Somatosensory Feedback for Neuroprosthetics

Front Cover
Somatosensory Feedback for Neuroprosthetics
Copyright Page
Dedication
Contents
List of contributors
Preface
I. Background and fundamentals
	1 Introduction to somatosensory neuroprostheses
		1.1 Scope and history of neuroprostheses
		1.2 Classification of neuroprostheses
		1.3 Basic components of the somatosensory system
			1.3.1 Somatosensory receptors and afferent nerves
			1.3.2 Central pathways and cortical areas
			1.3.3 Psychophysical processing and perception
		1.4 Overview of somatosensory neuroprostheses
			1.4.1 Noninvasive methods for feedback
				1.4.1.1 Vibrotactile stimulation
				1.4.1.2 Electrotactile stimulation
			1.4.2 Invasive methods for feedback
				1.4.2.1 Peripheral nerve stimulation
				1.4.2.2 Brain cortex stimulation
		1.5 Multidisciplinary approach and future directions
		Acknowledgments
		References
	2 Proprioception: a sense to facilitate action
		2.1 Introduction
		2.2 Sensors contributing to proprioception
			2.2.1 Muscle spindles
			2.2.2 Golgi tendon organs
		2.3 Proprioceptive coding along the cerebral cortical pathway
			2.3.1 Dorsal column pathway
			2.3.2 Thalamic proprioceptive encoding
			2.3.3 Somatosensory cortex
		2.4 Somato-motor connections and control of proprioceptive feedback
			2.4.1 Spinal reflexes
			2.4.2 Longer latency reflexes and sensorimotor connections
			2.4.3 Top-down modulation of proprioceptive signals
				2.4.3.1 Control of the fusimotor system
				2.4.3.2 Neural sensory gain modulation
		2.5 Cerebellar involvement in proprioception
			2.5.1 Cerebellar afferent pathway
			2.5.2 Sensorimotor adaptation
		2.6 Summary
		References
	3 Electrodes and instrumentation for neurostimulation
		3.1 Two fundamental requirements
		3.2 Recording and stimulating
		3.3 Requirements for efficacy and safety of a stimulating device
		3.4 Electrical model of stimulation: the electrode–tissue interface
			3.4.1 Physical basis of the electrode–tissue interface
			3.4.2 Capacitive/non-Faradaic charge transfer
			3.4.3 Faradaic charge transfer and the electrical model of the electrode–electrolyte interface
			3.4.4 Reversible and irreversible Faradaic reactions
			3.4.5 The origin of electrode potentials and the three-electrode electrical model
			3.4.6 Faradaic processes: quantitative description
			3.4.7 Charge injection during electrical stimulation: interaction of capacitive and Faradaic mechanisms
			3.4.8 Common waveforms used in neural stimulation
			3.4.9 Pulse train response and ratcheting
			3.4.10 Electrochemical reversal
		3.5 Introduction to extracellular stimulation of excitable tissue
			3.5.1 Cathodic and anodic stimulation
			3.5.2 Exploiting the voltage-gated sodium channel
			3.5.3 Quantifying action potential initiation
			3.5.4 Bipolar configurations; voltage-controlled stimulation
		3.6 Mechanisms of damage
			3.6.1 Tissue damage from intrinsic biological processes
			3.6.2 Tissue damage from electrochemical reaction products
			3.6.3 Multiple contributing factors
		3.7 Design compromises for efficacy and safety
		3.8 Requirements for efficacy and safety of a recording device
		3.9 Electrical model of the recording electrode
		3.10 Materials used for stimulating and recording electrodes
		3.11 Instrumentation
			3.11.1 Stimulation parameters of interest
			3.11.2 Recording architecture and parameters of interest
			3.11.3 Noise
			3.11.4 Common mode rejection
			3.11.5 Loading and impedance
		References
	4 Stimulus interaction in transcutaneous electrical stimulation
		4.1 Introduction
		4.2 User opinions on sensory feedback
		4.3 The role of sensory feedback in motor control
			4.3.1 Control policy
			4.3.2 Efferent copy
			4.3.3 Signal noise
			4.3.4 Implications
		4.4 Physiology of sensory feedback
			4.4.1 Mechanoreceptors
			4.4.2 Stimulus interaction
		4.5 Event-related feedback in upper-limb prosthetics
		4.6 Optimizing event-related feedback strategies
			4.6.1 Testing the internal model
			4.6.2 Effect of stimulation pattern
			4.6.3 Testing stimulus interaction
				4.6.3.1 Methods
				4.6.3.2 Results
				4.6.3.3 Implications for prosthetic control
		4.7 Conclusion
		References
II. Non-invasive methods for somatosensory feedback and modulation
	5 Supplementary feedback for upper-limb prostheses using noninvasive stimulation: methods, encoding, estimation-prediction ...
