Enhancing the control of myoelectric prostheses requires accurate and energy-efficient decoding of user intent. This thesis investigated the integration of Spiking Neural Networks (SNNs) for motor imagery based electroencephalography (MI-EEG) pattern recognition to improve both classification accuracy and energy efficiency for prosthetic command generation. Our proposed approach envisions an electromyographic (EMG) and MI-EEG hybrid control model, using SNNs to decode the movement intention from the MI-EEG, and the EMG providing proportional control. We implemented SNNs through ANN-to-SNN conversion, specifically evaluating lightweight deep convolutional network architectures (LENet) on a 3 class MI-EEG dataset from the same limb. Results demonstrate that the SNN model achieved a mean overall accuracy of 77.23%, on par with its LENet counterpart and competitive with state-of-the-art methods, while its theoretical energy estimations indicate that the SNN model could reduce computational energy consumption by 59.6% compared to its CNN equivalent, primarily due to sparse, event-driven processing. These findings underscore the potential of SNNs to enable accurate, low-power processing, paving the way for more natural and practical neuroprosthetic control systems.