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A Multimodal Feature Fusion Brain Fatigue Recognition System Based on Bayes-gcForest
You Zhou1, Pukun Chen2,3, Yifan Fan1
1College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel, Switzerland)
|May 11, 2024
Summary
This study presents a new system for detecting brain fatigue using electroencephalogram (EEG) and electrocardiogram (ECG) signals. The method achieves high accuracy, offering a cost-effective solution for monitoring cognitive load.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Data Science
Background:
- Brain fatigue significantly impacts health and productivity in modern society.
- Current methods for brain fatigue detection lack precision, cost-effectiveness, and user-friendliness.
- There is a need for efficient and accessible tools to monitor cognitive states.
Purpose of the Study:
- To develop a portable, cost-effective system for real-time physiological signal collection and analysis.
- To enhance the precision and efficiency of brain fatigue recognition.
- To broaden the application scope of brain fatigue monitoring.
Main Methods:
- Collected and analyzed electroencephalogram (EEG) and electrocardiogram (ECG) data from 20 subjects.
- Constructed a compact dataset incorporating multi-modal physiological signals.
- Employed a Bayesian-optimized multi-granularity cascade forest (Bayes-gcForest) model for fatigue state recognition.
Main Results:
- Achieved high recognition rates for brain fatigue: 95.71% on the DROZY dataset and 96.13% on the constructed dataset.
- Demonstrated the effectiveness of a multi-modal feature fusion model.
- Validated the system's capability for precise and efficient fatigue monitoring.
Conclusions:
- The developed system provides a viable, cost-effective solution for brain fatigue monitoring.
- The multi-modal approach enhances the accuracy of fatigue detection.
- Offers theoretical support for designing effective rest systems for researchers and professionals.
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