A Zero-Training Method for RSVP-Based Brain Computer Interface

Xujin Li, Shuang Qiu, Wei Wei, Huiguang He

Abstract: Brain-Computer Interface (BCI) is a communication system that transmits information between the brain and the outside world which does not rely on peripheral nerves and muscles. Rapid Serial Visual Presentation (RSVP)-based BCI system is an efficient and robust information retrieval method based on human vision. However, the current RSVP-BCI system requires a time-consuming calibration procedure for one new subject, which greatly restricts the use of the BCI system. In this study, we propose a zero-training method based on convolutional neural network and graph attention network with adaptive graph learning. Firstly, a single-layer convolutional neural network is used to extract EEG features. Then, the extracted features from similar samples were adaptively connected to construct the graph. Graph attention network was employed to classify the target sample through decoding the connection relationship of adjacent samples in one graph. Our proposed method achieves 86.76% mean balanced-accuracy (BA) in one self-collected dataset containing 31 subjects, which performs better than the comparison methods. This indicates our method can realize zero-calibration for an RSVP-based BCI system.