EEG-based Subjects Identification based on Biometrics of Imagined Speech using EMD

TL;DR

This study introduces an EEG-based biometric system using Empirical Mode Decomposition of imagined speech signals, achieving high accuracy in subject identification with various classifiers.

Contribution

The paper presents a novel method combining EMD and specific features for EEG-based biometric identification during imagined speech, demonstrating high classification accuracy.

Findings

Achieved up to 92% accuracy with Linear SVM

EMD-based features effectively distinguish subjects

Method outperforms traditional EEG biometric approaches

Abstract

When brain activity is translated into commands for real applications, the potential for human capacities augmentation is promising. In this paper, EMD is used to decompose EEG signals during Imagined Speech in order to use it as a biometric marker for creating a Biometric Recognition System. For each EEG channel, the most relevant Intrinsic Mode Functions (IMFs) are decided based on the Minkowski distance, and for each IMF 4 features are computed: Instantaneous and Teager energy distribution and Higuchi and Petrosian Fractal Dimension. To test the proposed method, a dataset with 20 subjects who imagined 30 repetitions of 5 words in Spanish, is used. Four classifiers are used for this task - random forest, SVM, naive Bayes, and k-NN - and their performances are compared. The accuracy obtained (up to 0.92 using Linear SVM) after 10-folds cross-validation suggest that the proposed method based on EMD can be valuable for creating EEG-based biometrics of imagined speech for Subjects identification.