EEG Signal Feature Extraction

MATLAB 2K 199 views 0 downloads 1 credits
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Resource Overview

This code implementation is divided into three segments, extracting comprehensive EEG features and implementing machine learning workflows

Detailed Documentation

This code structure consists of three main components. First, it extracts multiple features from EEG data, implementing signal processing techniques to capture frequency domain characteristics (such as alpha, beta, theta waves) and phase information through Fourier transforms or wavelet analysis. Second, the code utilizes these extracted features to train machine learning models, employing classification algorithms like SVM or neural networks to categorize and predict EEG patterns. Finally, the implementation applies these trained models to real EEG datasets, enabling the extraction of clinically relevant information including attention levels, emotional states, and cognitive load metrics through probabilistic inference and pattern recognition techniques.

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