This review article explores emerging trends in EEG signal processing, focusing on noise reduction, artifact removal, and feature extraction. It highlights the potential of graph signal processing (GSP), deep learning, and real-time processing in enhancing EEG analysis accuracy and efficiency. The study examines techniques in time, frequency, and time-frequency domains, with applications in cognitive neuroscience, brain-computer interfaces, and clinical diagnostics. It also discusses novel methods like GSP and deep learning, analyzing their impact on EEG signal analysis. A comparative analysis of existing methodologies is presented, identifying research gaps and future directions. The significance of GSP in exploring complex brain networks and dynamic interactions is emphasized. These findings enhance understanding of brain communication, offering insights into neurological disorders and cognitive functions. Advanced techniques address challenges related to non-stationary and noisy EEG signals, improving accuracy and efficiency. The review underscores the vital role of EEG signal processing in understanding the human brain. It emphasizes the importance of robust algorithms and innovative methods in advancing EEG signal analysis. This research sets the stage for future developments in EEG signal processing. Keywords: EEG signals, signal processing, feature extraction, classification models, graph signal processing. The article discusses the use of computer-aided technologies in EEG signal analysis to overcome challenges and achieve quicker, more accurate results. These technologies are widely used in diagnosing neurological and neuropsychiatric disorders such as epilepsy, major depressive disorder, alcohol use disorder, and dementia. EEG's application in motor imagery has advanced neuroprosthesis. Physiological data like EEG is valuable in identity authentication, sleep stage classification, emotion recognition, eye state detection, and drowsiness monitoring. EEG signal processing typically involves four steps: preprocessing, feature extraction, feature selection, and analysis using machine learning models or statistical tests. The review aims to explore EEG-based research applications in existing literature. Figure 1 illustrates the step-by-step process outlined in the paper. A comparative analysis is conducted to achieve the objectives of research focusing on varied data collection methods, preprocessing techniques, feature extraction methods, post-processing techniques, and result analysis methods.This review article explores emerging trends in EEG signal processing, focusing on noise reduction, artifact removal, and feature extraction. It highlights the potential of graph signal processing (GSP), deep learning, and real-time processing in enhancing EEG analysis accuracy and efficiency. The study examines techniques in time, frequency, and time-frequency domains, with applications in cognitive neuroscience, brain-computer interfaces, and clinical diagnostics. It also discusses novel methods like GSP and deep learning, analyzing their impact on EEG signal analysis. A comparative analysis of existing methodologies is presented, identifying research gaps and future directions. The significance of GSP in exploring complex brain networks and dynamic interactions is emphasized. These findings enhance understanding of brain communication, offering insights into neurological disorders and cognitive functions. Advanced techniques address challenges related to non-stationary and noisy EEG signals, improving accuracy and efficiency. The review underscores the vital role of EEG signal processing in understanding the human brain. It emphasizes the importance of robust algorithms and innovative methods in advancing EEG signal analysis. This research sets the stage for future developments in EEG signal processing. Keywords: EEG signals, signal processing, feature extraction, classification models, graph signal processing. The article discusses the use of computer-aided technologies in EEG signal analysis to overcome challenges and achieve quicker, more accurate results. These technologies are widely used in diagnosing neurological and neuropsychiatric disorders such as epilepsy, major depressive disorder, alcohol use disorder, and dementia. EEG's application in motor imagery has advanced neuroprosthesis. Physiological data like EEG is valuable in identity authentication, sleep stage classification, emotion recognition, eye state detection, and drowsiness monitoring. EEG signal processing typically involves four steps: preprocessing, feature extraction, feature selection, and analysis using machine learning models or statistical tests. The review aims to explore EEG-based research applications in existing literature. Figure 1 illustrates the step-by-step process outlined in the paper. A comparative analysis is conducted to achieve the objectives of research focusing on varied data collection methods, preprocessing techniques, feature extraction methods, post-processing techniques, and result analysis methods.