EEG channel and feature investigation in binary and multiple motor imagery task predictions

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dc.authorid0000-0003-0978-9653
dc.authorid0000-0002-3087-541X
dc.contributor.authorDegirmenci, Murside
dc.contributor.authorYuce, Yilmaz Kemal
dc.contributor.authorPerc, Matjaz
dc.contributor.authorIsler, Yalcin
dc.date.accessioned2026-01-24T12:29:27Z
dc.date.available2026-01-24T12:29:27Z
dc.date.issued2024
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractIntroduction Motor Imagery (MI) Electroencephalography (EEG) signals are non-stationary and dynamic physiological signals which have low signal-to-noise ratio. Hence, it is difficult to achieve high classification accuracy. Although various machine learning methods have already proven useful to that effect, the use of many features and ineffective EEG channels often leads to a complex structure of classifier algorithms. State-of-the-art studies were interested in improving classification performance with complex feature extraction and classification methods by neglecting detailed EEG channel and feature investigation in predicting MI tasks from EEGs. Here, we investigate the effects of the statistically significant feature selection method on four different feature domains (time-domain, frequency-domain, time-frequency domain, and non-linear domain) and their two different combinations to reduce the number of features and classify MI-EEG features by comparing low-dimensional matrices with well-known machine learning algorithms.Methods Our main goal is not to find the best classifier performance but to perform feature and channel investigation in MI task classification. Therefore, the detailed investigation of the effect of EEG channels and features is implemented using a statistically significant feature distribution on 22 EEG channels for each feature set separately. We used the BCI Competition IV Dataset IIa and 288 samples per person. A total of 1,364 MI-EEG features were analyzed in this study. We tested nine distinct classifiers: Decision tree, Discriminant analysis, Logistic regression, Naive Bayes, Support vector machine, k-Nearest neighbor, Ensemble learning, Neural networks, and Kernel approximation.Results Among all feature sets considered, classifications performed with non-linear and combined feature sets resulted in a maximum accuracy of 63.04% and 47.36% for binary and multiple MI task predictions, respectively. The ensemble learning classifier achieved the maximum accuracy in almost all feature sets for binary and multiple MI task classifications.Discussion Our research thus shows that the statistically significant feature-based feature selection method significantly improves the classification performance with fewer features in almost all feature sets, enabling detailed and effective EEG channel and feature investigation.
dc.description.sponsorshipSlovenian Research and Innovation Agency (Javna agencija za znanstvenoraziskovalno in inovacijskodejavnost Repub like Slovenije) [P1-0403, N1-0232]; Izmir Katip Celebi University Scientific Research Council Agency [2023-TDR-FEBE-0002]; Education Institution Research Fellowship under the 100/2000 Higher Education Institution Ph.D. Scholarship; Scientific and Technological Research Council of Turkey (TUBITAK)
dc.description.sponsorshipThe author(s) declare financial support was received for the research, authorship, and/or publication of this article. Matjaz Perc was supported by the Slovenian Research and Innovation Agency (Javna agencija za znanstvenoraziskovalno in inovacijskodejavnost Repub like Slovenije) (Grant Nos. P1-0403 and N1-0232). This study was also supported by Izmir Katip Celebi University Scientific Research Council Agency (Project Number 2023-TDR-FEBE-0002) for Murside Degirmenci's doctoral thes is studies. In addition, Murside Degirmenci holds a research fellowship from the Higher Education Institution Research Fellowship under the 100/2000 Higher Education Institution Ph.D. Scholarship and the 2211A General Doctoral Scholarship from the Scientific and Technological Research Council of Turkey (TUBITAK).
dc.identifier.doi10.3389/fnhum.2024.1525139
dc.identifier.issn1662-5161
dc.identifier.pmid39741784
dc.identifier.scopus2-s2.0-85213512950
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3389/fnhum.2024.1525139
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5382
dc.identifier.volume18
dc.identifier.wosWOS:001386130500001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherFrontiers Media Sa
dc.relation.ispartofFrontiers in Human Neuroscience
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260121
dc.subjectbrain-computer interface
dc.subjectelectroencephalogram
dc.subjectfeature and channel investigation
dc.subjectfeature selection
dc.subjectmachine learning
dc.subjectmotor imagery task classification
dc.titleEEG channel and feature investigation in binary and multiple motor imagery task predictions
dc.typeArticle

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