eprintid: 15839
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/58/39
datestamp: 2023-11-10 03:30:28
lastmod: 2023-11-10 03:30:28
status_changed: 2023-11-10 02:00:32
type: article
metadata_visibility: show
creators_name: Xuan, T.Y.
creators_name: Yahya, N.
creators_name: Khan, Z.
creators_name: Badruddin, N.
creators_name: Yusoff, M.Z.
title: EEG Motor Classification Using Multi-band Signal and Common Spatial Filter
ispublished: pub
keywords: Beamforming; Brain; Brain computer interface; Electroencephalography; Electrophysiology; Human computer interaction; Wavelet transforms, Classification accuracy; Classification algorithm; Classification framework; Common spatial patterns; Continuous wavelet transforms; Discriminative power; Multi- band signals; Prosthetic devices, Biomedical signal processing
note: cited By 0; Conference of 12th International Conference on Intelligent Human Computer Interaction, IHCI 2020 ; Conference Date: 24 November 2020 Through 26 November 2020; Conference Code:255179
abstract: Electroencephalography (EEG) signal is one of the popular approaches for analyzing the relationship between motor movement and the brain activity. This is mainly driven by the rapid development of Brain-Computer-Interface BCI devices for applications like prosthetic devices, using EEG as its input signal. The EEG is known to be highly affected by artefact and with more motor events, this may result in low classification accuracy. In this paper, classification of 3-class hand motor EEG signals, performing grasping, lifting and holding using Common Spatial Pattern (CSP) and pre-trained CNN is investigated. Thirteen electrodes capturing signals related to motor movement, C3, Cz, C4, T3, T4, F7, F3, Fz, F4, F8, P3, Pz and P4 are utilized and signal from α, β, � and θ bands are selected in the pre-processing stage. CSP filters utilizing the scheme of pair-wise are used to increase the discriminative power between two classes whereby the signals extracted by the CSP filter are converted into scalograms by utilizing Continuous Wavelet Transform (CWT). The accuracy of the proposed multi-band and CSP based classification algorithm tested using DenseNet giving average accuracy values of 97.3, 93.8 and 100, for GS, LT and HD movements, respectively. These results indicate that the classification framework using CSP filters and pre-trained CNN can provide a good solution in decoding hand motor movement from EEG signals. © 2021, Springer Nature Switzerland AG.
date: 2021
publisher: Springer Science and Business Media Deutschland GmbH
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102237694&doi=10.1007%2f978-3-030-68449-5_13&partnerID=40&md5=b10716a78dd597b319cfe3a624df028f
id_number: 10.1007/978-3-030-68449-5₁₃
full_text_status: none
publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
volume: 12615 
pagerange: 120-131
refereed: TRUE
isbn: 9783030684488
issn: 03029743
citation:   Xuan, T.Y. and Yahya, N. and Khan, Z. and Badruddin, N. and Yusoff, M.Z.  (2021) EEG Motor Classification Using Multi-band Signal and Common Spatial Filter.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12615 .  pp. 120-131.  ISSN 03029743