EEG / ECoG Ontology Droplet Signal source Signal source - - PowerPoint PPT Presentation

EEG / ECoG

Ontology Droplet

Signal source

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Signal source

Signal source

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Source localization

where v is th

v Es = m m Bs =

, where ,

x As = x v m = A E B = (1) x As n + =

W

ErrW Wx s !

2

" # =

, where

ErrW W As n + ( ) s !

2

" # = WA I ! ( ) s Wn +

2

" # = Ms Wn +

2

" # = M WA I ! = Ms 2 " # = Wn 2 " # + Tr MRMT ( ) = Tr WCWT ( ) + W RAT ARAT C + ( )

1 !

=

Err

W =

Wx ! s

2 + " W !1 ! A #

Constrained localization

Dale & Sereno (1993)

!"#$%&'#()%#*+,-%#$%.)(

• #',-(-/01+%2#+3%4*0+-5+2-6+#7,#'.8#$%)(+

8)$.,4()%.-$3+92)$/#+!:;+9-8,-$#$%+ ()%#$90<)8,(.%4*#

– =)>.$/+%2#+9-$$#9%.-$+?<6+)$+!:;+#55#9%+)$*+)+?').$+#55#9%+ 9)$+?#+%'.9>0

• :#9-88#$*#*+'#)*.$/1

– @49>A+BC+DEFFGHC+!"#$"%&'()*%+'"#%'#%,-#./-"%01-23%-(# 4'%-"%+32#5-*,"+6)-7#5,-#8$5#4&-99:#;3<=&+(>-#8!?

• B-8#+IJ-%92)3K+62.(#+'#)*.$/+!:;+,),#'31

– L-%+#"#'0-$#+43#3+%2#+3)8#+'#5#'#$9#+#(#9%'-*# – B-8#%.8#3+$#/)%."#+.3+4, – M#6)'#+-5+3,)%.)(+9().83 – N2#''0&,.9>.$/+.3+3%)$*)'*+,')9%.9# – M#6)'#+-5+?.)3#*+8#)34'#3

• Cheap, easy to use
• High temporal resolution
• Clinical use for anesthesia, epilepsy
• Research use for sleep, attention, cognition,

perception

• Very poor spatial resolution
• Many artifacts: eye movement, blinking, facial

gestures, heart activity

The good, the bad ...

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• **./0
• 1"234,45&26)

7"#$%#&'()82&39:

– ;#6,2)<=>?)@AB – 1C#,2)<?>D)@AB – E6+C2)<D>=F)@AB – G#,2)<=F>F?)@AB – H2--2)<)IJ)K)%+B

E&3#"9#&L)*M)NML)@466(2"3L)*M)EML)K)OP66#"L)OM)OM)<FJJDBM)E,,#&,45&)72'464,2,#9)

• %6,4+6#)9,4-%6%9)7#2,%"#9)4&)+2"266#6)4&)C%-2&)Q49%26)'5",#RM)

!"##$%&'()*+*,-.'/01/23.'/4456/4478'

!"#$%&'$()$*+,

a b c

Grating Finger Solenoid Disk Screw

a

b

c d

R L

R L

F3-Fz C3-Fz P3-Fz O1-Fz 100 ms 1 µV

What’s it good for

Zangaladze (1999)

What’s it good for

Varela (1999)

Correlation is not causation, right?

Big data

• Cheap sets

✓ Neurosky, eMotiv ✓ Toys, games (Mindflex) ✓ Kickstarter project ✓ Carnegie Mellon (Bryan Murphy)

• Massive repositories

✓ G. Church - Harvard

Electrocorticography (ECoG)

• Electrodes under the dura
• Many fewer artifacts (eyes, facial, scalp diffusion)
• Limited used in humans: epileptic ablation pre-
• perative guidance

