TY  - JOUR
PY  - 2023//
TI  - Driver drowsiness EEG detection based on tree federated learning and interpretable network
JO  - International journal of neural systems
A1  - Qin, Xue
A1  - Niu, Yi
A1  - Zhou, Huiyu
A1  - Li, Xiaojie
A1  - Jia, Weikuan
A1  - Zheng, Yuanjie
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 0129-0657
UR  - http://dx.doi.org/10.1142/S0129065723500090
ID  - ref1
ER  -