Correlation-Based Adaptive Filtering Performance Driven by Signal Decomposition Methods for EEG Signals of Individuals with Severe Motor Disabilities

Esra Kaya; Ismail Sarıtas

doi:10.17798/bitlisfen.1726313

Correlation-Based Adaptive Filtering Performance Driven by Signal Decomposition Methods for EEG Signals of Individuals with Severe Motor Disabilities

Abstract

Electroencephalogram (EEG) signals are obtained from the surface of the scalp through a Brain-Computer Interface (BCI) and show the activity of brain regions. EEG signals are useful tools for people, especially those who have severe motor disabilities for an improved quality of life. Because of their noisy nature caused by a small movement of the head, eye blink, or even breathing, it is extremely hard to extract meaningful information from EEG signals. Thus, it is imperative to filter EEG signals without losing their essential parts. While filtering, the concept of adaptivity comes in handy because of its flexible nature to preserve important information. In this context, a novel approach that applies Wavelet Decomposition (WD), Empirical Mode Decomposition (EMD), or Variational Mode Decomposition (VMD) methods to filter EEG signals adaptively based on correlation was proposed. The performances of developed methods were calculated based on the accuracies of binary classifications of five different labels by using machine learning classifiers and compared with an Elliptic Bandpass Filter. The study shows that the proposed adaptively implemented VMD is the most useful filtering method for EEG signals, providing the best performance with 76.1% subject-wise accuracy for binary classification of word association and feet motor imagery and an average accuracy of 70.4% for all binary classifications using a Support Vector Machine (SVM) classifier. The results show that correlation is an efficient tool to adaptively implement signal decomposition methods as filters by preserving meaningful information more successfully than an Elliptic Bandpass Filter.

Keywords

Ethical Statement

The authors declare that this study complies with Research and Publication Ethics.

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Details

Primary Language

English

Subjects

Bioinformatic Methods Development, Neural Engineering

Journal Section

Research Article

Authors

Esra Kaya ^*
0000-0003-1401-9071
Türkiye

Ismail Sarıtas
0000-0002-5743-4593
Türkiye

Publication Date

March 24, 2026

Submission Date

June 24, 2025

Acceptance Date

February 3, 2026

Published in Issue

Year 2026 Volume: 15 Number: 1

DOI

https://doi.org/10.17798/bitlisfen.1726313

IZ

https://izlik.org/JA57SL44LC

Cite

RIS / Bibtex

APA

Kaya, E., & Sarıtas, I. (2026). Correlation-Based Adaptive Filtering Performance Driven by Signal Decomposition Methods for EEG Signals of Individuals with Severe Motor Disabilities. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 15(1), 13-29. https://doi.org/10.17798/bitlisfen.1726313

AMA

1.Kaya E, Sarıtas I. Correlation-Based Adaptive Filtering Performance Driven by Signal Decomposition Methods for EEG Signals of Individuals with Severe Motor Disabilities. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026;15(1):13-29. doi:10.17798/bitlisfen.1726313

Chicago

Kaya, Esra, and Ismail Sarıtas. 2026. “Correlation-Based Adaptive Filtering Performance Driven by Signal Decomposition Methods for EEG Signals of Individuals With Severe Motor Disabilities”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15 (1): 13-29. https://doi.org/10.17798/bitlisfen.1726313.

EndNote

Kaya E, Sarıtas I (March 1, 2026) Correlation-Based Adaptive Filtering Performance Driven by Signal Decomposition Methods for EEG Signals of Individuals with Severe Motor Disabilities. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15 1 13–29.

IEEE

[1]E. Kaya and I. Sarıtas, “Correlation-Based Adaptive Filtering Performance Driven by Signal Decomposition Methods for EEG Signals of Individuals with Severe Motor Disabilities”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 15, no. 1, pp. 13–29, Mar. 2026, doi: 10.17798/bitlisfen.1726313.

ISNAD

Kaya, Esra - Sarıtas, Ismail. “Correlation-Based Adaptive Filtering Performance Driven by Signal Decomposition Methods for EEG Signals of Individuals With Severe Motor Disabilities”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15/1 (March 1, 2026): 13-29. https://doi.org/10.17798/bitlisfen.1726313.

JAMA

1.Kaya E, Sarıtas I. Correlation-Based Adaptive Filtering Performance Driven by Signal Decomposition Methods for EEG Signals of Individuals with Severe Motor Disabilities. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026;15:13–29.

MLA

Kaya, Esra, and Ismail Sarıtas. “Correlation-Based Adaptive Filtering Performance Driven by Signal Decomposition Methods for EEG Signals of Individuals With Severe Motor Disabilities”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 15, no. 1, Mar. 2026, pp. 13-29, doi:10.17798/bitlisfen.1726313.

Vancouver

1.Esra Kaya, Ismail Sarıtas. Correlation-Based Adaptive Filtering Performance Driven by Signal Decomposition Methods for EEG Signals of Individuals with Severe Motor Disabilities. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026 Mar. 1;15(1):13-29. doi:10.17798/bitlisfen.1726313