What type of study is this?

This is a Quantitative Study study.

October 2, 2025

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

IKI. KorkmazOndokuz Mayıs University CTCengiz TepeOndokuz Mayıs University

Key Points

CSP with LDA achieved the highest accuracy at 72.5%, outperforming statistical features in motor execution classification.
Evaluation included metrics such as accuracy, precision, recall, and F1 score, showing consistent results with CSP and LDA.
Real-time feasibility benchmarks and post-cue time-window analysis highlight the practical applications for brain-computer interfaces.
Future research aims to enhance real-time capabilities and cross-dataset generalization using hybrid deep learning approaches.

Abstract

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.

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