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

Abstract

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.

iTa-DFiE: An Innovative Tensor Algebra-Based Detection Framework for Incomplete Noninvasive Electroencephalography

“…1) Statistical method: including linear interpolation and splines. These simple interpolation techniques estimate missing values effectively for short gaps by exploiting the smoothness characteristic in time series data [16], [24][25]. In other words, they estimate missing values based on temporal continuity of time series that expect missing values are close to values in neighboring time ticks and following their moving trends.…”

“…and Xwe X first formulate the vectorizations of   , XZ and matricizations of   0 ,, Q Q B , as shown in(25,26,27).…”

Identifying Thematics in a Brain‐Computer Interface Research

“…Understanding areas of the brain and their function enables targeted BCI to be developed that aim to detect specifc signals these may be electric or metabolic [4,5,27,[39][40][41][42]. [37,40,43,44].…”

“…Understanding areas of the brain and their function enables targeted BCI to be developed that aim to detect specific signals these may be electric or metabolic [ 4 , 5 , 27 , 39 – 42 ]. Electric signals fall into the bands: alpha (8–12 Hz) which is associated with closing the eyes and relaxed states, awareness without concentration, good mood, calmness, learning new information, and self-awareness; beta (12–30 Hz) are emitted with energetic thinking, attention and alertness, and anxiousness; gamma (25–100 Hz) which associates with writing and reading; delta (1–4 Hz) related to deep sleep and unconsciousness; mu (7–13 Hz) that deals with motor neurons in a rest state; and theta (4–7 Hz) that are associated with sleep [ 37 , 40 , 43 , 44 ].…”