Recorded signals from implanted electrodes in the brain can be used to restore motor function for patients suffering from neurological injuries or neurodegenerative diseases, either through decoding the signal to control exterior assistive devices or as a trigger for downstream neurostimulation. However, an ongoing challenge in the field is the limited and unpredictable ability to record from neural prosthesis for longer periods of time due to the biological response from the brain, the technical issues related to the signal processing algorithms used, and the stability of the microelectrode itself. Our lab has recently uncovered multiple new observations of structural and functional remodeling of neurons surrounding implanted electrodes, which may contribute to the instability in recording quality over time. Some of these observations indicate losses, or changes, in synaptic connectivity of individual local neurons with the surrounding network. In this thesis, I have introduced signal processing tools which will help us to understand and characterize the structural and functional remodeling happening around the neural prosthesis, through the incorporation of multi-unit activity, coherence analysis, spike-triggered average of the local field potential, and spike triggered covariance of the local field potential. A better understanding of these changes can provide us with new insights into how the network is remodeling itself over time and the major factors which influence these processes, which will help us to enable the design of better and more biocompatible neural prostheses.
Read
