case of visual occlusion, whether the loss of information in images could be recovered and thus perceptually closed affects object recognition. In particular, many elderly subjects have defects in object recognition ability, which may be closely related to the abnormalities of perceptual functions. This phenomenon even can be observed in the early stage of dementia. Therefore, studying the neural mechanism of perceptual closure and its relationship with sensory and cognitive processing is important for understanding how the human brain recognizes objects, inspiring the development of neuromorphic intelligent algorithms of object recognition. In this study, a new experiment was designed to explore the realistic process of perceptual closure under occlusion and intact conditions of faces and building. The analysis of the differences in ERP components P1, N1, and Ncl indicated that the subjective awareness of perceptual closure mainly occurs in Ncl, but incomplete information has been processed and showed different manners compared to complete stimuli in N170 for facial materials. Although occluded, faces, but not buildings, still maintain the specificity of perceptual processing. The Ncl by faces and buildings did not show significant differences in both amplitude and latency, suggesting a “completing” process regardless of categorical features.

Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing

The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily affected by noise and redundant information that exists in the multi-channel electroencephalogram (EEG). To solve this problem, many temporal and spatial feature based channel selection methods have been proposed. However, temporal and spatial features do not accurately reflect changes in the power of the oscillatory EEG. Thus, spectral features of MI-related EEG signals may be useful for channel selection. Bispectrum analysis is a technique developed for extracting non-linear and non-Gaussian information from non-linear and non-Gaussian signals. The features extracted from bispectrum analysis can provide frequency domain information about the EEG. Therefore, in this study, we propose a bispectrum-based channel selection (BCS) method for MI-based BCI. The proposed method uses the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from bispectrum analysis to select EEG channels without redundant information. Three public BCI competition datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) were used to validate the effectiveness of our proposed method. The results indicate that our BCS method outperforms use of all channels (83.8% vs 69.4%, 86.3% vs 82.9% and 77.8% vs 68.2%, respectively). Furthermore, compared to the other state-of-the-art methods, our BCS method also can achieve significantly better classification accuracies for MI-based BCI (Wilcoxon signed test, p < 0.05).

The EEG Signal Analysis for Spatial Cognitive Ability Evaluation Based on Multivariate Permutation Conditional Mutual Information-Multi-Spectral Image

This study aims to find an effective method to evaluate the efficacy of cognitive training of spatial memory under a virtual reality environment, by classifying the EEG signals of subjects in the early and late stages of spatial cognitive training. This study proposes a new EEG signal analysis method based on Multivariate Permutation Conditional Mutual Information-Multi-Spectral Image (MPCMIMSI). This method mainly considers the relationship between the coupled features of EEG signals in different channel pairs and transforms the multivariate permutation conditional mutual information features into multi-spectral images. Then, a convolutional neural networks (CNN) model classifies the resultant image data into different stages of cognitive training to objectively assess the efficacy of the training. Compared to the multi-spectral image transformation method based on Granger causality analysis (GCA) and permutation conditional mutual information (PCMI), the MPCMIMSI led to better classification performance, which can be as high as 95% accuracy. More specifically, the Theta-Beta2-Gamma-band combination has the best accuracy. The proposed MPCMIMSI method outperforms the multi-spectral image transformation methods based on GCA and PCMI in terms of classification performance. The MPCMIMSI feature in the Theta-Beta2-Gamma band is an effective biomarker for assessing the efficacy of spatial memory training. The proposed EEG feature-extraction method based on MPCMIMSI offers a new window to characterize spatial information of the noninvasive EEG recordings and might apply to assessing other brain functions.

The Effect of the Graphic Structures of Humanoid Robot on N200 and P300 Potentials

Humanoid robots are widely used in brain computer interface (BCI). Using a humanoid robot stimulus could increase the amplitude of event-related potentials (ERPs), which improves BCI performance. Since a humanoid robot contains many human elements, the element that increases the ERPs amplitude is unclear, and how to test the effect of it on the brain is a problem. This study used different graphic structures of an NAO humanoid robot to design three types of robot stimuli: a global robot, its local information, and its topological action. Ten subjects first conducted an odd-ball-based BCI (OD-BCI) by applying these stimuli. Then, they accomplished a delayed matching-to-sample task (DMST) that was used to specialize the encoding and retrieval phases of the OD-BCI task. In the retrieval phase of the DMST, the global stimulus induces the largest N200 and P300 potentials with the shortest latencies in the frontal, central, and occipital areas. This finding is in accordance with the P300 and classification performance of the OD-BCI task. When induced by the local stimulus, the subjects responded faster and more accurately in the retrieval phase of the DMST than in the other two conditions, indicating that the local stimulus improved the subject's responses. These results indicate that the OD-BCI task causes subject's retrieval work when the subject recognizes and outputs the stimulus. The global stimulus that contains topological and local elements could make brain react faster and induce larger ERPs, this finding could be used during the development of visual stimuli to improve BCI performance.