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EEG-Based Mental Workload Classification Method Based on Hybrid Deep Learning Model Under IoT
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AI
DOI：10.1109/JBHI.2023.3281793.png)
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Abstract
En 中文
Automatically detecting human mental workload to prevent mental diseases is highly important. With the development of information technology, remote detection of mental workload is expected. The development of artificial intelligence and Internet of Things technology will also enable the identification of mental workload remotely based on human physiological signals. In this article, a method based on the spatial and time-frequency domains of electroencephalography (EEG) signals is proposed to improve the classification accuracy of mental workload. Moreover, a hybrid deep learning model is presented. First, the spatial domain features of different brain regions are proposed. Simultaneously, EEG time-frequency domain information is obtained based on wavelet transform. The spatial and time-frequency domain features are input into two types of deep learning models for mental workload classification. To validate the performance of the proposed method, the Simultaneous Task EEG Workload public database is used. Compared with the existing methods, the proposed approach shows higher classification accuracy. It provides a novel means of assessing mental workload.
Keywords:
Electroencephalography
Task analysis
Feature extraction
Brain modeling
Biomedical monitoring
Electrocardiography
Psychology
Convolutional neural network
electroencephalogram
long short-term memory
mental workload
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