Subject-Independent Drowsiness Recognition from Single-Channel EEG with an Interpretable CNN-LSTM model

For EEG-based drowsiness recognition, it is desirable to use subject-independent recognition since conducting calibration on each subject is time-consuming. In this paper, we propose a novel Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) model for subject-independent drowsiness rec...

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Bibliographic Details

Published in:	arXiv.org
Main Authors:	Cui, Jian, Zirui Lan, Zheng, Tianhu, Liu, Yisi, Sourina, Olga, Wang, Lipo, Müller-Wittig, Wolfgang
Format:	Paper
Language:	English
Published:	Ithaca Cornell University Library, arXiv.org 21.11.2021
Subjects:	Artificial neural networks Deep learning Electroencephalography Machine learning Recognition Sleepiness Visualization
ISSN:	2331-8422
Online Access:	Get full text
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