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.