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1 !"#"#$%&&'%()*"#%+,#*-"./% 0.12%!*34"$%!"#$%&%'()*+(,'&+-.(/*01&%-$*"( 2"34'+#3%5(*)(/-.3)*+"3-(6-"(73'8*(,!9/(6'03"-+( :-#;3"8%*"(2"34'+#3%5<(6%=(>*&3#(
2 &-(1+"4+%5$4#.,%( G+-3"(1+*H'##'#(( ;-4'('4*.4'I(-"I()&"H$*"((!"#"$%&'()#!*)#!"#$!%&' "+#!*)#(&)*+,!-( %;'(?+-3"(*+8-"3J'#( 3"(+'#1*"#'(%*(.&-$&,+&/'$)*00&12&3' *1/'!..!-#"1,4&3=(( 7$4!1( 5+*0"*4!1( 6&-$&.4!1( 6)*"#/%-447%784%.8*994#$4% 1:%784%-1-4#7;%
3 Functional Brain Imaging History EEG '+-( #% ((;&0-"(NNO(+'H*+I3"8% ((((BKPQ(((((B #% (E-"-.*8F(NNO(#1'H%+-.(-"-.5#3#( ERP '+-( #% (H*01&%'+(NRS(-4'+-83"8(E/9TF( #% (0-8"'%*'"H'1;-.*8+-0(ECNOF( fmri '+-( ((((BKKP((( (B #% ()CR!(GV>7(+'H*+I3"8#% ((((BKKP(((((B #% (?+*-I?-"I(NR6S( ((((BKKW(((((B #% (0&.$#*&+H'(NNO(X.%'+3"8(?5(!/9( feeg / BCI / MoBI '+-(Y( ((((@AAK(((MB #% (H*00'+H3-.(I+5Z'.'H%+*I'(NNO(%*5#( ((((@ABB(((MB #% (C*G!(>-?*+-%*+5( ((((@AB@((((MB #% (V".3"'(PZ7(0*?3.'(1;*"'(-11#( <%!*34"$%=>??( 6=(C-D'38<(@AAK(
4 EEG (scalp surface fields) Local Extracellular Fields ECOG (larger cortical surface fields) At each spatial recording scale, the signal is produced by active partial coherence of distributed activities at the next smaller scale. Local field dynamics also influence spike rate, timing, and synchrony. Intracellular and peri-cellular fields Synaptic and other transmembrane potentials Brain dynamics are inherently multi-scale Scott Makeig 2007
5 Macro field dynamics are spontaneously emerging dynamic patterns in complex, nonlinear media. Scott Makeig 2008
6 (S;-#'(H*"'#(E[+''0-"F( (94-.-"H;'#(EG'88#(\(S.'"JF(
7 The spatiotemporal field dynamics of cortex have not yet been imaged simultaneously on multiple spatial scales! Alan Friedman
8 "/%AJ!IG&K%% &O."7*71),% P#8"("71),% A1)B.178*9*-".% A1)B.1.1)B.*9% 0<%!*34"$%=>>L(
9 >*H-.( 65"H;+*"5( /*+%']( NNO( N^'H$4'( 6*&+H'#( >*H-.( 65"H;+*"5( 6D3"( 6D&..( 0<%!*34"$%=>>L(
10 N% N% N% N% N% N%
11 The very broad EEG point-spread function Simulated small parietal source Very broad projected scalp potentials
12 This animation is available for YouTube viewing at and for download at Z#H-.'(0&.$Z#*&+H'(-H$43%5<((-"I(3%#(NNO(1+*_'H$*"( (9D-.3"(9H-+(\(C-D'38(@ABA(
13 G.3"I(NNO(6*&+H'(6'1-+-$*"(?5(!"I'1'"I'"%(/*01*"'"%(9"-.5#3#( ICA can find distinct EEG source CSF activities -- and their simple scalp maps! NNO (((/*HD%-3.(S-+%5( Tony Bell, developer of Infomax ICA S-1'+`(6=(C-D'38<('%(-.=<(,!S6(EBKKLF(
14 P#+4D4#+4#7% 7*0-3"#(( *)(>*H-.(65"H;+*"5( /*+%']( [+''0-"(Z(1;-#'(H*"'#( G'88#(\(S.'"J(Z(-4-.-"H;'#( T;-.-0&#(
15 Independent EEG Components are Dipolar
16 PA5%V#+/%M1#T6)*"#%P#+4D4#+4#7%A1-D1#4#7%SPAU%I)1.4//4/%W% W%/4D*)*74/%784-%:)1-%784%)4-*"#+4)%1:%784%+*7*%W% J. Onton & S. Makeig 2006
17 PA5%*9/1%/4D*)*74/%.1)B.*9%()*"#%PA%D)1.4//4/% Nb&34-.'"%(I31*.'#( 63"8.'(I31*.'( H*01*"'"%( 7&-.Z#500'%+3H(I31*.'( H*01*"'"%(
18 EEGLAB MoBILAB BCILAB MPT SIFT Tools available -- but a two-cultures problem 6(C-D'38<(@AB@(