		5.1 Motivation
		5.2 Restoration of somatosensory feedback
		5.3 Encoding feedback variables using multichannel electrotactile stimulation
		5.4 Feeding back the command signal as opposed to its consequences
		5.5 Feedback can support predictive and corrective strategies
		5.6 Evaluating the role of feedback in the state estimation process
		5.7 Concluding remarks
		Acknowledgments
		References
	6 Noninvasive augmented sensory feedback in poststroke hand rehabilitation approaches
		6.1 Introduction: sensory information in hand motor performance
			6.1.1 Upper limb impairment
			6.1.2 Sensorimotor control of the upper limb
			6.1.3 Sensory input for optimal movement
			6.1.4 Augmented feedback to stimulate neural plasticity
		6.2 Current rehabilitation techniques
			6.2.1 Approach to rehabilitation
			6.2.2 Constraint-induced movement therapy
			6.2.3 Mirror therapy
			6.2.4 Robot-assisted therapy
		6.3 Augmented sensory feedback
			6.3.1 Aspects of feedback
			6.3.2 Feedback modalities
			6.3.3 Strategies for error feedback
			6.3.4 Developing a reliance on extrinsic feedback
			6.3.5 The sensory side of rehabilitation is an open question
			6.3.6 Auditory feedback
				6.3.6.1 Relevance of auditory information in motor learning
				6.3.6.2 Types of augmented auditory feedback
				6.3.6.3 Auditory feedback devices
					6.3.6.3.1 Improvements in motor performance
					6.3.6.3.2 Improvements in sensory awareness
				6.3.6.4 Conclusions on auditory sensory feedback
			6.3.7 Visual feedback
				6.3.7.1 Relevance of visual information in motor learning
				6.3.7.2 Benefits of virtual reality rehabilitation
				6.3.7.3 General features of a virtual reality setup
					6.3.7.3.1 Movement representation
					6.3.7.3.2 Interaction with objects during task performance/training
					6.3.7.3.3 Kinematic features recording
				6.3.7.4 Studies in virtual reality for rehabilitation purposes
				6.3.7.5 Other visual feedback delivery methods
				6.3.7.6 Conclusions on visual feedback
			6.3.8 Haptic feedback
				6.3.8.1 Relevance of haptic information in motor learning
				6.3.8.2 Movement-based (implicit) and sensory-based (explicit) haptic feedback
					6.3.8.2.1 Implicit haptic feedback
					6.3.8.2.2 Explicit haptic feedback: kinesthetic and tactile
					6.3.8.2.3 Feedback for kinesthetic illusion
				6.3.8.3 Devices for haptics
					6.3.8.3.1 Types of augmented haptic stimulation
					6.3.8.3.2 Vibrotactile sensory substitution
					6.3.8.3.3 Proprioceptive feedback
					6.3.8.3.4 Dynamic and performance feedback
				6.3.8.4 Conclusions on haptic feedback
			6.3.9 Multimodal feedback
				6.3.9.1 Multisensory integration in the human brain
				6.3.9.2 Studies on multimodal feedback
					6.3.9.2.1 Visual and haptic feedback
					6.3.9.2.2 Visual and auditory feedback
					6.3.9.2.3 Combination of visual, haptic, and auditory feedback
				6.3.9.3 Conclusions on multimodal feedback
			6.3.10 Sensory information enhancement
				6.3.10.1 Vagus nerve stimulation
				6.3.10.2 Stochastic resonance
					6.3.10.2.1 Optimal noise may benefit rehabilitation
					6.3.10.2.2 Studies on stochastic resonance for rehabilitation
