3D-STCNN: Spatiotemporal Convolutional Neural Network Based on EEG 3D Features for Detecting Driving Fatigue
Journal Title: Journal of Data Science and Intelligent Systems - Year 2024, Vol 2, Issue 1
Abstract
Fatigue driving has become one of the main causes of traffic accidents, and driving fatigue detection based on electroencephalogram (EEG) can effectively evaluate the driver's mental state and avoid the occurrence of traffic accidents. This article evaluates a feature extraction method for extracting multiple features of EEG signals and establishes a spatiotemporal convolutional neural network (STCNN) to detect driver fatigue. Firstly, we constructed a three-dimensional feature of the EEG signal, which includes the frequency domain, time domain, and spatial features of the EEG signal. Then, we use STCNN for fatigue state classification. STCNN is composed of an attention time network based on attention mechanism and an attention convolutional neural network based on attention mechanism. In addition, we conducted fatigue driving experiments and collected EEG signals from 14 subjects in both awake and fatigued states, ultimately collecting EEG data under three different driving task loads. We conducted extensive experiments on this basis and compared the effectiveness of STCNN and six competitive methods. The results show that the classification accuracy of STCNN is 87.55%, which can effectively detect the fatigue status of drivers.
Authors and Affiliations
Bo Peng, Dongrui Gao, Manqing Wang, Yongqing Zhang
Keywords
Related Articles
- EP ID EP752173
- DOI 10.47852/bonviewJDSIS3202983
- Views 15
- Downloads 0
Voice Biomarkers for Parkinson's Disease Prediction Using Machine Learning Models with Improved Feature Reduction Techniques
As a chronic and life-threatening disease, Parkinson’s disease (PD) causes people to become rigid and inactive and have shaky voices. There is an argument that current PD detection techniques are ineffective due to their...
Correlation Filters in Machine Learning Algorithms to Select Demographic and Individual Features for Autism Spectrum Disorder Diagnosis
Autism spectrum disorder is currently considered one of the main neurodevelopmental disorders with predominant characteristics of difficulty in social communication and cognitive skills, and limited and repetitive patter...
Customer Segmentation Using Machine Learning Model: An Application of RFM Analysis
Machine learning (ML) encompasses a diverse array of both supervised and unsupervised techniques that facilitate prediction, classification, and anomaly detection. Among the many fields of application for such techniques...
Bootstrap Methods for Canonical Correlation Analysis of Functional Data
The bootstrap method is a very general resampling procedure for investigating the distributional property of statistics. In this paper, we present two bootstrap methods with the aim of studying the functional canonical c...
Dynamic Neural Network Architecture Design for Predicting Remaining Useful Life of Dynamic Processes
The prediction of remaining useful life is critical in predictive health management. This is done to reduce the expenses associated with operation and maintenance by avoiding errors and failures in dynamic processes. Rec...