| Autor: |
Mwata-Velu T; Centro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN), Avenida Juan de Dios Bátiz Esquina Miguel Othón de Mendizábal Colonia Nueva Industrial Vallejo, Alcaldía Gustavo A. Madero, Ciudad de Mexico C.P. 07738, Mexico.; Institut Supérieur Pédagogique Technique de Kinshasa (I.S.P.T.-KIN), Av. de la Science 5, Gombe, Kinshasa 3287, Democratic Republic of the Congo.; Telematics and Digital Signal Processing Research Groups (CAs), Department of Electronics Engineering, Universidad de Guanajuato, Salamanca 36885, Mexico., Niyonsaba-Sebigunda E; Institut Supérieur Pédagogique Technique de Kinshasa (I.S.P.T.-KIN), Av. de la Science 5, Gombe, Kinshasa 3287, Democratic Republic of the Congo., Avina-Cervantes JG; Telematics and Digital Signal Processing Research Groups (CAs), Department of Electronics Engineering, Universidad de Guanajuato, Salamanca 36885, Mexico., Ruiz-Pinales J; Institut Supérieur Pédagogique Technique de Kinshasa (I.S.P.T.-KIN), Av. de la Science 5, Gombe, Kinshasa 3287, Democratic Republic of the Congo., Velu-A-Gulenga N; Institut Supérieur Pédagogique de Kikwit (I.S.P. KIKWIT), Av Nzundu 2, Com. Lukolela, Kikwit 8211, Democratic Republic of the Congo., Alonso-Ramírez AA; Instituto Tecnológico Nacional de México en Celaya (TecNM-Celaya), Av. Antonio García Cubas Pte 600, Celaya C.P. 38010, Guanajuato, Mexico. |
| Abstrakt: |
Nowadays, Brain-Computer Interfaces (BCIs) still captivate large interest because of multiple advantages offered in numerous domains, explicitly assisting people with motor disabilities in communicating with the surrounding environment. However, challenges of portability, instantaneous processing time, and accurate data processing remain for numerous BCI system setups. This work implements an embedded multi-tasks classifier based on motor imagery using the EEGNet network integrated into the NVIDIA Jetson TX2 card. Therefore, two strategies are developed to select the most discriminant channels. The former uses the accuracy based-classifier criterion, while the latter evaluates electrode mutual information to form discriminant channel subsets. Next, the EEGNet network is implemented to classify discriminant channel signals. Additionally, a cyclic learning algorithm is implemented at the software level to accelerate the model learning convergence and fully profit from the NJT2 hardware resources. Finally, motor imagery Electroencephalogram (EEG) signals provided by HaLT's public benchmark were used, in addition to the k-fold cross-validation method. Average accuracies of 83.7% and 81.3% were achieved by classifying EEG signals per subject and motor imagery task, respectively. Each task was processed with an average latency of 48.7 ms. This framework offers an alternative for online EEG-BCI systems' requirements, dealing with short processing times and reliable classification accuracy. |
