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Brain-computer interface (BCI) has received attention from researchers in many research fields as an emerging control technology that translates human thoughts into actions. Existing motor imagery (MI)-based BCI systems can only decode a limited number of neural intentions, thus limiting the scope of BCI applications. We propose a speed imagery (SI)-BCI paradigm, which aims to decode spontaneous SI intentions. Thus, the number of decodable intentions is increased by using the natural continuity of the physical quantity of speed. We further build a synchronous bimodal acquisition system of spontaneous SI intentions, which is capable of acquiring EEG signals and functional near-infrared spectroscopy (fNIRS) signals simultaneously. Specifically, an interpretable bimodal signal registration and attention algorithm, called STformer, is proposed for SI classification, which consists of two parts: 1) a bimodal registration algorithm for signal fusion that improves the tightness of spatio-temporal coupling of EEG and fNIRS signals. 2) a dual-path spatio-temporal feature extraction and global attention network that makes full use of bimodal spatio-temporal features for SI intention classification. Experimental results on two datasets show that the proposed SI-BCI system outperforms state-of-the-art methods in terms of data reliability, classification performance and interpretability.

A bimodal registration and attention method for speed imagery brain-computer interface