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Michael J. Black - January 2005 Brown University
Topics in Brain Computer Interfaces Topics in Brain Computer Interfaces CS295 CS295-
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Michael J. Black - January 2005 Brown University
Topics in Brain Computer Interfaces Topics in Brain Computer - - PowerPoint PPT Presentation
Topics in Brain Computer Interfaces Topics in Brain Computer - - PowerPoint PPT Presentation
Topics in Brain Computer Interfaces Topics in Brain Computer Interfaces CS295- -7 7 CS295 Professor: M ICHAEL B LACK TA: F RANK W OOD Spring 2005 Michael J. Black - January 2005 Brown University From what part of the brain should we
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Michael J. Black - January 2005 Brown University
Primary motor cortex (M1) Posterior parietal cortex Premotor cortex (PMA) Supplementary motor cortex (SMA)
SMA: involved in the planning
- f complex
movements and in two- handed movements. Posterior Parietal Cortex: involved in transforming visual information to motor commands. Premotor Cortex: involved in the sensory guidance
- f movement and
motor planning. M1: directly involved in producing muscle contraction.
Motor Systems
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Michael J. Black - January 2005 Brown University
Motor System
Primary motor cortex (M1) Foot Hip Trunk Arm Hand Face Tongue Larynx
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Michael J. Black - January 2005 Brown University
What is represented?
Using wrist and fingers Using elbow as fulcrum Using shoulder as fulcrum (outstretched arm)
Adapted from R. Shadmehr
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Michael J. Black - January 2005 Brown University
Signing Your Name
Prefrontal Cortex: I’ll sign my name. Posterior Parietal: combine visual and somatosensory information to localize pen wrt body. Premotor cortex: plan motion of hand wrt target path. Cerebellum: formulate details of movement in terms of dynamics. Primary Motor Cortex: sends motor commands down spinal cord. Brain Stem maintains stable posture during writing.
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Michael J. Black - January 2005 Brown University
Summary
Posterior Parietal Cortex: Transforms visual cues into plans for voluntary movements. Motor cortex: Initiating and directing voluntary movements Brainstem Centers: Postural control. Spinal Cord: Reflex coordination Motor neurons Skeletal Muscles Cerebellum: Learning movements and coordination Basal Ganglia: Learning movements, initiating movements. Thalamus Visual cues
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Michael J. Black - January 2005 Brown University
Motor Control
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Michael J. Black - January 2005 Brown University
Controlling a Motor Prosthesis
MI arm area of motor cortex. * know that activity of cells related to hand motion * accessible (in monkeys and humans) * hypothesis: natural for controlling continuous motion of a prosthesis
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Michael J. Black - January 2005 Brown University
How can we record the neural signals?
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Michael J. Black - January 2005 Brown University
Sensing the Brain
fMRI ~ 103 neurons EEG 104 MEG 103 LFP 102
Optical imaging
101 Spikes 100
Non-invasive Invasive Course(mm) Fine(microns)
SPACE TIME
Fast (msec) Slow(sec)
Source: Matt Fellows
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Michael J. Black - January 2005 Brown University
Cyberkinetics Array
SEM image
Extra-cellular recording
100 “ideal” microelectrodes 10x10 grid, 4x4 mm platform 1 or 1.5 mm long, Si shafts, Pt coated tips Glass separation Parylene insulation coating
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Michael J. Black - January 2005 Brown University
Array
Utah = Bionic = Cyberkinetics array. Fixed electrode depths – can’t move them to get a better signal. Take what you get and make the most of it.
Inventor: Richard Normann, Univ. of Utah.
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Michael J. Black - January 2005 Brown University
Signal Out
500 µm Bone
Connector Dura
White Matter
400 µm
Cortex
I III V VI
Arachnoid
Implant Surgical Procedure
Bone cap skin
- J. Donoghue 1/2001
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Michael J. Black - January 2005 Brown University
Surgical Implantation
WARNING: Graphic images of surgical procedure follow.
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Michael J. Black - January 2005 Brown University
Preclinical Safety:
Removal and Re Removal and Re-
- implantation
implantation
F1 Original Implant F1 Original Implant
F2 F2 + 4 wks Removed
+ 4 wks Removed
F3 +3 months F3 +3 months
First Implant Explant Second Implant
Donoghue Lab Arrays can be removed and reimplanted. Successful recordings can be obtained from reimplanted arrays.
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Michael J. Black - January 2005 Brown University
Surgical Methods
Intended to follow human neurosurgical procedures and methods.
- Limit duration
- Eliminate most foreign materials
- use established surgical methods
Bone flap fixation
Skin closure Percutaneous Connector
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Michael J. Black - January 2005 Brown University
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10 sig070b;SNR=5.236397 20 40
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57 units
Recorded waveforms
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Michael J. Black - January 2005 Brown University
n = 80± 7 in 3 recent MI implants Many neurons every day (19 tests over
110 days) Blue - no recording Red - best recordings
Donoghue Lab
- * 39 implants in 17 macaque
monkeys (February 1996-April 2003) * Recordings for 1098 days
Chronic Implants
From: Selim Suner
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Michael J. Black - January 2005 Brown University
Implant Challenges
- Electronics
– Miniaturization – Encapsulation – Telemetry – Heat dissipation – Low power – On board signal processing and spike sorting
Nurmikko and Patterson Chip-scale integration of array and electronics.
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Michael J. Black - January 2005 Brown University
Long term vision Nurmikko and Patterson
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Michael J. Black - January 2005 Brown University
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Michael J. Black - January 2005 Brown University
What do the neural signals encode?
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Michael J. Black - January 2005 Brown University
Language of the Brain Language of the Brain
“If spikes are the language
- f the brain, we would like
to be provide a dictionary… perhaps even providing the analog of a thesaurus.” Rieke, et al 1997.
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Michael J. Black - January 2005 Brown University
Some Terminology
Sequence of spikes from a single neuron = “spike train”
time
ISI Distribution (normalized histogram) Interspike Interval (ISI)
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Michael J. Black - January 2005 Brown University
Neural “Coding”
- How do cells represent information?
- ie, how is representation “coded” in action
potentials.
- If we understand the encoding then we can tackle
the “decoding” problem.
- inference – from activity to encoded property
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Michael J. Black - January 2005 Brown University
Neural Coding
What are the possibilities? You’ve got action potentials and now you want to represent “move the hand to the right”. How might you do it?
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Michael J. Black - January 2005 Brown University
Neural Coding
What are the possibilities?
- 1. Localist encoding in on/off response .
- 2. Rate coding.
- 3. Precise timing – pattern of spiking carries
information.
- 4. Ensembles code information that individuals can’t.
- 5. Synchronous firing within and across ensembles (it
is the interdependencies that matter).
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Michael J. Black - January 2005 Brown University
Neural Coding
- Localist view – each neuron codes a particular value
- “computer”-like model where neurons are binary
- at the low level cells represent things like
- rientation
- at the high level they represent complex
information
- Problems?
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Michael J. Black - January 2005 Brown University
Neural Coding
Population codes
- distributed representation
- information encoded in the overall activity of many
cells
- graded response – level of activity conveys
- information. Not binary.
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Michael J. Black - January 2005 Brown University
Orientation Selectivity
Hubel & Weisel, 1962
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Michael J. Black - January 2005 Brown University
Cracking the Neural Code
Source: Rob Kass
“rasters”
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Michael J. Black - January 2005 Brown University
Orientation Tuning
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Michael J. Black - January 2005 Brown University
Estimating Firing Rate
Source: Zemel & McNaughton, NIPS2000 tutorial