TY - JOUR
T1 - Improving Motor Imagery EEG Classification Based on Channel Selection Using a Deep Learning Architecture
AU - Mwata-Velu, Tat’Y
AU - Avina-Cervantes, Juan Gabriel
AU - Ruiz-Pinales, Jose
AU - Garcia-Calva, Tomas Alberto
AU - González-Barbosa, Erick Alejandro
AU - Hurtado-Ramos, Juan B.
AU - González-Barbosa, José Joel
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - 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.
AB - 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.
KW - 10–20 international system
KW - EEG signals
KW - EEGNet
KW - deep learning
KW - motor imagery
UR - http://www.scopus.com/inward/record.url?scp=85133696797&partnerID=8YFLogxK
U2 - 10.3390/math10132302
DO - 10.3390/math10132302
M3 - Artículo
AN - SCOPUS:85133696797
SN - 2227-7390
VL - 10
JO - Mathematics
JF - Mathematics
IS - 13
M1 - 2302
ER -