Priming cross-session motor imagery classification with a universal deep domain adaptation framework

Abstract

Electroencephalogram (EEG) based motor imagery (MI) brain–computer interfaces (BCI) are widely used in applications related to rehabilitation and external device control. However, due to the non-stationary and low signal-to-noise ratio characteristics of EEG, classifying motor imagery tasks of the same participant from different recording sessions is generally challenging. Whether the classification accuracy of cross-session MI can be improved from the perspective of domain adaptation is a question worth verifying. In this paper, we propose a Siamese deep domain adaptation (SDDA) framework for cross-session MI classification based on mathematical models in domain adaptation theory. The SDDA framework primarily consists of three components: a novel preprocessing method based on domain-invariant features, a maximum mean discrepancy (MMD) loss for aligning source and target domain embedding features, and an improved cosine-based center loss designed to suppress the influence of noise and outliers on the neural network. The SDDA framework has been validated with two classic and popular convolutional neural networks (EEGNet and ConvNet) from BCI research field in two MI EEG public datasets (BCI Competition IV IIA, IIB). Compared with the vanilla EEGNet and ConvNet, the SDDA framework improves the MI classification accuracy by 10.49%, 7.60% respectively in IIA dataset, and 4.59%, 3.35% in IIB dataset. The SDDA not only significantly improves the classification performance of the vanilla networks but also surpasses state-of-the-art transfer learning methods, making it a superior and user-friendly approach for MI classification.