EEG-Based Mental Workload Classification Method Based on Hybrid Deep Learning Model Under IoT

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.

Authors

• Shiliang Shao
• Guangjie Han
• Ting Wang City University of Hong Kong, Hong Kong SAR, China
• Chuan Lin Guizhou University
• Chunhe Song
• Chen Yao

Keywords

• Electroencephalography
• Task analysis
• Feature extraction
• Brain modeling
• Biomedical monitoring
• Electrocardiography
• Psychology
• Deep Learning
• Cognitive Load
• Deep Learning Models
• Internet Of Things
• Hybrid Deep Learning Model
• Mental Workload Classification
• Brain Regions
• Classification Accuracy
• Spatial Features
• Physiological Signals
• Wavelet Transform
• Spatial Domain
• Domain Features
• Development Of Artificial Intelligence
• Internet Of Things Technology
• Time-frequency Domain Features
• Convolutional Neural Network
• Support Vector Machine
• Time Domain
• Frequency Domain
• Bidirectional Long Short-term Memory
• Long Short-term Memory Model
• Electrocardiogram Signals
• Long Short-term Memory
• ResNet Model
• Heart Rate Variability
• Functional Brain Networks
• Channel Signal
• Support Vector Regression
• Multilayer Perceptron
• electroencephalogram
• mental workload
• Humans
• Signal Processing, Computer-Assisted
• Workload
• Brain
• Wavelet Analysis
• Algorithms