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EEG Motor Classification Using Multi-band Signal and Common Spatial Filter

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Intelligent Human Computer Interaction (IHCI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12615))

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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 \(\alpha \), \(\beta \), \(\varDelta \) and \(\theta \) 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.

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Acknowledgment

This research is supported by two research grants: (1) by the Ministry of Education Malaysia under Higher Institutional Centre of Excellence (HICoE) Scheme awarded to Centre on Intelligent Signal and Imaging Research (CISIR) under grant number 015MA0-050(6), and (2) by the Yayasan Universiti Teknologi Petronas under grant number YUTP-FRG 015LC0-031.

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Authors and Affiliations

  1. Intel Malaysia Sdn Bhd, Bayan Lepas, Malaysia

    Tan Yu Xuan

  2. Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS (UTP), Seri Iskandar, Malaysia

    Norashikin Yahya, Zia Khan, Nasreen Badruddin & Mohd Zuki Yusoff

Authors
  1. Tan Yu Xuan
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  2. Norashikin Yahya
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  3. Zia Khan
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  4. Nasreen Badruddin
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  5. Mohd Zuki Yusoff
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Corresponding author

Correspondence to Norashikin Yahya .

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Editors and Affiliations

  1. Woosong University, Daejeon, Korea (Republic of)

    Madhusudan Singh

  2. Dongseo University, Busan, Korea (Republic of)

    Dae-Ki Kang

  3. Keimyung University, Daegu, Korea (Republic of)

    Jong-Ha Lee

  4. Indian Institute of Information Technoloy, Allahabad, India

    Uma Shanker Tiwary

  5. Hankuk University of Foreign Studies, Yongin, Korea (Republic of)

    Dhananjay Singh

  6. Pukyong National University, Busan, Korea (Republic of)

    Wan-Young Chung

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Xuan, T.Y., Yahya, N., Khan, Z., Badruddin, N., Yusoff, M.Z. (2021). EEG Motor Classification Using Multi-band Signal and Common Spatial Filter. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12615. Springer, Cham. https://doi.org/10.1007/978-3-030-68449-5_13

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  • DOI: https://doi.org/10.1007/978-3-030-68449-5_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68448-8

  • Online ISBN: 978-3-030-68449-5

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