Classification of Finger Movements Through Optimal EEG Channel and Feature Selection

Murside Degirmenci¹Yılmaz Kemal Yüce²Matjaž Perc^3,4,5*Yalcin Isler^6*

• ¹Kutahya Vocational School, Kutahya Saglik Bilimleri Universitesi, Ktahya, Türkiye
• ²Computer Engineering, Alanya Alaaddin Keykubat Universitesi, Alanya, Türkiye
• ³Univerza v Mariboru, Maribor, Slovenia
• ⁴Kyung Hee University, Dongdaemungu, Republic of Korea
• ⁵Korea University, Seongbukgu, Republic of Korea
• ⁶Electrical and Electronics Engineering, Alanya Alaaddin Keykubat University, Antalya, Türkiye

Introduction: Electrencephalography (EEG)-based brain-computer interfaces (BCIs) have become popular as EEG is accepted as the simplest and non-invasive neuroimaging modality to record the brain's electrical activity. In the current BCI research context, apart from predicting extremity movements, recent BCI studies have been interested in accurately predicting finger movements of the same hand using different pattern recognition methods over EEG data collected based on motor imagery (MI), through which a mental image of the desired action is generated when a person ideally simulates or imagines carrying out a certain motor task. Methods: Our major goal is not to explore the best machine algorithm performance, but to investigate the best EEG channels and features that can be used in the classification of finger movements. Hence, the comprehensive analysis of the effectiveness of EEG channels and features is performed utilizing a statistically significant feature distribution over 19 EEG channels for each feature set independently. A bulky dataset of electroencephalographic MI for EEG-based BCIs is used in this study. A total of 1102 EEG features supplied from different feature domains have been investigated. Subsequently, these features were tested with well-known classifiers.Results: For subject-dependent analysis, the maximum accuracy of 59.17% was obtained using the EEG features that were selected the most and all EEG channels by the Support vector machine algorithm. For subject-independent analysis, the maximum accuracy of 39.30% was obtained using the mostly selected EEG features by the Support vector machine classifier.Discussion: 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.