@article{scholars17449,
            note = {cited By 0},
            year = {2022},
             doi = {10.1109/OJEMB.2022.3220150},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {IEEE Open Journal of Vehicular Technology},
           title = {Motor Imagery Classification for Brain Computer Interface using Deep Convolutional Neural Networks and Mixup Augmentation},
           pages = {1--8},
            issn = {26441330},
          author = {Alwasiti, H. and Yusoff, M. Z.},
        abstract = {{\ensuremath{<}}italic{\ensuremath{>}}Goal:{\ensuremath{<}}/italic{\ensuremath{>}} 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. {\ensuremath{<}}italic{\ensuremath{>}}Methods:{\ensuremath{<}}/italic{\ensuremath{>}} A customized Convolutional Neural Network with mixup augmentation was trained with {\ensuremath{<}}inline-formula{\ensuremath{>}}{\ensuremath{<}}tex-math notation="LaTeX"{\ensuremath{>}}{$\backslash$}scriptstyle {$\backslash$}mathtt {$\sim$} {\ensuremath{<}}/tex-math{\ensuremath{>}}{\ensuremath{<}}/inline-formula{\ensuremath{>}}120 EEG trials for only one subject per model. {\ensuremath{<}}italic{\ensuremath{>}}Results:{\ensuremath{<}}/italic{\ensuremath{>}} Modified ResNet18 and DenseNet121 models with mixup augmentation achieved 0.920 (95\&\#x0025; Confidence Interval: 0.908, 0.933) and 0.933 (95\&\#x0025; Confidence Interval: 0.922, 0.945) classification accuracy, respectively. {\ensuremath{<}}italic{\ensuremath{>}}Conclusions:{\ensuremath{<}}/italic{\ensuremath{>}} 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},
        keywords = {Biomedical engineering; Biomedical signal processing; Brain computer interface; Classification (of information); Convolution; Deep neural networks; Electrophysiology; Image classification; Interfaces (computer), BCI; Brain modeling; Classification performance; Confidence interval; Convolutional neural network; Deep learning; Spectrograms; Stockwell transform; Time-frequency Analysis; Training dataset, Electroencephalography},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141601510&doi=10.1109\%2fOJEMB.2022.3220150&partnerID=40&md5=7008622fa0e2cf117d6d10a68fe70539}
}