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MICROSCOPIC WIRELESS TO POWER BRAIN-MACHINE INTERFACES
SIPS 2008, WASHINGTON DC
Jan M. Rabaey Donald O. Pederson Distinguished Prof.
University of California at Berkeley
Scientific Co-Director BWRC
Director GSRC
Exponentials In Wireless to Continue
5 Billion people to be connected by 2015
(Source: NSN)
7 trillion wireless devices serving 7 billion
people in 2017 (Source: WWRF)
1000 wireless devices per person?
Wireless subscribers expected
to top 3 Billion in 2008! (40% penetration)
Towards a World wwith 1000 Radios per Person!
Information-Technology in Turmoil
Immersive Computing
Brain-Machine Interfaces – The Ultimate in Immersion
Example:
Emotiv, Inc
The Age of NeuroScience Revolutions in scientific research for specific
areas directly correlated to breakthroughs in
instrumentation
Example: physics, astronomy, biology
This is exactly what is happening in
neuroscience today
“There are good reasons to believe that we are at a turning
point, and that it will be possible in the next two decades to
formulate a meaningful understanding of brain function”
Lloyd Watts, Neuroscientist, 2003.
Brain-Machine Interfaces The Instrumentation of Neuroscience
Learning about the operational
mechanisms of the brain
– Only marginally understood
– Potential benefits to humanity hard
to overestimate
– Health care
– Improved interfaces
– Could have huge impact in totally different domains (e.g. neuro-
inspired computation)
Brain-Machine Interfaces Taking medical care to the next level
Brain-Machine Interfaces Taking medical care to the next level
– Deep brain stimulation (BDS) for
Parkinson Disease
Brain-Machine Interfaces
Spinal cord injuries/amputees (upper-limb prosthesis) Estimated population 200,000 people in the US
11,000 new cases in the US every year
[Lebedev, 2006]
Taking medical care to the next level
[MAL Nicolelis, Nature, 18 January 2001]
REACH TASK, POLE CONTROL
BMI-Driven Motor Control - Example
BMI-Driven Motor Control - Example
New York Times, May 29, 2008
Recorded neural activity spatial domains
ECoG (Electrocortigography)– A Great Learning and Instrumentation Tool
Courtesy: B. Knight, UCB
Neuroscience Institute
Observing Individual Neural Signal Characteristics
[Courtesy: Z. Nadasdy & D. Markovic]
Every sensing site detects 1-4 neurons (~1.5 on average)
Neural signal contains 3 parts: Spikes: up to 10kHz, 50-1,000μV;
LFP: 0.1-100Hz, contains ~20% info.; DC offset: no info., up to 1V or higher.
Active neuron firing rate: 10-100 Hz Resting neuron firing rate: 1-10Hz
Spike duration: ~1ms
[Courtesy: Subbu’s rat(s)]
Neural sensors
“The Michigan Probes” “The Utah Array”
Towards Integrated Wireless Implanted Interfaces
Power budget: μWs to 1 mW
Existing Approaches
COTS-based systems
Single-chip transmitters
Single-chip transmitters with detection
Harrison et al.
2006
Irazoqui et al.
2003 Mavoori et al.
2005
No system simultaneously meets size, power and safety
requirements
A Decade of ULP Wireless Sensor Research – Lessons Learned
A systems-level perspective up front is crucial
Choice of radio architecture influenced in big way by
nature of link and availability of energy
Simple is better
Complexity almost never
translates into efficiency
Exploit opportunities
offered by emerging
technologies More “than Moore” and “Beyond
Moore”
Example: Wake-up radio for WSN
Data
ACK
ACK
TX
WuRx
wake-up
signal
Data RX
• Reduces network latency
• Requires ultra-low power
(always on)
Data Receiver
WuRx
activate main radio
Wake-up receiver (WuRx)
listens for requests and
controls duty-cycle
data
out
RF-MEMS to Provide Selectivity
Uncertain IF Avoids Precision Oscillators
100 kbit/sec @ 50 μW
Moving the Boundaries ULP Wireless Receivers
100 μW target
ULP Wireless Sensor Nodes for BMI
Energy availability most compelling issue
Use of batteries not an option
Wireless link dominant source of power
dissipation
Asymmetrical (mostly TX)
Functionality depends upon intended use
Neuroscience versus motor control
There is No Unique Solution!
• Raw recording: spike + LPF;
• Spike extraction;
• Spike detection/sorting.
Size flexibility
Function versatility
Different applications require different interfaces (data rate, size, processing)
A platform approach to BMI
Why Exploring Different Array Sizes?
Some plausible solutions
Smaller arrays/ single probes
Flexible probes
Adaptive probes
Other interfaces?
