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BL
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| Record Number
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850129
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| Main Entry
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Sanchez, Justin Cort
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| Title & Author
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Neuroprosthetics : : principles and applications /\ Justin C. Sanchez.
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| Publication Statement
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Boca Raton :: CRC Press, Taylor & Francis Group,, [2016]
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| Series Statement
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Rehabilitation science in practice series
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| Page. NO
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1 online resource :: text file, PDF.
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| ISBN
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1466553243
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: 1466553251
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: 9781466553248
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: 9781466553255
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9781466553231 (hbk.)
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| Bibliographies/Indexes
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Includes bibliographical references (pages 203-227) and index.
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| Contents
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10.2.1. Fundamentals of effective DBS-first-generation approaches -- 10.2.1.1. Lead localization -- 10.2.1.2. Trial-and-error programming -- 10.2.1.3. Schedule of stimulation -- 10.3. Shifts in research/practice paradigms -- 10.3.1. Prototype methods and interventions-second-generation DBS -- 10.4. Second-generation experimental paradigms-application of DBS for Tourette syndrome -- 10.4.1.Comorbidities and pathology -- 10.4.2. Basal ganglia and repetitive behaviors -- 10.4.3. CM thalamus -- 10.4.4. New experimental test beds -- 10.4.5. Physiological biomarkers of TS -- 10.4.6. Surgery and subjects -- 10.4.7. Lead localization -- 10.4.8. Data collection -- 10.4.9. Clinical assessment -- 10.4.10. Characterization of power spectral density -- 10.4.11. Correlation of gamma with clinical improvement -- 10.5. Conclusion -- Exercise.
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3.5. Biopotential amplifiers -- 3.6. Filtering -- 3.6.1. Filter design -- 3.6.1.1. Pass band and stop band -- 3.6.1.2. Filter order -- 3.6.1.3. Finite impulse response and infinite impulse response filters -- 3.6.1.4. Hardware versus digital filters -- 3.7. Adaptive filters -- 3.8. Conclusion -- Exercises -- Learning objectives -- 4.1. Introduction -- 4.2. Targeting -- 4.2.1. Targeting example -- 4.2.1.1. Hand and arm region of the primary motor cortex -- 4.2.1.2. Nucleus accumbens and ventral striatum -- 4.2.1.3. Targeting in humans -- 4.3. Surgical methods for implantation -- 4.3.1. Surgery preparation and sterilization -- 4.3.2. Preparation of the operating theater -- 4.3.3. Surgery preparation: Physiological monitoring and anesthesia -- 4.3.4. Stereotaxic frame -- 4.3.5. Surgery and defining the layout -- 4.3.6. Cortical and subcortical mapping -- 4.3.7. Electrode driving -- 4.3.7.1. Microwires -- 4.3.7.2. Microprobes (FMA electrodes) -- 4.3.7.3. Blackrock (Utah electrodes).
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4.3.7.4. ECoG -- 4.3.8. Grounding, closing, and postoperative recovery -- 4.4. Surgical methods for perfusion -- 4.5. Surgical methods for explantation -- Exercise -- Learning objectives -- 5.1. Introduction -- 5.2. Morphological properties -- 5.2.1. Scanning electron microscopy imaging -- 5.2.2. Imaging observations -- 5.3. Electrical properties -- 5.3.1. Impedance testing procedure -- 5.3.2. Recording procedure -- 5.3.3. Impedance and electrophysiological observations -- 5.3.4. Probabilistic model for unit detection -- 5.4. Tissue properties -- 5.4.1. Histopathology -- 5.4.2.lmmunohistochemical procedures -- 5.4.3. Observation and imaging -- 5.4.4. Quantitative morphometry -- 5.4.5. Histopathologic analysis -- 5.4.6. Cerebrospinal fluid and blood collection -- 5.4.7. Biochemical analysis -- 5.5. Holistic abiotic and biotic analysis -- 5.6. Conclusion -- Exercises -- Learning objectives -- 6.1. Introduction -- 6.2. Evolution of decoders.