19 Localizing independent component sources Scalp EEG source ieeg sulcal seizure source FEASIBLE FOR SCALP EEG ICs? Will need at least: Anatomic MR image Accurate electrode positions Accurate co-registration Good skull conductivity estimate IC source domain estimate Onton Z. Akalin et al., Acar, 2005 S. Makeig, 2011
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20 &&'%Z,#*-"./%1:%&-1B1#%P-*$"#*B1#% 4OD4)"4#.4%1:%?[%4-1B1#/\%! (-c'+(d'.'"(g*""5(eo!cf(! (B#+(+'.-]-$*"(3"I&H$*"(! (-.%'+"-%'(1*#(-"I("'8('0*$*"#(! (+'.-](?'%e''"('0*$*"('13#*i'#(! (BZW(03"(1'+3*I#(*)('5'#ZH.*#'I( #1*"%-"'*&#(NNO(](BW('0*$*"#(! (PP(#&?_'H%#( V"%*"(\(C-D'38<(,-".%)-/#'.#01&2.#3)1-"/4').4)# AK(
21 !"I'1'"I'"%(C*I&.-%*+#( V"%*"(\(C-D'38<(,-".%)-/#'.#01&2.#3)1-"/4').4)# AK(
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24 ICA for BCI? IEEE TRANSACTIONS ON REHABILITATION ENGINEERING, VOL. 8, NO. 2, JUNE 2000 stimn C5 199, nner, par-, pp. atory euron 5, pp. ntrol eas, uend, opul. 70, based Clin.l comvol. esign mpo- 4, no. erelought on in 232, hand 9. com- A Natural Basis for Efficient Brain-Actuated Control Scott Makeig, Sigurd Enghoff, Tzyy-Ping Jung, and Terrence J. Sejnowski Abstract The prospect of noninvasive brain-actuated control of computerized screen displays or locomotive devices is of interest to many and of crucial importance to a few locked-in subjects who experience near total motor paralysis while retaining sensory and mental faculties. Currently several groups are attempting to achieve brain-actuated control of screen displays using operant conditioning of particular features of the spontaneous scalp electroencephalogram (EEG) including central -rhythms (9 12 Hz). A new EEG decomposition technique, independent component analysis (ICA), appears to be a foundation for new research in the design of systems for detection and operant control of endogenous EEG rhythms to achieve flexible EEG-based communication. ICA separates multichannel EEG data into spatially static and temporally independent components including separate components accounting for posterior alpha rhythms and central activities. We demonstrate using data from a visual selective attention task that ICA-derived -components can show much stronger spectral reactivity to motor events than activity measures for single scalp channels. ICA decompositions of spontaneous EEG would thus appear to form a natural basis for operant conditioning to achieve efficient and multidimensional brain-actuated control in motor-limited and locked-in subjects. I. INTRODUCTION Recent work in several laboratories has demonstrated that noninvasively recorded electric brain activity can be used to voluntarily control switches and communication channels, allowing a few so-called locked-in near-totally paralyzed subjects the ability to communicate, however slowly, with their families and aides ([4]; [14]; [2]). Communication rates achieved to date are in the range of several bits a minute, far from rates that would allow locked-in persons access to C-D'38('%(-.=<(5666#7-2./#8)*29:#6.;'.:<(@AAA(
