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Affiliation

CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, Guangdong, China

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1 Papers

Investigation of Artifact Contamination Impact on EEG Oscillations Towards Enhanced Motor Function Characterization

Topics: Medical Signal Acquisition, Analysis and Processing; Pattern Recognition & Machine Learning for Biosignal Data

Authors: Mojisola Asogbon ^{1

;

2} , Oluwarotimi Samuel ^{1

;

2} , Farid Meziane ^{1

;

2} , Guanglin Li ³ and Yongcheng Li ³

Affiliations: ¹ School of Computing, University of Derby, Derby, DE22 3AW, U.K. , ² Data Science Research Centre, University of Derby, Derby DE22 3AW, U.K. , ³ CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, Guangdong, China

Digital Object Identifier: 10.5220/0012373400003657

Proceedings of BIOSIGNALS 2024, ISBN: 978-989-758-688-0

Quick Abstract

The significant advancements in electroencephalography (EEG)-driven technology have led to its widespread use in assessing stroke-related conditions. Over the years, various studies have explored the potential of EEG oscillatory patterns in neurological research, with several of them giving limited attention to the signal processing techniques employed, precluding a proper understanding of EEG oscillatory patterns under various conditions. To resolve this issue, we systematically investigated how artifacts impact EEG oscillatory rhythms associated with upper limb movement-related tasks. Thus, the EEG signals of motor tasks were acquired non-invasively from healthy subjects and processed using automated artifact-attenuation methods. Subsequently, the Mu and Beta bands in the brain’s motor cortex region were extracted through time-frequency analysis and analyzed using relevant metrics. Experimental results revealed that artifacts in EEG would substantially influence the brain activation strength and response during motor tasks. Notably, signals preprocessed with Reduction of Electroencephalographic Artifacts based on Multi Wiener Filter and Enhanced Wavelet Independent Component Analysis (RELAX_MWF_wICA) showed better brain responses and high task classification performance compared to other methods and the raw signal across motor tasks. This study’s findings revealed that the choice of signal processing technique is crucial, as it would influence its analysis and interpretation, thus highlighting the need for careful consideration and usage.