A Review of Transfer Learning for EEG-Based Driving Fatigue Detection

Abstract

Driver mental state detection has been playing an increasingly significant role in safe driving for decades. Electroencephalogram (EEG)-based detection methods have already been applied to improve detection performance. However, numerous problems still have not been addressed in practical applications. Specifically, most of the existing traditional methods require a large number of training data, caused by differences in cross-subject samples and cross-time of the same subject, resulting in enormous calculations and time consumption. To overcome the above limitations, transfer learning, which applies data or knowledge from the source domain to the target domain, has been widely adopted in EEG processing. This article reviews the current state of mainstream transfer learning methods and their application based on driver mental state detection. To the best of our knowledge, this is the first comprehensive review of transfer learning methods for driving fatigue detection.