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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Bibliographic Details

Main Author:	ALWASITI, HAIDER
Format:	Thesis
Language:	en
Published:	2021
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://utpedia.utp.edu.my/id/eprint/20726/3/Haider%20Alwasiti_G02457.pdf http://utpedia.utp.edu.my/id/eprint/20726/
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