ArticleCognitive neurodynamics2018
EEG classification of driver mental states by deep learning.
Article in Cognitive neurodynamics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
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Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis.PloS one · 2020Pooled it
- DMG-GCN: A Dynamic Microstate-Guided Graph Convolutional Network for EEG Cognitive Workload Decoding in Air Traffic Control.Biosensors · 2026Article
- Unsupervised Identification of Driving Styles from Naturalistic Driving Data Through a Context-Normalized Framework.Sensors (Basel, Switzerland) · 2026Article
- Research on depression diagnosis method based on multi-scale analysis of frontal lead EEG.BMC medical informatics and decision making · 2026Article
- Decoding binocular color differences via EEG signals: linking ERP dynamics to chromatic disparity in CIELAB space.Experimental brain research · 2025Article
- CubicPat: Investigations on the Mental Performance and Stress Detection Using EEG Signals.Diagnostics (Basel, Switzerland) · 2025Article
- Decoding of movement-related cortical potentials at different speeds.Cognitive neurodynamics · 2024Article
- An improved CapsNet based on data augmentation for driver vigilance estimation with forehead single-channel EEG.Cognitive neurodynamics · 2024Article
- Cognitive workload estimation using physiological measures: a review.Cognitive neurodynamics · 2024Article
- Research on Fatigue Driving Detection Technology Based on CA-ACGAN.Brain sciences · 2024Article
- Driving fatigue detection based on brain source activity and ARMA model.Medical & biological engineering & computing · 2024Article
- The applied principles of EEG analysis methods in neuroscience and clinical neurology.Military Medical Research · 2023Review
- Drivers' Mental Engagement Analysis Using Multi-Sensor Fusion Approaches Based on Deep Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Exploring deep residual network based features for automatic schizophrenia detection from EEG.Physical and engineering sciences in medicine · 2023Article
- Mental workload level assessment based on compounded hysteresis effect.Cognitive neurodynamics · 2023Article
- EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks.Frontiers in neuroscience · 2023Article
- Assigning channel weights using an attention mechanism: an EEG interpolation algorithm.Frontiers in neuroscience · 2023Article
- A Wide Dynamic Range Sigma-Delta Modulator for EEG Acquisition Using Randomized DWA and Dynamic-Modulated Scaling-Down Techniques.Sensors (Basel, Switzerland) · 2022Article
- EEG-Based Mental Tasks Recognition via a Deep Learning-Driven Anomaly Detector.Diagnostics (Basel, Switzerland) · 2022Article
- Smart Steering Sleeve (SSensors (Basel, Switzerland) · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Driver fatigue is attracting more and more attention, as it is the main cause of traffic accidents, which bring great harm to society and families. This paper proposes to use deep convolutional neural networks, and deep residual learning, to predict the mental states of drivers from electroencephalography (EEG) signals. Accordingly we have developed two mental state classification models called EEG-Conv and EEG-Conv-R. Tested on intra- and inter-subject, our results show that both models outperform the traditional LSTM- and SVM-based classifiers. Our major findings include (1) Both EEG-Conv and EEG-Conv-R yield very good classification performance for mental state prediction; (2) EEG-Conv-R is more suitable for inter-subject mental state prediction; (3) EEG-Conv-R converges more quickly than EEG-Conv. In summary, our proposed classifiers have better predictive power and are promising for application in practical brain-computer interaction .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.