eprintid: 17449
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/74/49
datestamp: 2023-12-19 03:23:50
lastmod: 2023-12-19 03:23:50
status_changed: 2023-12-19 03:08:04
type: article
metadata_visibility: show
creators_name: Alwasiti, H.
creators_name: Yusoff, M.Z.
title: Motor Imagery Classification for Brain Computer Interface using Deep Convolutional Neural Networks and Mixup Augmentation
ispublished: pub
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
note: cited By 0
abstract: <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&#x0025; Confidence Interval: 0.908, 0.933) and 0.933 (95&#x0025; 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
date: 2022
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141601510&doi=10.1109%2fOJEMB.2022.3220150&partnerID=40&md5=7008622fa0e2cf117d6d10a68fe70539
id_number: 10.1109/OJEMB.2022.3220150
full_text_status: none
publication: IEEE Open Journal of Vehicular Technology
pagerange: 1-8
refereed: TRUE
issn: 26441330
citation:   Alwasiti, H. and Yusoff, M.Z.  (2022) Motor Imagery Classification for Brain Computer Interface using Deep Convolutional Neural Networks and Mixup Augmentation.  IEEE Open Journal of Vehicular Technology.  pp. 1-8.  ISSN 26441330