ArticleSensors (Basel, Switzerland)2021
An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 67 citations in OpenAlex.
- Dual-Stream Fusion of Eye-Tracking and ECG Signals for Fatigue Detection in Remote Tower Air Traffic Controllers.Bioengineering (Basel, Switzerland) · 2026Article
- Generative Adversarial Networks for Modeling Bio-Electric Fields in Medicine: A Review of EEG, ECG, EMG, and EOG Applications.Bioengineering (Basel, Switzerland) · 2026Review
- A Multidimensional Benchmark of Public EEG Datasets for Driver State Monitoring in Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2025Review
- ASFT-Transformer: A Fast and Accurate Framework for EEG-Based Pilot Fatigue Recognition.Sensors (Basel, Switzerland) · 2025Article
- Recent Advances in Portable Dry Electrode EEG: Architecture and Applications in Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2025Review
- A Comprehensive Review of Unobtrusive Biosensing in Intelligent Vehicles: Sensors, Algorithms, and Integration Challenges.Bioengineering (Basel, Switzerland) · 2025Review
- Smart IoT-driven biosensors for EEG-based driving fatigue detection: A CNN-XGBoost model enhancing healthcare quality.BioImpacts : BI · 2025Article
- Effects of Mental Workload Manipulation on Electroencephalography Spectrum Oscillation and Microstates in Multitasking Environments.Brain and behavior · 2025Article
- Brain-computer Interaction in the Smart Era.Current medical science · 2024Review
- A cross-attention swin transformer network for EEG-based subject-independent cognitive load assessment.Cognitive neurodynamics · 2024Article
- Detecting cognitive traits and occupational proficiency using EEG and statistical inference.Scientific reports · 2024Article
- EEG multi-domain feature transfer based on sparse regularized Tucker decomposition.Cognitive neurodynamics · 2024Article
- An evaluation of transfer learning models in EEG-based authentication.Brain informatics · 2023Article
- Generative adversarial networks in EEG analysis: an overview.Journal of neuroengineering and rehabilitation · 2023Review
- Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications.Scientific data · 2023Article
- Mental Fatigue Degree Recognition Based on Relative Band Power and Fuzzy Entropy of EEG.International journal of environmental research and public health · 2023Article
- Attention-based multi-semantic dynamical graph convolutional network for eeg-based fatigue detection.Frontiers in neuroscience · 2023Article
- A regression method for EEG-based cross-dataset fatigue detection.Frontiers in physiology · 2023Article
- EEG-FCV: An EEG-Based Functional Connectivity Visualization Framework for Cognitive State Evaluation.Frontiers in psychiatry · 2022Article
- Cognitive effects of prolonged continuous human-machine interaction: The case for mental state-based adaptive interfaces.Frontiers in neuroergonomics · 2022Review
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 3 countries.
Funding
Abstract
Fatigued driving is one of the main causes of traffic accidents. The electroencephalogram (EEG)-based mental state analysis method is an effective and objective way of detecting fatigue. However, as EEG shows significant differences across subjects, effectively "transfering" the EEG analysis model of the existing subjects to the EEG signals of other subjects is still a challenge. Domain-Adversarial Neural Network (DANN) has excellent performance in transfer learning, especially in the fields of document analysis and image recognition, but has not been applied directly in EEG-based cross-subject fatigue detection. In this paper, we present a DANN-based model, Generative-DANN (GDANN), which combines Generative Adversarial Networks (GAN) to enhance the ability by addressing the issue of different distribution of EEG across subjects. The comparative results show that in the analysis of cross-subject tasks, GDANN has a higher average accuracy of 91.63% in fatigue detection across subjects than those of traditional classification models, which is expected to have much broader application prospects in practical brain-computer interaction (BCI).
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Registered trials
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.