CASCE: A Contrastive Representation Learning Framework for Motor Imagery EEG-Based Unilateral Upper Limb Decoding

Nonmuscle channels are able to enhance voluntary movement control or improve rehabilitation efficacy, and analyzing electroencephalogram (EEG) signals yields valuable insights into functional neural activity during motor imagery (MI). However, most existing MI-EEG datasets primarily focus on exploring more discriminative tasks to control external devices, making it difficult to meet the actual needs of motor function rehabilitation. In addition, the expensive acquisition of labeled data hinders the practical application of EEG-based rehabilitation. In this study, we aim to collect EEG signals from 13 subjects performing four MI tasks of the unilateral upper limb: arm lifting/lowering and forearm pronation/supination. Moreover, we propose a Contrastive representation learning framework with an Attention Spatiotemporal Convolutional Encoder (CASCE) for MI-EEG decoding. In the pretraining phase, the unlabeled data from background samples and label-erased samples are applied to the noise addition module and the scaling module to generate pairs of positive and negative samples. These sample pairs are input into the encoder to learn temporal and spatial information, and the encoder parameters are further adjusted by using the contrastive loss function to measure the similarity of the feature information in the projection space. During the fine-tuning phase, the transferred encoder and the classification head are specifically adapted to the labeled MI-EEG data. The CASCE framework achieves a classification accuracy of 51.58% on the refined upper limb MI dataset. In addition, CASCE outperforms state-of-the-art (SOTA) methods with accuracies of 88.51% and 90.34% on the brain-computer interface (BCI) Competition IV 2a and 2b datasets, respectively.