A Brain–Computer Interface Based on Miniature-Event-Related Potentials Induced by Very Small Lateral Visual Stimuli

A Brain–Computer Interface Based on Miniature-Event-Related Potentials Induced by Very Small Lateral Visual Stimuli 170 177 IEEE Transactions on Biomedical Engineering (TBME)

A Brain–Computer Interface Based on Miniature-Event-Related Potentials Induced by Very Small Lateral Visual Stimuli

Traditional visual brain-computer interfaces (BCIs) was faced with the dilemma of whether or not to use large-size visual stimuli to code instructions. On the one hand, to overcome the noisy electroencephalography (EEG) environment, scientists preferred to use strong visual stimuli to elicit large-amplitude brain signals to control BCIs; on the other hand, users would easily feel visual fatigue and other adverse symptoms for long-term exposure to the strong or even irritating stimuli. A challenging approach to address this problem is to develop a method to recognize weak BCI signals induced by small visual stimuli. We propose a new BCI instruction encoding method——space-code division multiple access (SCDMA) scheme, and a new EEG decoding method——discriminative canonical pattern matching (DCPM) algorithm to implement a vision friendly and high-efficiency BCI system. Thirty-two characters are encoded by very small visual stimuli, which only subtends 0.5º of visual angle and are placed outside the fovea vision. Such small visual stimulus could only induce a miniature event-related potential (ERP) about 0.5 μV in amplitude. Notably, compared to the traditional P300 and SSVEP features, the miniature ERP has a reduction of about 10~20 dB in signal-to-noise rate, which poses a great challenge for BCI to recognize. Online tests of the new BCI system show a peak information transfer rate of 63.33 bits/min, demonstrating the feasibility of using very small and inconspicuous visual stimuli to implement an efficient BCI system. Therefore, the proposed innovative technique can lead to a considerable extension of applicable brain signals for BCIs, which can broaden the category of BCIs and strengthen the brain-computer communication.

Minpeng Xu

Minpeng Xu is with the Laboratory of Neural Engineering and Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China, and also with Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine,Tianjin University, Tianjin, China

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