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Classification of EEG Signals Based on Pattern Recognition Approach.

Hafeez Ullah Amin¹, Wajid Mumtaz¹, Ahmad Rauf Subhani¹

¹Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri Iskandar, Malaysia.

Frontiers in Computational Neuroscience

|December 7, 2017

Summary

This study introduces a novel wavelet-based feature extraction method for electroencephalogram (EEG) signal classification. The approach achieves high accuracy in distinguishing cognitive states, outperforming existing methods.

Keywords:

electroencephalogram (EEG)feature extraction feature selection machine learning classifiers

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Area of Science:

Neuroscience
Signal Processing
Machine Learning

Background:

Electroencephalogram (EEG) signal classification is crucial for understanding cognitive states.
Traditional feature extraction methods may not fully capture the complexity of EEG signals during cognitive tasks.

Purpose of the Study:

To propose and validate a novel wavelet-based feature extraction method for EEG signal classification.
To discriminate between EEG signals recorded during complex cognitive tasks and baseline conditions.

Main Methods:

Wavelet-based feature extraction, including multi-resolution decomposition and relative wavelet energy computation.
Feature optimization using Fisher's discriminant ratio (FDR) and principal component analysis (PCA).
Classification using K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) classifiers on a high-density EEG dataset.

Main Results:

Achieved up to 99.11% accuracy using SVM with coefficient approximations (A5) for low frequencies (0-3.90 Hz).
High accuracy rates (98.57% for SVM, 98.39% for KNN) were obtained for detailed coefficients (D5) in the 3.90-7.81 Hz range.
The proposed method demonstrated superior performance compared to existing quantitative feature extraction techniques on both a custom and a public dataset.

Conclusions:

The proposed wavelet-based feature extraction method reliably and accurately classifies EEG signals during cognitive tasks.
This approach offers a significant improvement over conventional methods for EEG-based cognitive state discrimination.