%0 Journal Article
%@ 03029743
%A Xuan, T.Y.
%A Yahya, N.
%A Khan, Z.
%A Badruddin, N.
%A Yusoff, M.Z.
%D 2021
%F scholars:15839
%I Springer Science and Business Media Deutschland GmbH
%J Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
%K 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
%P 120-131
%R 10.1007/978-3-030-68449-5₁₃
%T EEG Motor Classification Using Multi-band Signal and Common Spatial Filter
%U https://khub.utp.edu.my/scholars/15839/
%V 12615 
%X 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.
%Z 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