MACHINE LEARNING APPROACHES FOR EEG SIGNAL DESIGN AND INTERPRETATION: A SYSTEMATIC REVIEW

Electroencephalography (EEG) has become an essential instrument for noninvasive monitoring of brain activity, applicable in clinicaldiagnostics, brain-computer interfaces (BCIs), cognitive neuroscience, and mental health evaluation.The intrinsic complexity, non-stationarity, and susceptibility to noise of EEG signals provide considerable obstacles to precise interpretation and dependable feature extraction.In recent years, machine learning (ML) methodologies have exhibited significant potential in improving EEG signal design, preprocessing, classification, and interpretation.This systematic evaluation consolidates the latest machine learning methods utilized in EEG analysis, classifying them into supervised, unsupervised, and deep learning frameworks.Essential elements including signal denoising, dimensionality reduction, feature engineering, and classification methodologies are rigorously analyzed.The review emphasizes specialized applications such as seizure detection, emotion recognition, cognitive workload evaluation, and neurodegenerative disease diagnosis.Additionally, it examines the constraints of existing methodologies, including data Vandna, Banita https://iaeme.com/Home/