Deep Domain Adaptation for EEG-Based Cross-Subject Cognitive Workload Recognition

Abstract

For cognitive workload recognition, electroencephalography (EEG) signals vary from different subjects, thus hindering the recognition performance when direct extending to a new subject. Though calibrating the new subject or collecting more data would alleviate this issue, it is generally time-consuming and unrealistic. To cope with the problem, we propose a deep domain adaptation scheme for EEG-based cross-subject cognitive workload recognition, using the knowledge from the existing subjects (source domain) to improve the recognition performance of a new subject (target domain). Specifically, the proposed method has four modules: the EEG features extractor, feature distribution alignment, label classifier, and domain discriminator. The EEG feature extractor learns transferable shallow feature representation of both domains. The label classifier further learns the deep representation from the shallow one and trains the classifier. To reduce the domain discrepancy, we employ feature distribution alignment and domain discriminator from shallow and deep representation views using a distribution discrepancy metric and adversarial training with the feature extractor, respectively. We conduct experiments to recognize the low and high workload levels on a self-designed EEG dataset with 38 subjects performing the working memory cognitive task. Experimental results validate that our proposed framework outperforms the baselines significantly.