Improving Motor Imagery EEG Classification Based on Channel Selection Using a Deep Learning Architecture

Autor: Erick-Alejandro González-Barbosa, Tomas Alberto Garcia-Calva, Juan Gabriel Avina-Cervantes, Tat'y Mwata-Velu, José Ruiz-Pinales, Jose-Joel Gonzalez-Barbosa, Juan B. Hurtado-Ramos
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Mathematics; Volume 10; Issue 13; Pages: 2302
ISSN: 2227-7390
DOI: 10.3390/math10132302
Popis: Recently, motor imagery EEG signals have been widely applied in Brain–Computer Interfaces (BCI). These signals are typically observed in the first motor cortex of the brain, resulting from the imagination of body limb movements. For non-invasive BCI systems, it is not apparent how to locate the electrodes, optimizing the accuracy for a given task. This study proposes a comparative analysis of channel signals exploiting the Deep Learning (DL) technique and a public dataset to locate the most discriminant channels. EEG channels are usually selected based on the function and nomenclature of electrode location from international standards. Instead, the most suitable configuration for a given paradigm must be determined by analyzing the proper selection of the channels. Therefore, an EEGNet network was implemented to classify signals from different channel location using the accuracy metric. Achieved results were then contrasted with results from the state-of-the-art. As a result, the proposed method improved BCI classification accuracy.
Databáze: OpenAIRE
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