EEG-based motor execution classification of upper and lower extremities using machine learning

---

Korkmaz I., Tepe C.

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING, 2025 (SCI-Expanded, Scopus)

• Yayın Türü: Makale / Tam Makale
• Basım Tarihi: 2025
• Doi Numarası: 10.1080/10255842.2025.2566260
• Dergi Adı: COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING
• Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Compendex, EMBASE, INSPEC, MEDLINE
• Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

This study classifies upper- and lower-extremity motor execution from electroencephalography (EEG). We compared two feature extractors, statistical features and Common Spatial Patterns (CSP), and four classifiers: K-Nearest Neighbors, Linear Discriminant Analysis (LDA), Multilayer Perceptron, and Support Vector Machine. Metrics were accuracy, F1, precision, and recall. CSP with LDA achieved the best, most consistent performance (72.5% accuracy); statistical features underperformed. We report real-time feasibility benchmarks, post-cue time-window analysis, and significance tests for classifiers. Findings support BCI and neuroprosthesis development, while noting subject variability and dataset specificity. Future work is real-time use, cross-dataset generalization, and hybrid deep learning.