Abstract
A stroke may be accompanied by consequent disabilities that include neuromuscular, cognitive, somatosensitive, and physiological disconnections. However, neuroplasticity allows the brain to generate new pathways for learning and adapting to external situations after brain injuries, such as stroke. This chapter discusses artificial neuroplasticity based on a deep learning application. Complete electroencephalographic signals are used to reconstruct the original motor signal, restore the necessary pulse, and promote the motion in short-term memory in the spinal cord. The deep learning program was developed using a two-dimensional data process that augments the computed velocity and arrives at a natural procedure. Integrated technology reconstructs the lost signal, restoring motion signals in gray matter through either feature maps of the convolutional neural network of the resulting model or an algorithm that reconstructs the signal through the previously extracted characteristics of artificial neural networks.
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Díaz, R.J., Marin, L.V.J., García, M.A.B. (2019). Artificial Neuroplasticity with Deep Learning Reconstruction Signals to Reconnect Motion Signals for the Spinal Cord. In: Chaari, L. (eds) Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine. Advances in Predictive, Preventive and Personalised Medicine, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-11800-6_2
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DOI: https://doi.org/10.1007/978-3-030-11800-6_2
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