The Analysis and Comparison of LMS-Based Filtering Techniques for EEG Signals: Towards Informed Decision Making

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 8

Year of Publication : 2024

Authors : Garlet Llaza Huamani, Bryan Sanchez Urure, Estefany Huaman Colque, Jesús Talavera S.

10.14445/23488549/IJECE-V11I8P113

How to Cite?

Garlet Llaza Huamani, Bryan Sanchez Urure, Estefany Huaman Colque, Jesús Talavera S., "The Analysis and Comparison of LMS-Based Filtering Techniques for EEG Signals: Towards Informed Decision Making," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 8, pp. 123-130, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I8P113

Abstract:

Electroencephalography (EEG) is essential for observing brain activity. It offers non-invasive, high-resolution insights into neural dynamics. Despite its clinical and research applications, EEG signals are prone to noise from powerline interference, muscle artifacts and environmental sources. This study evaluates adaptive filtering techniques—LMS, NLMS, PNLMS and IPNLMS—for denoising EEG signals. A dataset of 23 EEG recordings contaminated with noise was used. Accelerometer signals served as reference inputs. The algorithms were assessed using Mean Squared Error (MSE) Signal-toNoise Ratio (SNR) and Pearson Correlation Coefficient. PNLMS was found to be the most effective. It achieved the lowest MSE (0.9193), highest SNR (1.0768) and highest correlation (46.9025%). While PNLMS excels in noise reduction it has computational demands that may limit its use in wearable devices. NLMS offers a practical balance. It balances performance and efficiency. Future work includes hybrid algorithms. Real-time implementations will be addressed. Adaptive parameter tuning will also be covered. These aim to enhance EEG signal processing and its applications in clinical and research environments.

Keywords:

EEG signal, LMS filtering, Motion artifact, NLMS, PNLMS, IPNLMS.

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