چكيده به لاتين
Reconstructing muscle activity from electromyogram (EMG) signals using non-invasive electroencephalogram (EEG) signals can lead to significant advancements in brain-computer interfaces (BCIs). However, isolating muscle-related signals from EEG is challenging because EEG sensors capture a mixture of signals originating from various cortical regions. In this thesis, a novel method for estimating muscular activity from non-invasive EEG signals during grip and lift (GAL) tasks is presented. For this purpose, similar to the approach of extracting muscle activity from EMG, the envelopes of five frequency bands—delta, theta, alpha, beta, and gamma—were selected as input features for the decoding models. These signals were then transformed into three-dimensional spatial-temporal matrices based on the EEG electrode locations. The proposed Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model was employed to extract spatial-temporal information from the EEG. This model was compared with five common linear and nonlinear decoding methods, including: 1) Statistical models: Multivariate Linear Regression (mLR), Kalman Filter (KF), Wiener Cascade Filter (WC), and 2) Machine learning approaches: eXtreme Gradient Boosting (XGB) and Multi-Layer Perceptron (MLP). According to the results, the mean ± standard deviation of the correlation coefficient (CC), coefficient of determination (R²), and normalized root mean square error (nRMSE) between the estimated muscle activity and the actual muscle activity of two muscles in five participants were 0.76 ± 0.10, 0.54 ± 0.17, and 0.21 ± 0.05, respectively. These values are comparable to the results of previous studies, including those using invasive Local Field Potential (LFP) and Electrocorticogram (ECoG) signals. More complex decoding approaches, such as CNN-LSTM and XGB, significantly outperformed linear models like mLR, KF, and the deep learning approach MLP (P-Value < 0.003). These findings indicate the proposed model's ability to extract nonlinear relationships between brain and muscle activity.
