Motor Imagery Classification for Brain Computer Interface using Deep Convolutional Neural Networks and Mixup Augmentation
<italic>Goal:</italic> Building a DL model that can be trained on small EEG training set of a single subject presents an interesting challenge that this work is trying to address. In particular, this study is trying to avoid the need for long EEG data collection sessions, and without com...
Saved in:
| Main Authors: | , |
|---|---|
| 格式: | Article |
| 出版: |
Institute of Electrical and Electronics Engineers Inc.
2022
|
| 在线阅读: | http://scholars.utp.edu.my/id/eprint/33889/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141601510&doi=10.1109%2fOJEMB.2022.3220150&partnerID=40&md5=7008622fa0e2cf117d6d10a68fe70539 |
| 标签: |
添加标签
没有标签, 成为第一个标记此记录!
|
| 总结: | <italic>Goal:</italic> Building a DL model that can be trained on small EEG training set of a single subject presents an interesting challenge that this work is trying to address. In particular, this study is trying to avoid the need for long EEG data collection sessions, and without combining multiple subjects training datasets, which has a detrimental effect on the classification performance due to the inter-individual variability among subjects. <italic>Methods:</italic> A customized Convolutional Neural Network with mixup augmentation was trained with <inline-formula><tex-math notation="LaTeX">\scriptstyle \mathtt ∼ </tex-math></inline-formula>120 EEG trials for only one subject per model. <italic>Results:</italic> Modified ResNet18 and DenseNet121 models with mixup augmentation achieved 0.920 (95% Confidence Interval: 0.908, 0.933) and 0.933 (95% Confidence Interval: 0.922, 0.945) classification accuracy, respectively. <italic>Conclusions:</italic> We show that the designed classifiers resulted in a higher classification performance in comparison to other DL classifiers of previous studies on the same dataset, despite the limited training dataset used in this work. Author |
|---|