Brain-computer interface (BCI) is a novel human-computer interaction model, which does not depend on the conventional output pathway (peripheral nerve and muscle tissue). In the past three decades, it has attracted the interest of researchers and gradually become a research hotspot. As a typical BCI application, the brain-controlled wheelchair (BCW) could provide a new communicating channel with the external environment for physically disabled people. However, the main challenge of BCW is how to decode multi-degree of freedom control instruction from electroencephalogram (EEG) as soon as possible. The research progress of BCW has been developed rapidly over the past fifteen years. In this review, we investigate the BCW from multiple perspectives, include the type of signal acquisition, the pattern of commands for the control system and the working mechanism of the control system. Furthermore, we summarize the development trend of BCW based on the previous investigation, and it is mainly manifested in three aspects: from a wet electrode to dry electrode, from single-mode to multi-mode, and from synchronous control to asynchronous control. With the continuous development of BCW, we also find new functions have been introduced into BCW to increase its stability and robustness. It is believed that BCW will be able to enter the real-life from the laboratory and will be widely used in rehabilitation medicine in the future.
As a new type of brain–computer interface (BCI), the rapid serial visual presentation (RSVP) paradigm has attracted significant attention. The mechanism of RSVP is detecting the P300 component corresponding to the target image to realize fast and correct recognition. This paper proposed an improved EEGNet model to achieve good performance in offline and online data. Specifically, the data were filtered by xDAWN to enhance the signal-to-noise ratio of the electroencephalogram (EEG) signals. The focal loss function was used instead of the cross-entropy loss function to solve the classification problems of unbalanced samples. Additionally, the subject-specific data were fed to the improved EEGNet model to obtain a subject-specific model. We applied the proposed model at the BCI Controlled Robot Contest in World Robot Contest 2021 and won the second place. The average recall rate of the four participants reached 51.56% in triple classification. In the offline data benchmark dataset (64 subjects-RSVP tasks), the average recall rates of groups A and B reached 76.07% and 78.11%, respectively. We provided an alternative method to identify targets based on the RSVP paradigm.
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