EEG-based Subjects Identification based on Biometrics of Imagined Speech using EMD
This work addresses the need for secure and non-invasive biometric identification systems based on brain activity, though it is incremental as it applies existing EMD methods to a specific EEG dataset.
The paper tackled the problem of identifying individuals using EEG signals from imagined speech by employing Empirical Mode Decomposition (EMD) to extract biometric features, achieving an accuracy of up to 0.92 with Linear SVM after cross-validation.
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.