Wolf, Michael T. (2008) Target tracking using clustered measurements, with applications to autonomous brain-machine interfaces. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechETD:etd-05292008-105504
This thesis presents new methods for classifying and tracking the signals of targets that produce clusters of observations, measured in successive recording intervals or scans. This multitarget tracking problem arises, for instance, in extracellular neural recordings, in which an electrode is inserted into the brain to detect the spikes of individual neurons. Since multiple active neurons may lie near the electrode, each detected spike must be assigned to the neuron that produced it, a task known as spike sorting. In the scenario considered in this thesis, the electrode signal is sampled over many brief recording intervals. In each recording interval, all spikes must first be clustered according to their generating neurons, and then each cluster must be associated to clusters from previous recording intervals, thus tracking the signals of putative neuron “targets.”
This thesis introduces a novel multitarget tracking solution for the above problem, called multiple hypothesis tracking for clusters (MHTC). The MHTC algorithm has two main parts: a Bayesian clustering algorithm for associating observations to clusters in each interval and a probabilistic supervisory system that manages association hypotheses across intervals. The clustering procedure provides significantly more consistent results than previously available methods, enabling more accurate tracking of targets over time. Such consistency is promoted by a maximum a posteriori (MAP) approach to optimizing a Gaussian mixture model via expectation-maximization (EM), in which information from the preceding intervals serves as a prior for the current interval while still allowing the number and locations of targets to change. MHTC's hypothesis management system, like that of traditional multiple hypothesis tracking (MHT) algorithms, propagates various possibilities for how to assign measurements to existing targets and uses a delayed decision-making logic to resolve data association ambiguities. It also, however, maintains several options, termed model hypotheses, for how to cluster the observations of each interval. This combination of clustering and tracking in a single solution enables MHTC to robustly maintain the identities of cluster-producing targets in challenging recording scenarios.
In addition to these classification and tracking techniques, this thesis presents advances in a miniature robotic electrode microdrive capable of extracellular recordings lasting for days at a time. As a whole, these contributions can play an important role in enabling an autonomous neural interface, which, by frequent automatic repositioning of its recording electrodes, can optimize the recording quality of extracellular signals associated with individual neurons and maintain high quality recordings for long periods of time. Such autonomous movable electrodes may eventually overcome key barriers to engineering a practical neuroprosthetic device and, in the near term, can significantly improve state-of-the-art neuroscience experimental procedures.
|Item Type:||Thesis (Dissertation (Ph.D.))|
|Subject Keywords:||BMI; classification; clustering; extracellular recording; MHT; multiple hypothesis tracking; neuroprosthetics; spike sorting|
|Degree Grantor:||California Institute of Technology|
|Division:||Engineering and Applied Science|
|Major Option:||Mechanical Engineering|
|Thesis Availability:||Public (worldwide access)|
|Defense Date:||16 May 2008|
|Default Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Imported from ETD-db|
|Deposited On:||02 Jun 2008|
|Last Modified:||26 Dec 2012 02:49|
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