Paper Info
Title
EEG-Based Driver Drowsiness Estimation Using an Online Multi-View and Transfer TSK Fuzzy System
Abstract
In the field of intelligent transportation, transfer learning (TL) is often used to recognize EEG-based drowsy driving for a new subject with few subject-specific calibration data. However, most of existing TL-based models are offline, non-transparent, and in which features are only represented from one view (usually only one algorithm is used to extract features). In this paper, we consider an online multi-view regression model with high interpretability. By taking the 1-order TSK fuzzy system as the basic regression component and injecting the nature of the multi-view settings into the existing transfer learning framework and enforcing the consistencies across different views, we propose an online multi-view & transfer TSK fuzzy system for driver drowsiness estimation. In this novel model, features in both the source domain and the target domain are represented from multi-view perspectives such that more pattern information can be utilized during model training. Also, comparing with offline training, the proposed online fuzzy system meets the practical requirements more competently. An experiment on a driving dataset demonstrates that the proposed fuzzy system has smaller drowsiness estimation errors and higher interpretability than introduced benchmarking models.
Year
DOI
Venue
2021
10.1109/TITS.2020.2973673
IEEE Transactions on Intelligent Transportation Systems
Keywords
DocType
Volume
Transfer learning,multi-view learning,TSK fuzzy systems,EEG
Journal
22
Issue
ISSN
Citations 
3
1524-9050
1
PageRank 
References 
Authors
0.35
0
5
Name
Order
Citations
PageRank
Yizhang Jiang138227.24
Yuanpeng Zhang233.48
Chuang Lin33040390.74
Dongrui Wu4165893.01
Chin-Teng Lin53840392.55