Data augmentation for AI classification in EEG-based motor imagery
Electroencephalography (EEG) classification for motor imagery can be challenging with consumer-grade devices such as the Emotiv headset, where data quality and sample size are limited. In this study, nine different augmentation methods (Rotation, Mixup, Random Time Shift, Frequency-Domain Augmentati...
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| 格式: | Thesis-Master by Coursework |
| 語言: | English |
| 出版: |
Nanyang Technological University
2025
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/183540 |
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