Multiple Scale Convolutional Few-Shot Learning Networks for Online P300-Based Brain–Computer Interface and Its Application to Patients With Disorder of Consciousness

P300 brain–computer interfaces (BCIs) have significant potential for detecting and assessing residual consciousness in patients with disorders of consciousness (DoC) but are limited by insufficient data collected from them. In this study, a multiple scale convolutional few-shot learning (MSCNN-FSL) network was proposed to detect and recognize small sample P300 signals. A multiple scale convolutional neural network (MSCNN) was developed to learn different scale features from different scales of receptive fields to obtain more information from electroencephalograms (EEGs). Then, a prototypical network with cosine distance was introduced as a classifier to classify small sample P300 signals. The MSCNN-FSL was evaluated in two independent online BCI experiments. In the first P300 speller experiment, the presented network achieves good character recognition performance with average accuracies of 98.02% ± 1.70%. In the second experiment, eight healthy controls achieved photograph recognition performance with average accuracies of 98.75% ± 1.49%, and three of the 12 patients with DoC achieved more than 64% online accuracies with significance. Our results indicated that the proposed MSCNN-FSL could correctly assess the three patients who may have residual consciousness but were misdiagnosed by the coma recovery scale-revised (CRS-R). Clinical evaluation after two months did prove our BCI assessment results for the three patients.