  The key to electroencephalography (EEG)-based brain-computer interface (BCI)
lies in neural decoding, and its accuracy can be improved by using hybrid BCI
paradigms, that is, fusing multiple paradigms. However, hybrid BCIs usually
require separate processing processes for EEG signals in each paradigm, which
greatly reduces the efficiency of EEG feature extraction and the
generalizability of the model. Here, we propose a two-stream convolutional
neural network (TSCNN) based hybrid brain-computer interface. It combines
steady-state visual evoked potential (SSVEP) and motor imagery (MI) paradigms.
TSCNN automatically learns to extract EEG features in the two paradigms in the
training process, and improves the decoding accuracy by 25.4% compared with the
MI mode, and 2.6% compared with SSVEP mode in the test data. Moreover, the
versatility of TSCNN is verified as it provides considerable performance in
both single-mode (70.2% for MI, 93.0% for SSVEP) and hybrid-mode scenarios
(95.6% for MI-SSVEP hybrid). Our work will facilitate the real-world
applications of EEG-based BCI systems.