					6.3.10.2.3 Possible implications in feedback evaluations
				6.3.10.3 Conclusion on sensory enhancement
		6.4 Future directions for augmented feedback
		References
	7 Targeted reinnervation for somatosensory feedback
		7.1 Introduction
		7.2 Targeted reinnervation surgery and mechanisms of somatosensory restoration
		7.3 Cutaneous reinnervation: tactile sensation
			7.3.1 Neurophysiology of cutaneous targeted sensory reinnervation
			7.3.2 Functional use of cutaneous sensory reinnervated sites
			7.3.3 The importance of matched feedback: embodiment
			7.3.4 Variability in cutaneous reinnervation
			7.3.5 State of technology for providing haptic feedback
		7.4 Muscle sensory reinnervation: kinesthesia
		7.5 Neuropathic pain
		7.6 Conclusion
		References
	8 Transcranial electrical stimulation for neuromodulation of somatosensory processing
		8.1 Introduction
			8.2 Chapter objectives
			8.3 Methods of transcranial electrical stimulation and mechanism of action
				8.3.1 Transcranial direct current stimulation
				8.3.2 Transcranial alternating current stimulation
				8.3.3 Transcranial random noise stimulation
				8.3.4 Transcranial pulsed current stimulation
			8.4 Experiment results and discussion
				8.4.1 Neuromodulation of somatosensory processing by transcranial electrical stimulation
					8.4.1.1 Modulation of tactile senses and haptic perception
					8.4.1.2 Modulation of proprioception
					8.4.1.3 Sensory modulation in stroke patients
				8.4.2 Modulating multisensory integration
		8.5 Future opportunities
		8.6 Conclusions
		References
III. Peripheral nerve implants for somatosensory feedback
	9 Connecting residual nervous system and prosthetic legs for sensorimotor and cognitive rehabilitation
		9.1 Introduction
		9.2 Intraneural electrodes
			9.2.1 Implantable electrodes
			9.2.2 Surgical procedure
		9.3 Intraneural electrical stimulation
			9.3.1 Characterization of the electrically evoked sensation
			9.3.2 Neuroprosthetic leg
			9.3.3 Sensory encoding strategy
			9.3.4 Sensorimotor integration
			9.3.5 Cognitive integration
			9.3.6 Health benefits
		9.4 Conclusions
		References
	10 Biomimetic bidirectional hand neuroprostheses for restoring somatosensory and motor functions
		10.1 Introduction
		10.2 Mechanoreceptors and somatosensory pathways
		10.3 Neural interfaces
		10.4 Neural stimulation
		10.5 Closed-loop system
		10.6 Encoding strategies
			10.6.1 Linear modulation
			10.6.2 Amplitude modulation
			10.6.3 Frequency modulation
			10.6.4 Biomimetic stimulation
		10.7 Neuron models
		10.8 Model-based approaches
		10.9 Challenges for bidirectional sensory and motor function restoration
			10.9.1 Artifact removal for bidirectional neural systems
		10.10 Conclusions
		References
IV. Cortical implants for somatosensory feedback
	11 Restoring the sense of touch with electrical stimulation of the nerve and brain
		11.1 Introduction
			11.1.1 The importance of touch in manual behavior
			11.1.2 Electrical activation of neurons
			11.1.3 Neural coding—the language of the nervous system
		11.2 Neural basis of touch
			11.2.1 Tactile innervation of the skin
			11.2.2 Medial lemniscal pathway
			11.2.3 Somatosensory cortex
