Machine Learning–Based Signal Classification for Brain–Computer Interface Applications

Abstract

Brain–computer interfaces (BCIs) translate brain activity into control signals for external devices. Electroencephalography (EEG) is the most widely used noninvasively modality for BCIs because of its portability and temporal resolution, but EEG signals are low-SNR, nonstationary, and highly subject-specific, making classification challenging. This paper reviews state-of-the-art machine learning (ML) methods for EEG/BCI signal classification, proposes a comprehensive end-to-end methodology combining preprocessing, feature extraction (CSP, time-frequency features), and modern classifiers (LDA, SVM, ensemble methods, CNNs), and presents a sample experimental pipeline using public motor-imagery datasets. Results show that deep models (CNNs) typically outperform classical shallow classifiers when sufficient data or transfer learning is available, while filter-bank CSP and transfer learning remain effective for limited data and subject-specific calibration. The paper concludes with practical recommendations, limitations, and future research directions in transfer learning, domain adaptation, explainability, and privacy for BCI systems.