چكيده به لاتين
Abstract:
However, brain-computer-based devices have not been usable compared to other controls, and for people with disabilities, brain-computer-based devices are the most important means of communicating with the outside world. These devices are used to order equipment and so on. These tools can extend the capabilities of a person. One of the issues that have recently been extended with the increasing number of crashes and brain and spinal cord problems, as well as the advancement of medical engineering science, is communicating between the brain and computer.This can help people with disabilities to communicate well with their surroundings. In order to help patients with speech and movement problems, neural and muscular prosthetics and robotic rehabilitation equipment with direct control of brain signals in recent years has been taken into consideration and extensive research has been done to upgrade the software and hardware of the devices. Devices that generate bridge between between people and the outside environment using brain signals are called BCI-based devices. The basis of this system is the classification of brain signals to achieve some of the person's wishes or thoughts. For this classification, there are various methods depending on the characteristics of the feature-specific attrition algorithms and classification methods, which should determine these factors in order to achieve the best results in classifying each set of data. In this research, a BCI system is proposed using artificial neural networks.
The proposed neural networks are taught using various meta-innovative algorithms, and ultimately, the best algorithms in this study are presented and the performance of the proposed BCI system is discussed. . Finally, with this proposed system, for some subjects such as DS1c, the classification rate was nearly 100%, and for the rest of the subjects, up to 90%, with the maintenance of optimal speed.
Keywords: Calssification; Recognition; BCI; EEG; Motor Imageries; Neural Networks;
