A Small-Sample Method with EEG Signals Based on Abductive Learning for Motor Imagery Decoding

Motor imagery (MI) electroencephalogram (EEG) decoding, as a core component widely used in noninvasive brain-computer interface (BCI) system, is critical to realize the interaction purpose of physical world and brain activity. However, the conventional methods are challenging to obtain desirable results for two main reasons: there is a small amount of labeled data making it difficult to fully exploit the features of EEG signals, and lack of unified expert knowledge among different individuals. To handle these dilemmas, a novel small-sample EE -G decoding method based on abductive learning (SSE-ABL) is proposed in this paper, which integrates perceiving module that can extract multiscale features of multi-channel EEG in semantic level and knowledge base module of brain science. The former module is trained via pseudo-labels of unlabeled EEG signals generated by abductive learning, and the latter is refined via the label distribution predicted by semi-supervised learning. Experimental results demonstrate that SSE-ABL has a superior performance compared with state-of-the-art methods and is also convenient for visualizing the underlying information flow of EEG decoding.