Deep Transfer Regression for EEG-based Driving Fatigue Detection

Abstract

Recently, Electroencephalography (EEG) has been increasingly utilized in driving fatigue detection tasks. However, the inter-subject variabilities in EEG data render models trained on one subject ineffective for being directly applied to others. Transfer learning has been widely used to address this issue, but most existing transfer learning algorithms primarily focused on classification tasks. Therefore, we propose a transfer regression model for EEG-based driving fatigue detection, whose core idea is to learn the weights of models from various source domain data and a base model from target domain training data through an attention network. By assembling models trained on different domain data, predictions are obtained. We conducted experiments on the two subsets of the benchmark SEED-VIG dataset, and the results demonstrate that our transfer regression model effectively enhances the driving fatigue detection performance. The source code is available from https://github.com/SunseaIU/ATR-EEG.