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Driver Drowsiness EEG Detection Based on Tree Federated Learning and Interpretable Network

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journal contribution
posted on 2023-02-17, 10:41 authored by X Qin, Y Niu, Huiyu Zhou, X Li, W Jia, Y Zheng

Accurate identification of driver’s drowsiness state through Electroencephalogram (EEG) signals can effectively reduce traffic accidents, but EEG signals are usually stored in various clients in the form of small samples. This study attempts to construct an efficient and accurate privacy-preserving drowsiness monitoring system, and proposes a fusion model based on tree Federated Learning (FL) and Convolutional Neural Network (CNN), which can not only identify and explain the driver’s drowsiness state, but also integrate the information of different clients under the premise of privacy protection. Each client uses CNN with the Global Average Pooling (GAP) layer and shares model parameters. The tree FL transforms communication relationships into a graph structure, and model parameters are transmitted in parallel along connected branches of the graph. Moreover, the Class Activation Mapping (CAM) is used to find distinctive EEG features for representing specific classes. On EEG data of 11 subjects, it is found that this method has higher average accuracy, F1-score and AUC than the traditional classification method, reaching 73.56%, 73.26% and 78.23%, respectively. Compared with the traditional FL algorithm, this method better protects the driver’s privacy and improves communication efficiency.

History

Author affiliation

School of Computing and Mathematical Sciences, University of Leicester

Version

  • AM (Accepted Manuscript)

Published in

International Journal of Neural Systems

Publisher

World Scientific Publishing

issn

0129-0657

Copyright date

2023

Available date

2024-01-19

Language

en

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