Improving motor imagery through a mirror box for BCI users

The aim of this study was to evaluate mirror visual feedback (MVF) as a training tool for brain-computer interface (BCI) users. This is because approximately 20-30% of subjects require more training to operate a BCI system using motor imagery. Electroencephalograms (EEGs) were recorded from 18 healthy subjects, using event-related desynchronization (ERD) to observe the responses during the movement or movement intention of the hand for the conditions of control, imagination, and the MVF with the mirror box. We constituted two groups: group 1: control, imagination, and MVF; group 2: control, MVF, and imagination. There were significant differences in imagination conditions between groups using MVF before or after imagination (right-hand, P = 0.0403; left-hand, P = 0.00939). The illusion of movement through MVF is not possible in all subjects, but even in those cases, we found an increase in imagination when the subject used the MVF previously. The increase in the r2s of imagination in the right and left hands suggests cross-learning. The increase in motor imagery recorded with EEG after MVF suggests that the mirror box made it easier to imagine movements. Our results provide evidence that the MVF could be used as a training tool to improve motor imagery.NEW & NOTEWORTHY The increase in motor imagery recorded with EEG after MVF (mirror visual feedback) suggests that the mirror box made it easier to imagine movements. Our results demonstrate that MVF could be used as a training tool to improve motor imagery.

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.

Wheelchair Control in a Virtual Environment by Healthy Participants Using a P300-BCI Based on Tactile Stimulation: Training Effects and Usability

Tactile stimulation is less frequently used than visual for brain-computer interface (BCI) control, partly because of limitations in speed and accuracy. Non-visual BCI paradigms, however, may be required for patients who struggle with vision dependent BCIs because of a loss of gaze control. With the present study, we attempted to replicate earlier results by Herweg et al. (2016), with several minor adjustments and a focus on training effects and usability. We invited 16 healthy participants and trained them with a 4-class tactile P300-based BCI in five sessions. Their main task was to navigate a virtual wheelchair through a 3D apartment using the BCI. We found significant training effects on information transfer rate (ITR), which increased from a mean of 3.10–9.50 bits/min. Further, both online and offline accuracies significantly increased with training from 65% to 86% and 70% to 95%, respectively. We found only a descriptive increase of P300 amplitudes at Fz and Cz with training. Furthermore, we report subjective data from questionnaires, which indicated a relatively high workload and moderate to high satisfaction. Although our participants have not achieved the same high performance as in the Herweg et al. (2016) study, we provide evidence for training effects on performance with a tactile BCI and confirm the feasibility of the paradigm.