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 and Juan Gabriel Avina-Cervantes ()
Additional contact information
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

Mathematics, 2023, vol. 11, issue 21, 1-17

Abstract: 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.

Keywords: electroencephalogram (EEG); fast independent component analysis (FastICA); motor execution (ME); motor imagery (MI); brain–computer interfaces (BCIs); support vector machines (SVMs) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/21/4409/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/21/4409/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:21:p:4409-:d:1266473

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().