Authors:
Pradeep Kr. Sharma,Pankaj Dadheech,DOI NO:
https://doi.org/10.26782/jmcms.2026.04.00009Keywords:
EEG signal classification,Steady-State Visual Evoked Potential,attention focus detection,motor imagery,Brain-Computer Interface,machine learning,deep learning,Abstract
New developments in the Brain-Computer Interface (BCI) technology have increased the rate at which research has been done on precise and quick electroencephalography (EEG)-based signal classification models. This review analyses new trends, procedures, problems, and gaps in research on EEG signal classification in three large cognitive paradigms: Steady-State Visual Evoked Potential (SSVEP), detection of the attention focus, and motor imagery (MI). These paradigms form the focus of real-time BCI applications, e.g., assistive technologies, neurorehabilitation, adaptive learning, and augmented interaction systems. The analysis presented in the paper on the development of the traditional machine learning (ML) and the modern deep learning (DL) models of the EEG interpretation systematically reviews the progression of the original ideas in the EEG interpretation field. Power spectral density analysis, Common Spatial Patterns (CSP), wavelet transform, and empirical mode decomposition (EMD) techniques of feature extraction, and Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) techniques are critically examined. Some of the performance evaluation metrics that are widely employed in the literature are also addressed. Special attention is paid to the real-life issues that accompany real-world EEG data, such as low signal-to-noise ratio, artifact contamination, inter-subject variability, limited diversity of datasets, and bad model interpretability. It is believed that such public benchmark datasets as BCI Competition datasets, PhysioNet, and other multi-subject repositories can be used to support comparative analysis. Additional requirements of unified evaluation frameworks, real-time system-aware assessment, hybrid models, multimodal fusion strategies, transfer learning, and explainable AI have been identified in the review in an attempt to enhance the accuracy, robustness, and trustworthiness of EEG-based cognitive systems. On the whole, the given study can be used as a consolidated basis for the creation of future-generation EEG-based BCI frameworks.Refference:
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