		11.3 Electrical interfaces with the nervous system
			11.3.1 Targets of neural interfaces
			11.3.2 Interface hardware—peripheral
			11.3.3 Interface hardware—central
		11.4 Shaping artificial touch sensations
			11.4.1 Contact location—leveraging somatotopic maps
			11.4.2 Contact pressure
			11.4.3 Timing of contact events
			11.4.4 Sensory quality
		11.5 Future horizons
		References
	12 Intracortical microstimulation for tactile feedback in awake behaving rats
		12.1 Introduction
		12.2 Behavioral instrumentation and training schedule
		12.3 Vibrotactile detection experiments
		12.4 Intracortical microstimulation in rats
		12.5 Psychophysical correspondence between sensations elicited by vibrotactile and electrical stimulation
		12.6 Validation of psychometric equivalence functions
		12.7 Behavioral demonstration of a tactile neuroprosthesis in rats
		12.8 Conclusions
		Acknowledgment
		References
	13 Cortical stimulation for somatosensory feedback: translation from nonhuman primates to clinical applications
		13.1 Introduction
		13.2 A brief history of somatosensory neuroprosthetics with nonhuman primates
		13.3 Why nonhuman primates are a pertinent model for the development of somatosensory neuroprosthetics
		13.4 How nonhuman primate studies can help engineer somatosensory neuroprosthetics
			13.4.1 Development of cortical implants
			13.4.2 Somatosensory feedback encoding
			13.4.3 Validation of computational models
		13.5 Experimental setups for somatosensory studies with nonhuman primates
			13.5.1 Cortical and intracortical electrical stimulation
			13.5.2 Somatosensory inputs
			13.5.3 Visual inputs
			13.5.4 Behavioral tracking
		13.6 Conclusion
		References
	14 Touch restoration through electrical cortical stimulation in humans
		14.1 Introduction
			14.1.1 Advantages of cortical stimulation
			14.1.2 Current clinical uses of direct cortical stimulation
			14.1.3 History of direct cortical stimulation
			14.1.4 Direct cortical stimulation and perception in humans
		14.2 Stimulation physiology
			14.2.1 Sensory processing physiology
			14.2.2 Activation of the tactile sensory system via electrical stimulation
		14.3 Direct cortical stimulation for sensory feedback and neuroprosthetic control
			14.3.1 The perception and psychophysics of direct cortical stimulation
			14.3.2 Primary somatosensory cortex direct cortical stimulation parameters and perception
				14.3.2.1 Perception
				14.3.2.2 Amplitude
				14.3.2.3 Pulse width
				14.3.2.4 Pulse frequency
				14.3.2.5 Charge
				14.3.2.6 Train duration
				14.3.2.7 Novel stimulation waveforms
			14.3.3 Percept localization
			14.3.4 Brain state, attention, and perception
			14.3.5 Response times
			14.3.6 Sensory ownership and the rubber hand illusion
			14.3.7 Use of primary somatosensory cortex direct cortical stimulation as task feedback
		14.4 Future advances in cortical sensory stimulation
			14.4.1 More channels
			14.4.2 Concurrent stimulation and recording
			14.4.3 Wireless technologies
		14.5 Conclusion
		References
	15 Design of intracortical microstimulation patterns to control the location, intensity, and quality of evoked sensations i...