25 MPT 6APG56% #HH"=&H#I='I&hY"3"h6APG56(
26 Challenge: Given EEG epochs each time-locked to a HEAR or LOOK cue, train a model to estimate, from the EEG in each epoch, in which direction attention switched (HEAR"LOOK or LOOK"HEAR). 7-%-`(a(T*e"#'"I('%(-.=<(@AAP(
27 5#%&&'%524#B1#T08"X%M47Y1)3% 5.+"-&2%<)#,)2!1-)#=.2>?/'/# G9(BK<(PA<(BQ<(9##*H3-$4'(EjPF( HEAR " LOOK S+30-+5(EjBF(43#&-.( [+'b&'"h5(edjf( BA( (W( (P( G9(K<Q<(3"H.&I'#()+*"%-.('5'(X'.I( (9f'"I(("(9f'"I( 9&I3*(((((((((j3#&-.( k( (ZB( A( >-%'"H5(+'(6;3c(( A( -"I(9##*H3-$4'(EjPF(43#&-.( G9(PK<BK<(PU<(9##*H3-$4'(43#&-.(EjPF( G9(P<(lP<(l<(lA<(S+30-+5( 6*0-%*#'"#*+5(-"I(C*%*+( Z(
28 T Mullen, 2011
29 E)*#/"4#7%&_M%E847*%M47Y1)3%A1##4.BR"7,( Tim Mullen, S. Makeig et al. unpublished
30 M I C R O SPIKES MoBI LFP ECOG EEG MACRO ~1,000,000 GHz? BRAIN BEHAVIOR ~1 MHz Recorded!? Average Mobile Brain/Body Imaging Record what the brain does, What the brain experiences, And what the brain organizes. RT ~1 Hz S. Makeig 2007
31 @-2'.#'&2;'.;#A1-'.;#&"!"-#9)*2<'"-B(,'-+.5(-..(?+-3"(30-83"8(#%&I3'#(ECNO<(SNT<()CR!<(-"I( NNOF((-+'(H*"I&H%'I(3"(+383I.5(#%-$H(#'-%'I(*+(1+*"'( 1*#3$*"#(e3%;(*".5(%;'(0*#%(03"30-.(X"8'+(0*4'0'"%#( -..*e'i=(( CN O( NNO( )CR!( `8,N((!"(-..(0*I-.3$'#(?&%(NNO<(%;'(#'"#*+#(-+'(84*R,=(( C&#H.'(-"I(0*4'0'"%#(H*"%+3?&%'(E "*3#' F(#38"-.#=( &"<)&).!/#-"I('.!)-24%"./#e3%;3"(-(PZ7('"43+*"0'"%=( %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%8*/%#4R4)%(44#%1(/4)R4+%1)% n( SNT(
32 !1("94%6)*"#F61+,%P-*$"#$%S!16PU%A1#.4D7% B=(R'H*+I(#30&.%-"'*&#.5<(I&+3"8("-%&+-..5(0*$4-%'I(?';-43*+<( (`8*7%784%()*"#%+14/%%%%% (E;38;ZI'"#3%5(NNOF( ( ((`8*7%784%()*"#%4OD4)"4#.4/%%%E#'"#*+5(#H'"'(+'H*+I3"8F( ( ( (`8*7%784%()*"#%1)$*#"b4/% ((E?*I5(\('5'(0*4'0'"%#<( ( ( ( ( ( ((((((((((((((( ( ( ( ( (c/4%4r19r"#$%-*.8"#4%94*)#"#$%-4781+/%% %%%%71%V#+d%-1+49d%*#+%-4*/@)4%% %%%%%%%#1#T/7*B1#*),%S.1#74O7T%*#+%"#74#B1#T)49*74+U%% %%%%%%%%%%:@#.B1#*9%)49*B1#/8"D/%*-1#$%784/4%+*7*%-1+*9"B4/<% Scott Makeig, 2011
33 !16P\%!1("94%6)*"#F61+,%P-*$"#$(
34 MoBI Lab at SCCN, UCSD LSL Lab Streaming Layer software for synchronous multi-stream, multi-platform recording and feedback freely available on Google Code. Also, SNAP Pythonbased scripting for complex experiment control. See
35
36 MoBI Lab: Two-Person Mirroring Experiment Photo: T Bel Bahar & E Tumer, 2011
37 MoBI Lab: Two-Person Mirroring Experiment
38 Development of Shared Attention A Mother and Child MoBI Experiment Gedeon Deak et al., 2011
39 3-yr old child Reward Observation Mother Pops the Bubble! mu blank screen (baseline) T Mullen, Yu Liao,,S Makeig, G. Deak 2011
40 S. Makeig, J Grethe, N Bigdely-Shamlo, 2012
41 Swartz Center for Computational Neuroscience (SCCN) UCSD, La Jolla CA SCCN currently has over 50 researchers and students working on electrophysiological brain dynamics via high-density EEG, ECoG, MoBI, and other data some 26 of us shown here th Anniversary SCCN Impromptu celebration 1/2/12
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