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Convolutional Neural Network for Imagine Movement Classification for Neurorehabilitation of Upper Extremities Using Low-Frequency EEG Signals for Spinal Cord Injury

2022; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-030-99170-8_20

1865-0937

Mario G. Gualsaquí, Alejandro S. Delgado, Lady L. González, Giovana F. Vaca, Diego Almeida-Galárraga, Graciela Marisa Salum, Carolina Cadena-Morejón, Andrés Tirado-Espín, Fernando Villalba-Meneses,

Muscle activation and electromyography studies

As a result of the improvement of digital signal processing techniques and pattern recognition, it has been possible to relate brain signals with motor actions. Indeed, there are many ongoing investigations related to brain-computer interfaces that might be helpful for biomedical applications in rehabilitation procedures. This study proposes to use delta electroencephalographic signal band (0.3 Hz–3 Hz) with a classification of imagine movements using a convolutional neural network for neurorehabilitation assistant for upper limbs in patients with spinal cord injuries. This was achieved through the classification of 5 classes of movements to predict potential imaginary movement by the training of a convolutional neural network with a specific architecture for electroencephalographic signals, EEGNet. Interpolation and independent component analysis was applied as well to optimize the training of a neural network which allowed to predict neurophysiological motor processes with a 31% accuracy. Hence, the classification of movement-related cortical potential with convolutional neural network model opens the possibility for future Brain-Computer Interfaces applications in the biomedical field for rehabilitation processes.