EEG-BCI Features Discrimination between Executed and Imagined Movements Based on FastICA, Hjorth Parameters, and SVM
Tat’y Mwata-Velu,
Armando Navarro Rodríguez,
Yanick Mfuni-Tshimanga,
Richard Mavuela-Maniansa,
Jesús Alberto Martínez Castro,
Jose Ruiz-Pinales,
Juan Gabriel Avina-Cervantes
Affiliations
Tat’y Mwata-Velu
Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Avenida Juan de Dios Bátiz esquina Miguel Othón de Mendizábal Colonia Nueva Industrial Vallejo, Alcadía Gustavo A. Madero, Ciudad de México 07738, Mexico
Armando Navarro Rodríguez
Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Avenida Juan de Dios Bátiz esquina Miguel Othón de Mendizábal Colonia Nueva Industrial Vallejo, Alcadía Gustavo A. Madero, Ciudad de México 07738, Mexico
Yanick Mfuni-Tshimanga
Institut Supérieur des Techniques Appliquées (ISTA-NDOLO), Avenue de l’aérodrome, Kinshasa 6593, Democratic Republic of the Congo
Richard Mavuela-Maniansa
Institut Supérieur Pédagogique Technique de Kinshasa (ISPT-KIN), Av. de la Science 5, Gombe, Kinshasa 3287, Democratic Republic of the Congo
Jesús Alberto Martínez Castro
Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Avenida Juan de Dios Bátiz esquina Miguel Othón de Mendizábal Colonia Nueva Industrial Vallejo, Alcadía Gustavo A. Madero, Ciudad de México 07738, Mexico
Jose Ruiz-Pinales
Telematics and Digital Signal Processing Research Groups (CAs), Department of Electronics Engineering, University of Guanajuato, Salamanca 36885, Mexico
Juan Gabriel Avina-Cervantes
Telematics and Digital Signal Processing Research Groups (CAs), Department of Electronics Engineering, University of Guanajuato, Salamanca 36885, Mexico
Brain–Computer Interfaces (BCIs) communicate between a given user and their nearest environment through brain signals. In the case of device handling, an accurate control-based BCI depends essentially on how the user performs corresponding mental tasks. In the BCI illiteracy-related literature, one subject could perform a defined paradigm better than another. Therefore, this work aims to identify recorded Electroencephalogram (EEG) signal segments related to the executed and imagined motor tasks for BCI system applications. The proposed approach implements pass-band filters and the Fast Independent Component Analysis (FastICA) algorithm to separate independent sources from raw EEG signals. Next, EEG features of selected channels are extracted using Hjorth parameters. Finally, a Support Vector Machines (SVMs)-based classifier identifies executed and imagined motor features. Concretely, the Physionet dataset, related to executed and imagined motor EEG signals, provided training, testing, and validating data. The numerical results let us discriminate between executed and imagined motor tasks accurately. Therefore, the proposed method offers a reliable alternative to extract EEG features for BCI based on executed and imagined movements.
