Cargando…
Motor Imagery Analysis from Extensive EEG Data Representations Using Convolutional Neural Networks
Motor imagery is a complex mental task that represents muscular movement without the execution of muscular action, involving cognitive processes of motor planning and sensorimotor proprioception of the body. Since the mental task has similar behavior to that of the motor execution process, it can be...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9414220/ https://www.ncbi.nlm.nih.gov/pubmed/36015854 http://dx.doi.org/10.3390/s22166093 |
_version_ | 1784775937792933888 |
---|---|
author | Lomelin-Ibarra, Vicente A. Gutierrez-Rodriguez, Andres E. Cantoral-Ceballos, Jose A. |
author_facet | Lomelin-Ibarra, Vicente A. Gutierrez-Rodriguez, Andres E. Cantoral-Ceballos, Jose A. |
author_sort | Lomelin-Ibarra, Vicente A. |
collection | PubMed |
description | Motor imagery is a complex mental task that represents muscular movement without the execution of muscular action, involving cognitive processes of motor planning and sensorimotor proprioception of the body. Since the mental task has similar behavior to that of the motor execution process, it can be used to create rehabilitation routines for patients with some motor skill impairment. However, due to the nature of this mental task, its execution is complicated. Hence, the classification of these signals in scenarios such as brain–computer interface systems tends to have a poor performance. In this work, we study in depth different forms of data representation of motor imagery EEG signals for distinct CNN-based models as well as novel EEG data representations including spectrograms and multidimensional raw data. With the aid of transfer learning, we achieve results up to 93% accuracy, exceeding the current state of the art. However, although these results are strong, they entail the use of high computational resources to generate the samples, since they are based on spectrograms. Thus, we searched further for alternative forms of EEG representations, based on 1D, 2D, and 3D variations of the raw data, leading to promising results for motor imagery classification that still exceed the state of the art. Hence, in this work, we focus on exploring alternative methods to process and improve the classification of motor imagery features with few preprocessing techniques. |
format | Online Article Text |
id | pubmed-9414220 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94142202022-08-27 Motor Imagery Analysis from Extensive EEG Data Representations Using Convolutional Neural Networks Lomelin-Ibarra, Vicente A. Gutierrez-Rodriguez, Andres E. Cantoral-Ceballos, Jose A. Sensors (Basel) Article Motor imagery is a complex mental task that represents muscular movement without the execution of muscular action, involving cognitive processes of motor planning and sensorimotor proprioception of the body. Since the mental task has similar behavior to that of the motor execution process, it can be used to create rehabilitation routines for patients with some motor skill impairment. However, due to the nature of this mental task, its execution is complicated. Hence, the classification of these signals in scenarios such as brain–computer interface systems tends to have a poor performance. In this work, we study in depth different forms of data representation of motor imagery EEG signals for distinct CNN-based models as well as novel EEG data representations including spectrograms and multidimensional raw data. With the aid of transfer learning, we achieve results up to 93% accuracy, exceeding the current state of the art. However, although these results are strong, they entail the use of high computational resources to generate the samples, since they are based on spectrograms. Thus, we searched further for alternative forms of EEG representations, based on 1D, 2D, and 3D variations of the raw data, leading to promising results for motor imagery classification that still exceed the state of the art. Hence, in this work, we focus on exploring alternative methods to process and improve the classification of motor imagery features with few preprocessing techniques. MDPI 2022-08-15 /pmc/articles/PMC9414220/ /pubmed/36015854 http://dx.doi.org/10.3390/s22166093 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lomelin-Ibarra, Vicente A. Gutierrez-Rodriguez, Andres E. Cantoral-Ceballos, Jose A. Motor Imagery Analysis from Extensive EEG Data Representations Using Convolutional Neural Networks |
title | Motor Imagery Analysis from Extensive EEG Data Representations Using Convolutional Neural Networks |
title_full | Motor Imagery Analysis from Extensive EEG Data Representations Using Convolutional Neural Networks |
title_fullStr | Motor Imagery Analysis from Extensive EEG Data Representations Using Convolutional Neural Networks |
title_full_unstemmed | Motor Imagery Analysis from Extensive EEG Data Representations Using Convolutional Neural Networks |
title_short | Motor Imagery Analysis from Extensive EEG Data Representations Using Convolutional Neural Networks |
title_sort | motor imagery analysis from extensive eeg data representations using convolutional neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9414220/ https://www.ncbi.nlm.nih.gov/pubmed/36015854 http://dx.doi.org/10.3390/s22166093 |
work_keys_str_mv | AT lomelinibarravicentea motorimageryanalysisfromextensiveeegdatarepresentationsusingconvolutionalneuralnetworks AT gutierrezrodriguezandrese motorimageryanalysisfromextensiveeegdatarepresentationsusingconvolutionalneuralnetworks AT cantoralceballosjosea motorimageryanalysisfromextensiveeegdatarepresentationsusingconvolutionalneuralnetworks |