... and the ugly

• Flexible, foldable, actively multiplexed, high-density

electrode array for mapping brain activity in vivo

Jonathan Viventi1,2,13, Dae-Hyeong Kim3,13, Leif Vigeland4, Eric S Frechette5, Justin A Blanco6, Yun-Soung Kim7, Andrew E Avrin8, Vineet R Tiruvadi9, Suk-Won Hwang7, Ann C Vanleer9, Drausin F Wulsin9, Kathryn Davis5, Casey E Gelber9, Larry Palmer4, Jan Van der Spiegel8, Jian Wu10, Jianliang Xiao11, Yonggang Huang12, Diego Contreras4, John A Rogers7 & Brian Litt5,9

• Nature Neuroscience, Dec. 2011
• a

b c d e f

1 mm 300 µm 2 mm Right Side Left 0.3 0.2 0.1 400 800 1,000 Si Metal SiO2 600 Bending radius (µm) Strain (%) Thickness of epoxy (µm) 0.8 PI 12.5 µm PI 25 µm Previous Current 10 20 30 0.6 0.4 0.2 Stiffness (10–6 Nm2) Id (mA) Vd (V) 5V 3V 1V 0.4 0.8 1.2 1.6 1 2 3 4 Id (µA) Id (A) Vg (V) 40 60 20 6 10–4 10–5 10–6 10–7 –2 2 4 Output Row Select Elect

• rode

+V Buffer Multiplexer 200 µm Pt contact electrodes Horizontal / vertical interconnect Doped Si ribbons on polyimide Si VIA Pt 1st ML 2nd ML Multilayer

• ffset VIA

structure

Figure 1 Flexible, high-resolution multiplexed electrode array. (a) Photograph of a 360-channel high-density active electrode

• array. The electrode size and spacing was 300 × 300 m and

500 m, respectively. Inset, a closer view showing a few unit

• cells. (b) Schematic circuit diagram of single unit cell containing

two matched transistors (left), transfer characteristics of drain-to- source current (Id) from a representative flexible transistor on linear (blue) and logarithmic (red) scales as gate to source voltage (Vg) was swept from −2 to +5 V, demonstrating the threshold voltage (Vt) of the transistor (center). Right, current-voltage characteristics

• f a representative flexible silicon transistor. Id was plotted as a

function of drain-to-source voltage (Vd). Vg was varied from 0 to 5 V in 1-V steps. (c) Schematic exploded view (left) and corresponding microscope image of each layer: doped silicon nanoribbons (right frame, bottom), after vertical and horizontal interconnection with arrows indicating the first and second metal layers (ML, right frame, second from bottom), after water-proof encapsulation (right frame, third from bottom) and after platinum electrode deposition (right frame, top). Green dashed lines illustrated the offset via structure, critical for preventing leakage current while submerged in conductive fluid. (d) Images of folded electrode array around low modulus polydimethylsiloxane (PDMS) insert. (e) Bending stiffness

• f electrode array for varying epoxy thicknesses and two different polyimide (PI) substrate thicknesses. A nearly tenfold increase in flexibility between

the current device and our prior work was shown. (f) Induced strain in different layers depending on the change in bending radius.

d

• ,

d f n g

• .

ss

• n
• h

) ch h n

• a

b

d e. ls , y

• i-

f e s- se r e

• d

e k d re e

• f

t

• n

selecting 10 of the 15 trials, averaging the evoked responses and repeat-

a

b

c

• Multiplexing along column, speed <5µsec
• Sampling rate > 10kS/sec
• Low cross-talk
• Sampling area 10 x 9 mm
• Claim: sample 80 x 80 mm, 25,600 channels

at > 1.2 kS/sec

Finger Movement Classification for an Electrocorticographic BCI

Pradeep Shenoy Kai J. Miller Jeffrey G. Ojemann Rajesh P.N. Rao

• Dept. of Computer Science and Engineering

University of Washington {pshenoy,kai,rao}@cs.washington.edu, jeff.ojemann@seattlechildrens.org

als, channel. features,

s1 s2 s3 s4 s5 s6 0.1 0.2 0.3 0.4 0.5 0.6 Classifying Individual Fingers Subject Error LPM SVM

• Fig. 1.

Classifying finger movement activity: The figure shows the 5-class cross-validation error for the LPM and SVM classifiers, across 6

• subjects. The results show that a high degree of accuracy is possible in

distinguishing individual finger movements using ECOG. Also, the LPM consistently outperforms the SVM. (Chance level for a 5-class problem is 80% error.)

