EEG-Based Classification for Autism Spectrum Disorder and Emotional State Recognition - A Critical Evaluation

EEG signal processing has seen rapid advances in recent years due to innovations in machine learning, deep learning, and data preprocessing techniques. This review examines major studies from 2020 to 2024, exploring preprocessing methods, sampling rates, data samples, and model accuracy metrics across various EEG applications. Through a year-wise analysis, highlight the progression in signal processing methods, the impact of higher sampling rates, and the rise of sophisticated architectures like hybrid neural networks and graph-based models. Identify notable trends, including improved accuracy rates and efficiency gains with adaptive algorithms and the integration of automated artifact removal in preprocessing pipelines. This review also contrasts the advantages and limitations of recent technologies, providing a comprehensive outlook on current capabilities and future directions in EEG signal processing.