eprintid: 17403
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/74/03
datestamp: 2023-12-19 03:23:48
lastmod: 2023-12-19 03:23:48
status_changed: 2023-12-19 03:07:59
type: article
metadata_visibility: show
creators_name: Awais, M.A.
creators_name: Yusoff, M.Z.
creators_name: Yahya, N.
title: Classification of Sub-frequency Bands Based Two-Class Motor Imagery Using CNN
ispublished: pub
keywords: Brain computer interface; Clinical research; Deep learning, BCI; Beta rhythms; Clinical application; Frequency ranges; Frequency sub band; In-depth knowledge; Motor imagery; MU rhythm; PhysioNet; Research applications, Image classification
note: cited By 0; Conference of 1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 ; Conference Date: 17 December 2020 Through 18 December 2020; Conference Code:286319
abstract: EEG has been primarily used in both clinical and research applications. Brain-computer system (BCI) is one of the leading EEG research applications that offer special users a new means of communication. Previous studies have reported the occurrence of MI patterns in mu and beta rhythms, but that does not provide in-depth knowledge of the frequency range. This paper focuses on the classification of 2-class Motor Imagery using several frequency sub-bands in the mu and beta range. �EEG motor imagery dataset from the Physionet database,� has been used for validation purposes. Although this data includes both imagery and real movements, we have just used the imagination data. Data is collected from 109 healthy subjects, but we have only used the first 15 subjects in the study. The study aims to divide the data into multiple frequency bands to study the motor imagery classification behaviour over different frequencies. Afterward, a CNN-based deep learning model with two convolutional layers has been used to classify the left and right classes for different types of same data. The study seeks to compare the results from various sub-frequency bands. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
date: 2022
publisher: Springer Science and Business Media Deutschland GmbH
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142699257&doi=10.1007%2f978-981-16-2183-3_80&partnerID=40&md5=0d4f65a83b38f0f1c6ab741072444167
id_number: 10.1007/978-981-16-2183-3₈₀
full_text_status: none
publication: Lecture Notes in Electrical Engineering
volume: 758
pagerange: 851-857
refereed: TRUE
isbn: 9789811621826
issn: 18761100
citation:   Awais, M.A. and Yusoff, M.Z. and Yahya, N.  (2022) Classification of Sub-frequency Bands Based Two-Class Motor Imagery Using CNN.  Lecture Notes in Electrical Engineering, 758.  pp. 851-857.  ISSN 18761100