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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/16/6093 |
| _version_ | 1797442072123277312 |
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| author | Vicente A. Lomelin-Ibarra Andres E. Gutierrez-Rodriguez Jose A. Cantoral-Ceballos |
| author_facet | Vicente A. Lomelin-Ibarra Andres E. Gutierrez-Rodriguez Jose A. Cantoral-Ceballos |
| author_sort | Vicente A. Lomelin-Ibarra |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T12:36:31Z |
| format | Article |
| id | doaj.art-02ec69f08c2b426d8dc00d15640fb4a3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:36:31Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-02ec69f08c2b426d8dc00d15640fb4a32023-11-30T22:22:56ZengMDPI AGSensors1424-82202022-08-012216609310.3390/s22166093Motor Imagery Analysis from Extensive EEG Data Representations Using Convolutional Neural NetworksVicente A. Lomelin-Ibarra0Andres E. Gutierrez-Rodriguez1Jose A. Cantoral-Ceballos2Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey 64849, MexicoMAHLE Shared Services, Monterrey 64650, MexicoTecnologico de Monterrey, School of Engineering and Sciences, Monterrey 64849, MexicoMotor 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.https://www.mdpi.com/1424-8220/22/16/6093deep learningmotor imagerymotor skill impairment |
| spellingShingle | Vicente A. Lomelin-Ibarra Andres E. Gutierrez-Rodriguez Jose A. Cantoral-Ceballos Motor Imagery Analysis from Extensive EEG Data Representations Using Convolutional Neural Networks Sensors deep learning motor imagery motor skill impairment |
| 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 | deep learning motor imagery motor skill impairment |
| url | https://www.mdpi.com/1424-8220/22/16/6093 |
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