MSATNet: multi-scale adaptive transformer network for motor imagery classification - Hu, Lingyan; Hong, Weijie; Liu, Lingyu (Frontiers in Neuroscience, 2023)
Motor imagery brain-computer interface(MI-BCI) can parse user motor imagery to achieve wheel-chair control or motion control for smart prostheses. However, problems of poor feature extraction and low cross-subject performance exist in the model for motor imagery classification tasks. To address these problems, we propose a multi-scale adaptive transformer network (MSATNet) for motor imagery classification. Therein, we design a multi-scale feature extraction (MSFE) module to extract multi-band highly-discriminative features. Through the adaptive temporal transformer (ATT) module, the temporal decoder and multi-head attention unit are used to adaptively extract temporal dependencies. Efficient transfer learning is achieved by fine-tuning target subject data through the subject adapter (SA) module. Within-subject and cross-subject experiments are per-formed to evaluate the classification performance of the model on the BCI Competition IV 2a and 2b datasets. The MSATNet outperforms benchmark models in classification performance, reaching 81.75% and 89.34% accuracies for the within-subject experiments and 81.33% and 86.23% accura-cies for the cross-subject experiments. The experimental results demonstrate that the proposed method can help build a more accurate MI-BCI system.