مقاله انگلیسی رایگان در مورد یادگیری ماشین برای کدگشایی الکتروانسفالوگرافی – IEEE 2018

A. Related Work: BMI

It has been reported clinically that, diseases like stroke and traumatic brain injury do cause long-term, unilateral loss of motor control. Neurological disabilities and related diseases are causing the permanent loss of motoring of limbs, in addition to sensory malfunction. In particular, even in specific and related cases, limbs disability is very severe that it is not likely to feed oneself or even communicate. With the advances on technology, and in specific – BMI – Brain Machine Interface, there is a new research direction that aims to support disabled patients by translating neural signals from the brain into useful control signals for guiding prosthetic limbs. The prime objective in developing a neural prosthesis is to substitute neural circuitry in the brain, that no longer functions correctly or efficiently, Fig.1. While achieving such goal, this requires artificial reconstruction of neuron-to neuron connections in a way that can be recognized by the remaining normal circuitry, and that promotes appropriate interaction. It is estimated that approximately (9 million) people worldwide suffer from stroke every year and that almost (30%) of stroke survivors suffering from irreversible motor impairments, Teo et al. [2]. According to Andrew et al. [3], brain-controlled interfaces are devices that capture brain transmissions involved in a subject’s intention to act. Neural activated prosthetic devices is becoming an important domain, and progressively relevant to a number of clinical and various neurological related diseases treatments. Fig. 1. Electroencephalogram (EEG) brain waves have been used extensively for people with disabilities, [1]. In particular, we refer to Fayad and Elmiyeh, [4], where it was mentioned about such input devices for stimulating zones within the nervous system, in particular, have been especially successful in achieving therapeutic effects. Charles et al. [5] also reviewed the challenges to clinical translation and discusses potential solutions. They made a focuses on hardware reliability, state-of-the-art decoding algorithms, and surgical considerations during implantation.