This review article by Rammivas Sharma and Hemant Kumar Meena explores cutting-edge electroencephalography (EEG) signal processing techniques, focusing on noise reduction, artifact removal, and feature extraction. The study also examines emerging trends such as graph signal processing (GSP), deep learning-based methods, and real-time processing, highlighting their potential in enhancing EEG signal analysis accuracy and efficiency. The research reviews state-of-the-art techniques and advanced feature extraction methods in time, frequency, and time-frequency domains, with applications in cognitive neuroscience, brain–computer interfaces, and clinical diagnostics. The paper presents a comparative analysis of existing methodologies, identifying research gaps and future directions. It emphasizes the significance of GSP in exploring intricate brain networks and dynamic interactions, offering insights into neurological disorders and cognitive functions. Advanced techniques address challenges related to non-stationary and noisy EEG signals, significantly improving accuracy and efficiency in EEG signal analysis. The review underscores the vital role of EEG signal processing in unraveling the complexities of the human brain and sets the stage for future developments in the field.This review article by Rammivas Sharma and Hemant Kumar Meena explores cutting-edge electroencephalography (EEG) signal processing techniques, focusing on noise reduction, artifact removal, and feature extraction. The study also examines emerging trends such as graph signal processing (GSP), deep learning-based methods, and real-time processing, highlighting their potential in enhancing EEG signal analysis accuracy and efficiency. The research reviews state-of-the-art techniques and advanced feature extraction methods in time, frequency, and time-frequency domains, with applications in cognitive neuroscience, brain–computer interfaces, and clinical diagnostics. The paper presents a comparative analysis of existing methodologies, identifying research gaps and future directions. It emphasizes the significance of GSP in exploring intricate brain networks and dynamic interactions, offering insights into neurological disorders and cognitive functions. Advanced techniques address challenges related to non-stationary and noisy EEG signals, significantly improving accuracy and efficiency in EEG signal analysis. The review underscores the vital role of EEG signal processing in unraveling the complexities of the human brain and sets the stage for future developments in the field.