QuadTPat: Quadruple Transition Pattern-based explainable feature engineering model for stress detection using EEG signals
Abstract The most cost-effective data collection method is electroencephalography (EEG), which obtains meaningful information about the brain. Therefore, EEG signal processing is crucial for neuroscience and machine learning (ML). Therefore, a new EEG stress dataset has been collected, and an explai...
| Main Authors: | , , , , , |
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| 格式: | Article |
| 語言: | English |
| 出版: |
Nature Portfolio
2024-11-01
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| 叢編: | Scientific Reports |
| 在線閱讀: | https://doi.org/10.1038/s41598-024-78222-8 |