Congratulations to Andre Cakici for finishing his Master’s Thesis!
We congratulate Andre Cakici on the completion of his master’s thesis on the topic “Decoding Hand Digit Dynamics with a Motor Unit Model”. The master’s thesis was successfully completed in a very good presentation with excellent results.
In his thesis, he dealt with the neural drive of the skeletal muscles responsible for different degrees of freedom of the hand. Neural drive occurs either via the motor cortex in the brain, which propagates through the spinal cord and efferent neurons, or via afferent feedback. The motor unit translates this control signal into force generation. Examination of the motor unit discharge patterns provides direct information about this signal.
A combination of a blind source separation algorithm (convolutional kernel compensation) and a factorization method (non-negative matrix factorization) was used to study the biphasic activity (flexion and extension) of hand digits. 320 channels of surface electromyography were recorded and decomposed into individual spike trains of motor units by Andre in post-processing.
At the end of his great work, Andre showed with the NMF results that although there was a group of motor units that were not correlated with flexion or extension, most of the variance in the population was explained by these two components.
We wish Andre the best for his future and thank him for his great work in our lab!