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6.2.1. Assumptions that influence neural decoding -- 6.2.2. Model generalization -- 6.3. Extracting neural features as control signals -- 6.3.1. Mistaking noise for neural signals -- 6.3.2. Example of neural features -- 6.3.2.1. LFP spectrograms -- 6.3.2.2. Spike rates and spike-LFP coherence -- 6.3.2.3. Principal component analysis -- 6.3.2.4. Event-related potentials -- 6.4. Examples of neuroprosthetic decoders -- 6.4.1. Fundamental example-Wiener decoder -- 6.4.1.1. Decoding hand trajectory -- 6.4.2. Advanced example-reinforcement learning decoder -- 6.4.2.1. Subjects and neural data acquisition -- 6.4.2.2. Actor-critic RLBMI control architecture -- 6.4.2.3. Neural-controlled robot reaching task -- 6.4.2.4. RLBMI stability when initialized from random initial conditions -- 6.4.2.5. RLBMI stability during input space perturbations: loss or gain of neuron recordings -- 6.4.2.6. RLBMI decoder stability over long periods -- 6.4.2.7. Actor-critic RLBMI control of robot arm.
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6.4.2.8. RLBMI performance during input space perturbations -- 6.4.2.9. Potential benefits of using RL algorithms for BMI decoders -- 6.4.2.10. The RL decoder does not require explicit training data -- 6.4.2.11. The RL decoder remains stable despite perturbations to the neural input -- 6.4.2.12. Obtaining and using feedback for RLBMI adaptation -- Exercises -- Learning objectives -- 7.1. Introduction -- 7.2. Nerve responses to electrical current -- 7.3. Strength-duration curves -- 7.3.1. Activation order -- 7.3.2. Adaptation -- 7.4. Current flow -- 7.4.1. Current density -- 7.4.2. Electrode size -- 7.4.3. Tissue impedance -- 7.4.4. Electrode arrangements -- 7.5. Current types -- 7.5.1. Stimulation-induced tissue damage -- 7.5.2. Pulsed currents -- 7.5.3. Effects of pulsed current types -- 7.6. Example applications -- Exercises -- Learning objectives -- 8.1. Introduction -- 8.2. Hand rehabilitation strategies -- 8.3. Fundamentals of functional electrical stimulation.
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8.4. Functional outcome measures -- 8.5. An exemplar of closed-loop neuroprosthetic control of FES -- 8.5.1. Study participants -- 8.5.2. Behavioral function -- 8.5.3. Neural data acquisition -- 8.5.4. Muscle stimulation -- 8.5.5. Actor-critic reinforcement learning architecture -- 8.5.6. Adaptive BCI usage -- 8.5.7. Critic as error potential classifier -- 8.6. Closed-loop trials -- 8.6.1. Performance over time -- 8.6.2.Comparison of performance across subjects -- 8.7. Conclusion -- Exercises -- Learning objectives -- 9.1. Introduction -- 9.2. Design -- 9.2.1. Effect of signal choice -- 9.2.2. Three primary constraints -- 9.2.3. Implant location and form factor -- 9.2.4. Fabrication and construction -- 9.2.5. Hermetic packaging -- 9.2.6. Considerations for battery recharging and telemetry -- 9.3. Safety -- 9.3.1. Device interactions -- Exercises -- Learning objectives -- 10.1. Introduction -- 10.2. DBS as a foundational technology.
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Learning objectives -- 1.1. Introduction -- 1.2. Design thinking -- 1.3. Inspiration for neuroprosthetic design -- 1.4. Prototypical example of neuroprosthetic design thinking -- 1.4.1. Expert interviews -- 1.4.2. User observations -- 1.4.2.1. The assisted kitchen -- 1.4.2.2. Memory assist -- 1.4.3. Synthesis -- Exercise -- Learning objectives -- 2.1. Introduction -- 2.2. Electrical interfaces -- 2.2.1. The interface problem -- 2.2.2. Half-cell potential -- 2.2.3. Electrode polarization -- 2.2.4. Electrode circuit model -- 2.2.5. Noise sources -- 2.2.6. Tricks of the trade -- 2.3. Electrode design -- 2.3.1. Examples of neuroprosthetic electrode arrays -- 2.3.1.1. Microwire arrays -- 2.3.1.2. Planar micromachined -- 2.3.1.3.2-D bulk micromachined -- 2.3.1.4. Electrocorticogram electrodes -- 2.3.1.5. Depth electrodes -- Exercises -- Learning objectives -- 3.1. Introduction -- 3.2. Use of sensors -- 3.3. What is a signal? -- 3.4. What is noise?
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Neural stimulation.
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Neuroprostheses-- Design and construction.
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HEALTH FITNESS / Diseases / General
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MEDICAL / Clinical Medicine
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MEDICAL / Diseases
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MEDICAL / Evidence-Based Medicine
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MEDICAL / Internal Medicine
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Neural stimulation.
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| Dewey Classification
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616.8046
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| LC Classification
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R856.S323 2016
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