Comprehensive EEG Signal Feature Extraction for Neurological Disorder Diagnosis: Focus on Alzheimer's, Parkinson's, and Seizure Disorders

Abstract

This research paper examines the use of Electroencephalogram (EEG) signal feature extraction for diagnosing neurological disorders, specifically Alzheimer's, Parkinson's, and seizure disorders. It evaluates various methods for categorizing EEG signals, including time-domain, frequency-domain, and statistical transformations emphasizing their effectiveness in distinguishing relevant brainwave patterns (beta, alpha, theta, delta) from artifacts like eye blinks and muscle movements. The study highlights the challenges in artifact removal and provides an overview of key feature extraction techniques, particularly in the time and frequency domains. The implementation section details the application of machine learning algorithms to classify mental states using statistical features from EEG signals. The research identifies specific EEG patterns associated with Alzheimer's, Parkinson's, and seizure disorders, noting alterations in alpha, theta, and delta waves. The paper underscores the critical role of EEG feature extraction in diagnosing neurological disorders and recommends incorporating additional frequency-based methods to enhance predictive accuracy in future research.