  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.
