Proceedings of the Multimedia University Engineering Conference (MECON 2022)

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Partial Directed Coherence for the Classification of Motor Imagery-Based Brain-Computer Interface

Authors
Muhammad Ahsan Awais1, Mohd Zuki Yusoff1, *
1Centre for Intelligent Signal and Imaging Research (CISIR), Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
*Corresponding author. Email: mzuki_yusoff@utp.edu.my
Corresponding Author
Mohd Zuki Yusoff
Available Online 23 December 2022.
DOI
10.2991/978-94-6463-082-4_13How to use a DOI?
Keywords
Brain-Computer Interface; Motor Imagery; Electroencephalogram; Feature Extraction; Classification; Partial directed coherence; Brain connectivity
Abstract

In recent years, the research community around the globe has contributed significantly to improve the brain-computer interface based assistive technologies. Electroencephalographic brain-computer interface enables the person to communicate with the outside world by creating an advanced communication protocol between the brain and the computer. Motor imagery-based BCIs aim to predict the specific patterns elicited by imagining some planned movements. Standard BCI systems incorporate the use of spatial features from the motor cortex. However, several researchers claim to have the intercommunication of different brain regions during the motor task. Thus, a unique approach like brain connectivity is essential to extract the intercommunication of brain regions through several electrode channels during a MI task. In this work, brain effective connectivity has been estimated using partial directed coherence, and it has been used as the feature extraction method. An extensive 2-class motor imagery dataset from Physionet database incorporating 91 subjects has been used for the validation purposes. Our proposed work reached the average classification accuracy of 97.45% using an SVM classifier. The findings of this study revealed the significance of brain connectivity features over the conventional features extracted from a single brain region.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Multimedia University Engineering Conference (MECON 2022)
Series
Advances in Engineering Research
Publication Date
23 December 2022
ISBN
978-94-6463-082-4
ISSN
2352-5401
DOI
10.2991/978-94-6463-082-4_13How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Muhammad Ahsan Awais
AU  - Mohd Zuki Yusoff
PY  - 2022
DA  - 2022/12/23
TI  - Partial Directed Coherence for the Classification of Motor Imagery-Based Brain-Computer Interface
BT  - Proceedings of the Multimedia University Engineering Conference (MECON 2022)
PB  - Atlantis Press
SP  - 121
EP  - 131
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-082-4_13
DO  - 10.2991/978-94-6463-082-4_13
ID  - Awais2022
ER  -