		15.1 Introduction
		15.2 Stimulation design
			15.2.1 Historical experiments
			15.2.2 Electrical effects on neurophysiology
		15.3 Parameterization
			15.3.1 Sensory brain–machine interfaces
			15.3.2 Biomimetic stimulation pattern design
			15.3.3 Sensory substitution stimulation
			15.3.4 Charge
		15.4 Applications in human participants
			15.4.1 Cortical surface stimulation
			15.4.2 Intracortical microstimulation
		15.5 Bidirectional brain–machine interfaces
		15.6 Conclusion
		References
V. Future technologies
	16 Neural electrodes for long-term tissue interfaces
		16.1 Introduction
		16.2 Peripheral nerve electrodes
			16.2.1 Surface electrodes
			16.2.2 Extraneural electrodes
				16.2.2.1 Cuff electrodes
				16.2.2.2 Flat interface nerve electrode
				16.2.2.3 Other extraneural electrodes
			16.2.3 Intraneural electrodes
				16.2.3.1 Longitudinal intrafascicular electrodes
				16.2.3.2 Transverse intrafascicular multichannel electrodes
				16.2.3.3 Multielectrode arrays
			16.2.4 Regenerative electrodes
		Acknowledgments
		References
	17 Challenges in neural interface electronics: miniaturization and wireless operation
		17.1 Introduction
		17.2 Important aspects of neural interface electronics
			17.2.1 Microelectrode array
			17.2.2 Data acquisition
			17.2.3 Stimulation
			17.2.4 Integrated processing on chip
			17.2.5 Communication
			17.2.6 Power management
		17.3 RF solutions for wireless power transfer
		17.4 Optical solutions for wireless power transfer
			17.4.1 Optical penetration depths for biological tissue for different wavelengths
			17.4.2 Laser power limitations for skin
		17.5 Ultrasonic solutions for wireless power transfer
		17.6 Conclusion
		References
	18 Somatosensation in soft and anthropomorphic prosthetic hands and legs
		18.1 Introduction
		18.2 Soft and anthropomorphic prostheses
			18.2.1 Upper limb prostheses
			18.2.2 Lower limb prostheses
		18.3 Sensing techniques in prostheses
			18.3.1 Sensing techniques
				18.3.1.1 Prosthetic sensors
				18.3.1.2 Electronic skins
			18.3.2 Applications in upper limb prostheses
			18.3.3 Applications in lower limb prostheses
		18.4 Outlook and future directions
		References
	19 Prospect of data science and artificial intelligence for patient-specific neuroprostheses
		19.1 Introduction
		19.2 Classical machine learning methods for neuroprosthetic applications
			19.2.1 Probability theory and evaluation metrics for machine learning models
				19.2.1.1 Probability theory
				19.2.1.2 Bias and variance
				19.2.1.3 The evaluation metrics
			19.2.2 Feature selection techniques
			19.2.3 Logistic regression
			19.2.4 k-Nearest neighbor classifier
			19.2.5 Support vector machines
			19.2.6 Decision trees
			19.2.7 Ensemble methods
			19.2.8 Reinforcement learning
			19.2.9 Artificial neural networks
		19.3 Deep learning methods for neuroprosthetic applications
			19.3.1 Convolutional neural networks
			19.3.2 Recurrent neural networks
		19.4 Conclusion
		References
	20 Modern approaches of signal processing for bidirectional neural interfaces
		20.1 Signal processing in neural signal recording
			20.1.1 Generalized signal processing workflow
			20.1.2 Preprocessing
				20.1.2.1 Denoising of the signal
				20.1.2.2 Running observational window analysis
				20.1.2.3 Feature extraction and selection
				20.1.2.4 Features for classification
				20.1.2.5 Feature extraction and selection for clustering
			20.1.3 Spike detection
				20.1.3.1 Amplitude thresholding
				20.1.3.2 Template matching
				20.1.3.3 Energy-based spike detection
				20.1.3.4 Wavelet-based spike detection
				20.1.3.5 Feature selection
			20.1.4 Classification and clustering
				20.1.4.1 Classification
				20.1.4.2 Clustering
				20.1.4.3 Combining classification and clustering
		20.2 Signal processing in neural stimulation
			20.2.1 Processing through modeling
				20.2.1.1 Parametric stimulus encoding
				20.2.1.2 Nonparametric stimulus encoding
		20.3 Closing the loop
		References
	21 Safety and regulatory issues for clinical testing
		21.1 Relationships of quality, regulatory, safety, and testing with clinical studies
		21.2 Medical device lifecycle phases and design control
		21.3 Verification and validation testing
		21.4 Regulatory paths for clinical studies in the United States
		21.5 Regulatory paths for device commercialization in the United States
		21.6 Comparison of European Union and United States regulatory processes
			21.6.1 Clinical studies in the European Union
			21.6.2 Device commercialization in the European Union
		References
Index
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