MOTOR IMAGERY CLASSIFICATION FOR BCI USING STOCKWELL TRANSFORM, DEEP METRIC LEARNING, AND DCNN WITH MIXUP AUGMENTATION
Inter-individual EEG variability is a major issue limiting the performance of Brain-Computer Interface (BCI) classifiers. However, most previous deep learning (DL) models are still using the dataset of multiple subjects to train a single model due to the limited augmentation techniques available...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://utpedia.utp.edu.my/20726/3/Haider%20Alwasiti_G02457.pdf http://utpedia.utp.edu.my/20726/ |
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| Institution: | Universiti Teknologi Petronas |
| Language: | English |