InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection.

Hong Zeng,Wael Zakaria,Wanzeng Kong,Jiaming Zhang,Xiufeng Li,Fabio Babiloni,Borghini Gianluca

doi:10.3390/s20247251

Abstract

Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the significant differences in EEG signals across subjects, and difficulty in collecting sufficient EEG samples for analysis during driving, detecting fatigue across subjects through using EEG signals remains a challenge. EasyTL is a kind of transfer-learning model, which has demonstrated better performance in the field of image recognition, but not yet been applied in cross-subject EEG-based applications. In this paper, we propose an improved EasyTL-based classifier, the InstanceEasyTL, to perform EEG-based analysis for cross-subject fatigue mental-state detection. Experimental results show that InstanceEasyTL not only requires less EEG data, but also obtains better performance in accuracy and robustness than EasyTL, as well as existing machine-learning models such as Support Vector Machine (SVM), Transfer Component Analysis (TCA), Geodesic Flow Kernel (GFK), and Domain-adversarial Neural Networks (DANN), etc.

Highlights

In recent years, there has been rapid increase in the number of traffic accidents, yielded to huge losses to people’s lives and their properties
We propose an Easy Transfer Learning (EasyTL)-based model, named InstanceEasyTL, that focuses on performing fatigue detection across subjects based on EEG
To verify the performance of InstanceEasyTL, we compare it with the traditional methods, such as Support Vector Machine (SVM), Transfer Component Analysis (TCA), Geodesic Flow Kernel (GFK), Domain-adversarial Neural Networks (DANN), and EasyTL

Summary

Introduction

There has been rapid increase in the number of traffic accidents, yielded to huge losses to people’s lives and their properties. A lot of evidence shows that driving under the condition of fatigue state (fatigue driving) is one of the main causes of traffic accidents. Statistical results indicate that fatigue driving leads to 35–45% of road traffic accidents [1,2,3], and directly causes. 1550 deaths, 71,000 injuries, and $12.5 billion in economic losses each year according to the reports of American National Highway Traffic Safety Administration (NHTSA) [4]. It is of vital importance to design an efficient and accurate analysis model for detecting fatigue over time during driving. There are three ways to detect fatigue mental states.

Methods

Results

Discussion

Conclusion