Brain Machine Interfaces（PPT讲稿）Modeling Strategies for Neural Signal Processing

Brain machine Interfaces: Modeling strategies for Neural Signal Processing Jose C. Principe, Ph D Distinguished Professor ECE, BME Computational NeuroEngineering Laboratory Electrical and computer Engineering Department University of Florida www.cnel.ufledu principe@cnel ufl. edu CINEL eering Lab

Brain Machine Interfaces: Modeling Strategies for Neural Signal Processing Jose C. Principe, Ph.D. Distinguished Professor ECE, BME Computational NeuroEngineering Laboratory Electrical and Computer Engineering Department University of Florida www.cnel.ufl.edu principe@cnel.ufl.edu

Brain Machine Interfaces (BMI) x a man made device that either substitutes a sensory input to the brain, repairs functional communication between brain regions or translates intention of movement

Brain Machine Interfaces (BMI) A man made device that either substitutes a sensory input to the brain, repairs functional communication between brain regions or translates intention of movement

Types of BMIs x Sensory(Input BMI): Providing sensory input to form percepts when natural systems are damaged EX: Visual, Auditory Prosthesis x Motor(Output BMD): Converting motor intent to a command output (physical device, damaged limbs) EX: Prosthetic arm control *K Cognitive BMI: Interpret internal neuronal state to deliever feedback to the neural population EX: Epilepsy, DBS Prosthesis Computational Neuroscience and Technology developments are playing a larger role in the development of each of these areas

Types of BMIs Sensory (Input BMI): Providing sensory input to form percepts when natural systems are damaged. Ex: Visual, Auditory Prosthesis Motor (Output BMI): Converting motor intent to a command output (physical device, damaged limbs) Ex: Prosthetic Arm Control Cognitive BMI: Interpret internal neuronal state to deliever feedback to the neural population. Ex: Epilepsy, DBS Prosthesis Computational Neuroscience and Technology developments are playing a larger role in the development of each of these areas

General Architecture BCl(BMI) bypasses the brain's normal pathways of peripheral nerves(and muscles BCI SYSTEM SIGNAL PROCESSING SIGNAL IGITTZEI eature Translation DEVICE ACQUISITION Extraction Algorithm COMMANDS J.R Wolpaw et al. 200 Nature Reviews Neuroscience

J.R. Wolpaw et al. 2002 BCI (BMI) bypasses the brain’s normal pathways of peripheral nerves (and muscles) General Architecture

The Fundamental Concept BRAI MACHINE INTENT ACTION Decoding PERCEPT STIMULUS Coding Neural interface Physical Interface Need to understand how brain processes information Stimulus Neural Response Coding Given To be inferred Decoding To be inferred Given

INTENT PERCEPT ACTION STIMULUS Decoding Coding BRAIN MACHINE Neural Interface Physical Interface The Fundamental Concept Stimulus Neural Response Coding Given To be inferred Decoding To be inferred Given Need to understand how brain processes information

Levels of Abstraction for Neurotechnology 紫 Brain is an extremely 1 m CNS complex system Syst 紫1012 neurons x 10 synapses 1 clAps x Specific 1 mmNetworks interconnectivity Neurons Synapse = H2N-C-C-OH Molecules R

Levels of Abstraction for Neurotechnology Brain is an extremely complex system 1012 neurons 1015 synapses Specific interconnectivity

Tapping into the Nervous System xx The choice and availability of brain signals and recording methods can greatly influence the ultimate performance of the BMI The level of BMI performance may be attributed to selection of electrode technology, choice of model, and methods for extracting rate, frequency, or timing codes

Tapping into the Nervous System The choice and availability of brain signals and recording methods can greatly influence the ultimate performance of the BMI. The level of BMI performance may be attributed to selection of electrode technology, choice of model, and methods for extracting rate, frequency, or timing codes

Spatial and Temporal Scales of Neural signals on-lnvasIvc Invasive MRI -103 neurons Optical imaging EEG 102 101 10 03 LFP 10 Spikes MEG Ce出hi SPACE Fine(microns) ( indep signals) http:/ida.firstfhg.de/projects/bci/bbciofficiall

http://ida.first.fhg.de/projects/bci/bbci_official/ Coarse(mm)

Choice of Scale for Neuroprosthetics Bandwidth ocalization (approximate) Scalp 0~80Hz Volume Electrodes Conduction Cortical surface Electro 0-500Hz Cortical Surface corticogram (ECoG) Implanted 0-7KHz Single Neuron Electrodes 1 mm

Choice of Scale for Neuroprosthetics Bandwidth (approximate) Localization Scalp Electrodes 0 ~ 80 Hz Volume Conduction Cortical Surface Electro￾corticogram (ECoG) 0 ~ 500Hz Cortical Surface Implanted Electrodes 0 ~ 7kHz Single Neuron

Spatial Resolution of Recordings Regional Domain Signals Invasiveness EEG 3-5cm Non-invasive ECOG 5-1cm 1mm Field Potential vasive Single Unit 200 microns Moran

Spatial Resolution of Recordings Moran