Custom Domain Adaptation: A New Method for Cross-Subject, EEG-Based Cognitive Load Recognition

Electroencephalograms (EEG) have shown to be a useful approach to measure the cognitive load in tasks where mental effort is involved. However, EEG signals present a high variability among subjects as well as a non-stationary behavior, so that distributions among samples of different subjects are mismatched. Methods based on Unsupervised Domain Adaptation (UDA) have been used as an effective solution to reduce such discrepancy, while the ones leveraged by deep learning (D-UDA) have improved the classification results over shallow approaches. However, most D-UDA methods assume that even though there are differences in marginal distributions between source and target domains, their conditional distributions remain fixed, which does not hold in many EEG databases. To address this problem, we propose a new D-UDA method, named Custom Domain Adaptation (CDA), which integrates Adaptive Batch Normalization (AdaBN) and Maximum Mean Discrepancy (MMD) into two independent deep neural networks in order to reduce the marginal and conditional distribution differences. CDA was compared with six popular D-UDA methods using a free-available dataset of cognitive loads and obtained an accuracy of 98.2± 2.67%, which outperformed these state-of-the-art methods.