Classification of finger movements through optimal EEG channel and feature selection
Experimental results demonstrate that despite the high-class number, the proposed approach obtained a modest yet considerable advancement in finger movement prediction when the results are compared to the results of similar studies. Additionally, for almost all feature sets, the statistical significance-based feature reduction method improves the prediction performance in the most of classifiers, contributing elaborate EEG channel and feature analysis. Nonetheless, in this study, we used an EEG dataset recorded from only 13 healthy subjects; therefore, a dataset covering more subjects is necessary to reach a more general conclusion.
Authors
- Yılmaz Kemal Yüce (ORCID: https://orcid.org/0000-0003-4023-0401)
- Mürşide Değirmenci (ORCID: https://orcid.org/0000-0003-0978-9653)
- Matjaž Perc (ORCID: https://orcid.org/0000-0002-3087-541X)
- Yalçın İşler (ORCID: https://orcid.org/0000-0002-2150-4756)
Institutions
- Kyung Hee University (KR)
- University of Maribor (SI)
- Complexity Science Hub (AT)
- Alanya University (TR)
- Universidad CES (CO)
- University Clinical Centre Maribor (SI)
- Korea University (JP)
- Alanya Alaaddin Keykubat Üniversitesi (TR)
- Kutahya Saglik Bilimleri Universitesi
Publication Details
- Journal
- Frontiers in Human Neuroscience
- Published
- 2025-07-16
- DOI
- https://doi.org/10.3389/fnhum.2025.1633910
- Citations
- 2
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 2.86
Funders
- Javna Agencija za Raziskovalno Dejavnost RS
- Dokuz Eylül Üniversitesi