Performance limits of brain machine interfaces

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dc.contributor.advisorJohnson, Don H.en_US
dc.creatorGoodman, Ilan N.en_US
dc.date.accessioned2011-07-25T02:04:54Zen_US
dc.date.available2011-07-25T02:04:54Zen_US
dc.date.issued2010en_US
dc.description.abstractUnderstanding the constraints governing information transfer between electrodes and neurons is crucial to the effective design of neural prostheses. In sensory prostheses such as cochlear implants, information is transferred to the brain by stimulating neurons to produce sensation. In motor prostheses such as cortically controlled bionic limbs, neural recordings are processed to extract information needed to control a computer or mechanical device. In each case, performance of the prosthesis hinges on how effectively information can be conveyed to or from the device at the interface between brain and machine. In this thesis, we investigate the performance capabilities and constraints of brain machine interfaces (BMIs) using an information theoretic approach. Modeling the BMI as a vector Poisson process channel, we compute the information capacity of several different types of BMI channels. Since capacity defines the ultimate fidelity limits of information transmission by any system, this approach gives us an objective way of evaluating and comparing different types of BMIs by determining the best possible performance of each system given its unique constraints. For stimulation BMIs, we examine how the capacity of the system scales with the number of inputs, the constraints on the inputs, and inter-neuronal dependencies. For control BMIs, we quantify the loss in performance that results from using extracellular recordings, where signals from multiple neurons are received on a single electrode. This performance loss can be mitigated through spike sorting, and we show how the properties of the spike sorting algorithm have direct consequences for the resulting BMI capacity. We also provide extensions to the basic models to account for signal attenuation, cross-talk, and measurement noise. Finally, we discuss the real-world significance of BMI capacity in the context of Rate-Distortion Theory, and interpret the capacity results using performance criteria that are relevant to BMIs. This framework provides a direct way to compare competing systems, and allows us to make predictions about the specific conditions necessary for a BMI to achieve a desired performance level.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.callnoTHESIS E.E. 2010 GOODMANen_US
dc.identifier.citationGoodman, Ilan N.. "Performance limits of brain machine interfaces." (2010) Diss., Rice University. <a href="https://hdl.handle.net/1911/61988">https://hdl.handle.net/1911/61988</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/61988en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectBiologyen_US
dc.subjectNeurosciencesen_US
dc.subjectElectronicsen_US
dc.subjectElectrical engineeringen_US
dc.titlePerformance limits of brain machine interfacesen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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