• Neural Engineering, 2007

Reconstructing Speech from Human Auditory Cortex

Brian N. Pasley1*, Stephen V. David2, Nima Mesgarani2,3, Adeen Flinker1, Shihab A. Shamma2, Nathan E. Crone4, Robert T. Knight1,3,5, Edward F. Chang3

• f America, 5 Department of Psychology, University of California Berkeley, Berkeley, California, United States of America

Improved reconstruction adding wavelet analysis, to account for frequency sweeps

Epidermal Electronics

Dae-Hyeong Kim,1* Nanshu Lu,1* Rui Ma,2* Yun-Soung Kim,1 Rak-Hwan Kim,1 Shuodao Wang,3 Jian Wu,3 Sang Min Won,1 Hu Tao,4 Ahmad Islam,1 Ki Jun Yu,1 Tae-il Kim,1 Raeed Chowdhury,2 Ming Ying,1 Lizhi Xu,1 Ming Li,3,6 Hyun-Joong Chung,1 Hohyun Keum,1 Martin McCormick,2 Ping Liu,5 Yong-Wei Zhang,5 Fiorenzo G. Omenetto,4 Yonggang Huang,3 Todd Coleman,2 John A. Rogers1† We report classes of electronic systems that achieve thicknesses, effective elastic moduli, bending stiffnesses, and areal mass densities matched to the epidermis. Unlike traditional wafer-based technologies, laminating such devices onto the skin leads to conformal contact and adequate adhesion based on van der Waals interactions alone, in a manner that is mechanically invisible to the user. We describe systems incorporating electrophysiological, temperature, and strain sensors, as well as transistors, light-emitting diodes, photodetectors, radio frequency inductors, capacitors, oscillators, and rectifying diodes. Solar cells and wireless coils provide

• ptions for power supply. We used this type of technology to measure electrical activity produced

by the heart, brain, and skeletal muscles and show that the resulting data contain sufficient information for an unusual type of computer game controller.

12 AUGUST 2011 VOL 333 SCIENCE

Startup out of Physics Dept., Univ. of Illinois Urbana-Champaigne

A demonstrative platform is shown in Fig. 1, integrating a collection of multifunctional sen- sors (such as temperature, strain, and electro- physiological), microscale light-emitting diodes (LEDs), active/passive circuit elements (such as transistors, diodes, and resistors), wireless power coils, and devices for radio frequency (RF) com- munications (such as high-frequency inductors, capacitors, oscillators, and antennae), all integrated

• n the surface of a thin (~30 mm), gas-permeable

elastomeric sheet based on a modified polyester (BASF, Ludwigshafen, Germany) with low Young’s modulus (~60 kPa) (fig. S1A). The devices and interconnects exploit ultrathin layouts (<7 mm), neutral mechanical plane configurations, and op- timized geometrical designs. The active elements use established electronic materials, such as sil- icon and gallium arsenide, in the form of fila- mentary serpentine nanoribbons and micro- and

• nanomembranes. The result is a high-performance

system that offers reversible, elastic responses to large strain deformations with effective moduli (<150 kPa), bending stiffnesses (<1 nN m), and areal mass densities (<3.8 mg/cm2) that are or- ders of magnitude smaller than those possible with conventional electronics or even with re- cently explored flexible/stretchable device tech- nologies (10–19). Water-soluble polymer sheets [polyvinyl alcohol (PVA) (Aicello, Toyohashi, Japan); Young’s modulus, ~1.9 GPa; thickness, ~50 mm (fig. S1B)] serve as temporary supports for manual mounting of these systems on the skin in an overall construct that is directly anal-

• gous to that of a temporary transfer tattoo. The

image in Fig. 1B, top, is of a device similar to the

• ne in Fig. 1A, after mounting it onto the skin

by washing away the PVA and then partially

"

Electrophysiological recordings

ECG EMG leg EMG leg EEG forehead EMG throat EMG-controlled game EEG Stroop (>90%)

Demos