2018/09/13 by Luis Alfredo Moctezuma, Moctezuma, Luis Alfredo, Marta Molinas +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Medicine · Neuroscience · #Blind Source Separation Techniques #ECG Monitoring and Analysis #EEG and Brain-Computer Interfaces #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Neural Networks and Applications #Neurons and Cognition (q-bio.NC) #Signal Processing (eess.SP) #cs.LG #eess.SP #electronic engineering #information engineering #q-bio.NC
paper · pdf · doi:10.48550/arxiv.1809.06697
arxiv created 2018/09/13 · openalex publication_date 2018/09/13 · arxiv updated 2018/09/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.