Autor(es)

Santos, Eliana M. ; San-Martin, Rodrigo ; Fraga, Francisco J. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2021 18TH INTERNATIONAL MULTI-CONFERENCE ON SYSTEMS, SIGNALS & DEVICES (SSD); v. N/A, p. 6-pg., 2021-01-01.

Resumo

Motor imagery (MI) is the most used paradigm in Brain-Computer Interface (BCI). As in other BCI paradigms, it is divided into feature extraction and classification. In this study, we implemented two feature extraction methods, one based on the well-known Common Spatial Pattern (CSP) algorithm and the other based on a not so well-known electroencephalogram (EEG) source localization technique called Low-Resolution Brain Electromagnetic Tomography (LORETA). We evaluated 30 right-handed subjects from an EEG data set made publicly available through Giga Science, where participants performed MI of left- and right-hand movements. After feature extraction with CSP and LORETA, MI tasks classification were carried out using the Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naive Bayes (NB) and Multi-Layer Perceptron (MLP) algorithms. Finally, we evaluated classification performance with all possible combination of classifiers and feature extraction methods. For all classifiers, the CSP feature extraction method performed better than LORETA. The best classification accuracy for the LORETA method was 71.2% and for the CSP method was 94.2%, both achieved with SVM. (AU)

• Processo FAPESP: 17/15243-7 - Análise de conectividade cerebral baseada em eletroencefalografia para auxílio ao diagnóstico da Doença de Alzheimer e da Lesão Axonal Difusa
• Beneficiário: Francisco José Fraga da Silva
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 15/09510-7 - Análise computacional do eletroencefalograma para auxílio ao diagnóstico precoce da Doença de Alzheimer
• Beneficiário: Francisco José Fraga da Silva
• Modalidade de apoio: Auxílio à Pesquisa - Regular