ArticleSensors (Basel, Switzerland)2020
InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 26 citations in OpenAlex.
- Simultaneous detection of physical and mental fatigue using limited-channel EEG for practical workplace monitoring.Medical & biological engineering & computing · 2026Article
- An improved feature extraction algorithm for EEG-based driving fatigue recognition.Scientific reports · 2025Article
- Smart IoT-driven biosensors for EEG-based driving fatigue detection: A CNN-XGBoost model enhancing healthcare quality.BioImpacts : BI · 2025Article
- A regression method for EEG-based cross-dataset fatigue detection.Frontiers in physiology · 2023Article
- Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain-Computer Interface.Sensors (Basel, Switzerland) · 2022Article
- A Study on Seizure Detection of EEG Signals Represented in 2D.Sensors (Basel, Switzerland) · 2021Article
- An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
Abstract
Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the significant differences in EEG signals across subjects, and difficulty in collecting sufficient EEG samples for analysis during driving, detecting fatigue across subjects through using EEG signals remains a challenge. EasyTL is a kind of transfer-learning model, which has demonstrated better performance in the field of image recognition, but not yet been applied in cross-subject EEG-based applications. In this paper, we propose an improved EasyTL-based classifier, the InstanceEasyTL, to perform EEG-based analysis for cross-subject fatigue mental-state detection. Experimental results show that InstanceEasyTL not only requires less EEG data, but also obtains better performance in accuracy and robustness than EasyTL, as well as existing machine-learning models such as Support Vector Machine (SVM), Transfer Component Analysis (TCA), Geodesic Flow Kernel (GFK), and Domain-adversarial Neural Networks (DANN), etc.
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