Scarring of tissue reduces sensitivity
Function Versatility and Data Rate
Neuroscience: Interested in raw data
waveform
Motor control: Mostly interested in
timing of spikes
Requires: Spike detection, feature
extraction and clustering (assigning spikes to different neurons)
2 orders of magnitude in data rate
Data rate versus Node Size
[Rizk, etc., “A single-chip signal processing and telemetry engine for an implantable 96-channel neural data acquisition system”, Journal of Neural Engineering, pp. 309-321, 2007]
[Harrison, etc., visuals of “A Low-Power Integrated Circuit for a Wireless 100-Electrode Neural Recording System”, ISSCC, 2006]
Towards Integrated Brain-Machine Interfaces
Power budget: μWs to 1 mW
Batteries problems:
size
replacement
Energy scavenging inside the body a relatively young research area
e.g. utilizing body heat (thermoelectric)
0.6 W / mm2 @ T=5°
[Paradiso05] Powering via RF
advantages:
energy source sits outside the body
versatile
limitations:
possible health risks of EM radiation
Powering Implants
Various Brain Implants
Device Power Supply Size
[mm2]
Power
[mW]
Deep Brain
Stimulator
[Medtronic]
Lithium iodine batteries (low
currents). Lifetime 3 - 5 years
30 x 50 0.04
Cochlear Implant
[MED EL]
Via RF (inductive coupling) 25 x 25 < 60
Retinal Implant
[Theogarajan06]
Via RF (inductive coupling) 11 x 11 3.2
BMI
[Harrison07]
Via RF (inductive coupling) 5 x 6 13.5
Power Limitations
• No solution has demonstrated in vivo wireless capability;
• Is it possible at all to satisfy the requirement curves?
The Transmission Channel
Consists of different tissues
with different dielectric
properties
Power Availability and Node Size
Different coupling mechanisms
show different dependencies on
size
Capacitive coupling problematic
due to large “leakage” currents
through body
Inductive coupling best for
antennas > 3 mm
Radiative energy transfer best suitable for further
miniaturization
Courtesy: Michael Mark, UCB
A 5x5 mm2 System
Near-Field Inductive
Vin > Vth
no voltage drop across rectifier
Vin = 1.2 V
Idd = 2 mA, Cdd = 1 nF
Pcomparator 5 W
Efficiency 91.7 %
Rout 40
A 1x1 mm2 System?
Far-Field Electromagnetic
Vin < Vth
Needs voltage multiplication
Vin = 40 – 140 mV @
1k
Frequency 1-2 GHz
Preceive 1-10 W
Efficiency ??
VTH of NMOS (65 nm) as function of L and W/L
Capacitive may be better option
The Data-Acquisition Challenge
Courtesy: R. Muller, UCB
Detection of small signals (uVs to 100 uV) with
large dc or low-frequency contents at 1 uW?
Example Data
Large dynamic range
Very noisy!
With field potential
Field potential removed
Data Acquistion - Example • Harrison Neural Amplifier [Utah]
– Data reduction by comparator threshold detection - MUX
amplifier to ADC
[Harrison ISSCC ‘06]
Data Acquisition Front-Ends Power IRNoise BW Area
Utah 32 W
2.5Vsup
5.1 Vrms 5kHz .25mm2
0.5 m
MIT 7.56 W
2.5Vsup
3.06 Vrms 5.3kHz .16mm2
0.5 m
Medtronic 2 W 1 Vrms 100Hz 1.7mm2
0.8 m
Opportunities
• Increased usage of digital to offer adaptivity, reconfigurability,
duty cycle, and robustness
• All current interfaces use “ancient” technologies
• Use of innovative passives
• Understanding of “neural coding” could lead to major
improvements
Signal Processing Reduces Data Rate
Courtesy: Dejan Markovic [UCLA]
But Comes at a Major Cost …
Computational Complexity of Feature Extraction
Complexity = NAdds + 10 NMults
200 samples/spike
*: Requires off-line training †: Haar Wavelet, Level 5
Courtesy: Dejan Markovic [UCLA]
Needs ULP digital
The Wireless Data Link
All interfaces today use narrow-band RF-ID
like solutions
simple modulation schemes such as OOK or
ASK
However … data link highly asymmetrical –
TX dominated
Pulse-based UWB techniques far more
appropriate
Lower energy/bit
Higher data rates
Courtesy: David Chen, UCB
Challenge: Combining high data-rate
transmission with efficient power reception
Observation: UWB advantageous
- combine properties of passive transmitters
and pulse-based radios
RIR Passive Transmitter
total
absorption
total reflection
Some Musings Going Forward BMI: an example of the many “nanomorphic”
bio-interfaces we may see emerging over the
coming decades
Potential target: observing
living cells in vitro
Need further breakthroughs
in size and energy reduction
Opportunity: nano-technology
The Unexpected Outcomes of Technology Innovation
Final Reflections Microscopic wireless as the ULP driver
Exploring energy bounds
Exploiting the “More Moore”, “More than Moore” and “Beyond Moore” opportunities
Extending to new application domains
Brain-Machine Interfaces the Ultimate
in Immersive Technologies
The potential is huge
Societal impact first, human advancement
next
Requires broad multi-disciplinary
collaboration
The new reality of engineering
A major attraction to a new generation of
engineers and beyond
Thank you!
The contributions of the following colleagues
and friends are truly apreciated:
Jose Carmena, Bob Knight, Dejan Markovic,
“Subbu” Venkatraman, Simone Gambini,
Michael Mark, David Chen, Rikki Muller, Nate
Pletcher, Brian Otis.