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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| 主要作者: | |
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| 格式: | Thesis |
| 語言: | English |
| 出版: |
2021
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| 主題: | |
| 在線閱讀: | http://utpedia.utp.edu.my/20726/3/Haider%20Alwasiti_G02457.pdf http://utpedia.utp.edu.my/20726/ |
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| 機構: | Universiti Teknologi Petronas |
| 語言: | English |
| 總結: | 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 for EEG signals, and the difficulty of
collecting large EEG datasets from each subject. Building a DL model that can be
trained on an extremely small EEG training set of a single subject presents an
interesting challenge that this work is trying to address. The deep metric learning
(DML) model is known for the ability to converge on a small dataset, however, this
kind of model has not been studied yet on BCI EEG signals. |
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