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Type of Document	Dissertation
Author	Cao, Shiyan
URN	etd-07232003-012018
Persistent URL	http://resolver.caltech.edu/CaltechETD:etd-07232003-012018
Title	Spike train characterization and decoding for neural prosthetic devices
Degree	PhD
Option	Mechanical Engineering
Advisory Committee	Advisor Name Title Joel Burdick Committee Chair Ken Pickar Committee Member Melany Hunt Committee Member Richard Andersen Committee Member
Keywords	neuroscience prosthetic decode wavelet Spike
Date of Defense	2003-07-01
Availability	unrestricted
Abstract Neural prosthetic device has the potential of benefiting millions of lock-in and spinal cord injury survivors. One branch of the ongoing research is to construct reach movement based prosthetic devices. This thesis proposes statistical methods based on applying the Haar wavelet packets to spike trains in order to answer some of the questions in this field. Although spike train is the most frequently used data in the neural science community, its stochastic properties are not fully understood or characterized. This thesis suggests a formal spike train characterization method using the Haar wavelet packet. Because of the multi-scale property of the wavelet packet, Poisson characteristics at different scales can be assessed. Moreover, Poisson Scale-gram is proposed to help visualize the characteristics of the spike train at different scales. Because some neurons display non-Poisson characteristics, it is necessary to extract the relevant features from spike trains in the context of decoding. The thesis suggests a feature extraction method that searches all the wavelet packet coefficients for the ones with the largest discriminability, quantified by mutual information. This technique returns the most informative feature(s) in the context of the Bayesian classifier. Decoding performance of this proposed method is compared against the one using mean firing rate only on both surrogate data and the actual data from PRR. It is also crucial to decode cognitive states because they provide the extra control signals necessary for practical implementation of the prosthetic devices. This thesis proposes a simple finite state machine approach along with an interpreter that interprets the decoding results and to regulate when the transition should occur. It demonstrates that the finite state machine framework, when coupled with the interpreter, offers a simple autonomous control scheme for the neuron prosthetic system envisioned. While the neural prosthetic system is in its infancy, many theoretical and experimental works lay the foundation for a bright future in this field. This thesis answers the spike train characterization and decoding questions in a theoretical manner while offering several novel techniques that bring new ideas and insights into the research field.
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