3D-STCNN: Spatiotemporal Convolutional Neural Network Based on EEG 3D Features for Detecting Driving Fatigue

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 (ATNet) based on attention mechanism and an Attention Convolutional Neural Network (ACNN) 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.

Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid

“…Machine learning is a type of AI focused on building computer systems that learn from data, enabling software to improve its performance over time [ [25] , [26] , [27] , [28] ]. Also, ANNs are a class of statistical learning algorithms used in machine learning and cognitive science domains [ [29] , [30] , [31] , [32] , [33] ]. In this study, using ANNs and different evolutionary algorithms, parameters such as μ nf and TC were analyzed and estimated in silica-alumina-MWCNT/water–NF.…”

Encoding temporal information in deep convolution neural network

“…There has been significant research to EEG (Bashivan et al, 2015 ; Zhang et al, 2019 ; Li et al, 2020 ; Peng et al, 2024 ) to combine the time-dependency feature while learning local and global features. Despite that, the approaches are useful but often come with the cost of dedicated architecture to a specific task, are computationally expensive, and cannot be generalized to different tasks.…”