Advancement in EEG Signal Feature Extraction for Enhanced Seizure Detection and Prediction

Epileptic seizure is a highly severe neurological disorder that abruptly interrupts the daily life of those afflicted. In order to advance the technology utilized for managing epileptic seizures, new diagnostic procedures have concentrated on creating electroencephalogram (EEG) techniques that are based on machine learning and deep learning models. The main aim of this research is to focus on some of thecommonly used methods for feature extraction as mentioned in the literature. It will also delve into potential uses of these methods. There are many ways described in this article to process and extract features from EEG signals. Those include time, frequency, decomposing, time-frequency and spatial. Finally, we look at different areas where artificial intelligence is utilized including assistive technology, classification of brain related diseases brain-computer interface systems and integration of machine learning techniques. These applications contribute to a general architecture for analyzing EEG signals. Lastly, potential directions in EEG signal analysis involving feature extraction will be discussed. This study's findings will guide researchers find out which machine learning models that are most effective when it comes to deep learning as well as how feature extraction can be done so that it can enhance detection accuracy of epilepsy seizure based on EEG data.