چكيده به لاتين
This thesis is defined in order to solve one of the most important problems of a part of the society who suffers from mobility impairments or spinal cord injuries which limit functions of moving in any of the limbs. BMI systems can interpret what they want by decoding the activities in the brain. These systems actually allow the mentioned people to do many things with less dependence on others. In addition, these systems can have a significant impact on the advancement of technology and the improvement of the routine life of people. The main goal of this research is to continuously decode the position of the hand in three dimensions, using ECoG signals recorded from the motor cortex of the brain. The important point in this research is the use of ECoG signals to estimate the position of the monkey's hand, which has been obtained in previous research in this field. These signals provide the possibility of long-term recording and, as a result, access to a bigger data set. In this research, with the help of mathematical modeling and the use of deep neural networks, which are very suitable for big data sets, feature extraction and estimation of movement activities are done according to the signals. One of the challenges in this area is the accuracy and computational time in estimating movements, which in the presented methods have been tried to improve these two important factors compared to the previous research projects. In most signal processing methods, the two stages of feature extraction and mathematical modeling are considered separately and using different training processes. Regarding this issue, several deep neural network structures are presented and analyzed in this research, which both feature extraction and mathematical modeling stages, and the training process is carried out simultaneously. The methods discussed in this research include four deep neural network models: 3D-CNN, LSTM, two-layer LSTM, and CNN-LSTM. To check the performance speed and accuracy of deep neural network models, the results of each model are compared with the common PLS model, which has been considered in many research projects. The findings of this research indicate that the use of deep neural networks provides the possibility of estimating movements according to the signals used for training and in an efficient time. Also, among the investigated models, the two-layer LSTM neural network provides the highest prediction accuracy. This method with an average correlation coefficient of 0.77 has the closest prediction of the movement trajectory compared to the real movement trajectory. Keywords: Brain-Machine Interface (BMI), Decoding, Feature extraction, Deep Learnin (DL), ECoG Signals, Convolutional Neural Network (CNN), Long Short Term Memory (LSTM)
