@article{scholars17306,
            year = {2022},
           title = {Motor Imagery Classification for Brain Computer Interface Using Deep Convolutional Neural Networks and Mixup Augmentation},
             doi = {10.1109/OJEMB.2022.3220150},
           pages = {171--177},
         journal = {IEEE Open Journal of Engineering in Medicine and Biology},
          volume = {3},
            note = {cited By 0},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
          author = {Alwasiti, H. and Yusoff, M. Z.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146682515&doi=10.1109\%2fOJEMB.2022.3220150&partnerID=40&md5=ab94ac1ff84b9e57883ebd4177717508},
        abstract = {Goal: 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. Methods: A customized Convolutional Neural Network with mixup augmentation was trained with {\^a}?1/4120 EEG trials for only one subject per model. Results: 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. Conclusions: 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. {\^A}{\copyright} 2022 IEEE.},
            issn = {26441276},
        keywords = {Brain computer interface; Classification (of information); Convolution; Deep neural networks; Image classification, BCI; Classification performance; Confidence interval; Convolutional neural network; Data collection; Deep learning; Motor imagery classification; Stockwell transform; Training dataset; Training sets, Convolutional neural networks}
}