Mining EEG Brain Dynamics: New Directions in Functional Brain Imaging
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1 Mining EEG Brain Dynamics: New Directions in Functional Brain Imaging!"#$%&'()*+%!"#$%&%'()*+(,'&+-.(/*01&%-$*"( 2"34'+#3%5(*)(/-.3)*+"3-(6-"(73'8*(,/92:(9-3;-"( 6'1%'0<'+:(=>?>(
2 Human Functional Brain Imaging EEG fmri feeg MoBI BCI ESI!,%&'()*+%-./.(
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
4 Q"*+N"&"8("'*B*+$B-.'+(Z'..'"( 4L"*7'7#89% M3=*N*7#89% A=)%+)3)8'<#3%'3?%2#?1:'<#3%#B%445%C%DEF% *6%;G&FD4H%% &#?1:'<#36% J)18#+:*':K% ;#8<"#7=':'2*"% >':1)%!967)2% &#?1:'<#36% ;#8<"#"#8<"':%!,%&'()*+%-..I(
5 Borhegyi et al., JNS, 2004
6 Brain dynamics are inherently multi-scale Local Extracellular Fields EEG (scalp surface fields) ECOG (larger cortical fields) surface At each spatial recording scale, the signal is produced by active partial coherence of distributed activities at the next smaller scale. Intracellular and pericellular fields Synaptic and other transmembrane potentials Scale chauvinism Scott Makeig 2007
7 (OF-#'(G*"'#(P[+''0-"R( (Q4-.-"GF'#(PO.'"\R(
8
9
10 ]*G-.( 65"GF+*"5( /*+%'Y( T'.-$4'(!"N'1'"N'"G'( ]*G-.( 65"GF+*"5( 6B3"( 6B&..(!,%&'()*+%-..I(
11 The very broad EEG point-spread function
12 _&0-"(L.'G%+*'"G'1F-.*8+-0(PLLMR( `"%*"(^(A-B'38:(=>>C(
13 The Dome of the Sky Scott Makeig 2008
14 3-D structure of the Universe NASA 2009
15 X K% K% K% K% K% K% =S7(!"%'+1+'%-$*"(*)(6G-.1(LLM(638"-.#((( K%
16 Electromagnetic source localization Solve the forward problem using realistic head models (BEM) Inverse Problem Simple Map Mesh generation Sensor Localization Signal Processing Source Image Segmentation MRI Zeynep Akalin Acar, & Scott Makeig 06 EEG/MEG
17 Blind EEG Source Separation by Independent Component Analysis CSF EEG Cocktail Party S. Makeig (2000)
18 Infomax ICA Makeig et al., NIPS95
19 Are EEG source outputs (nearly) independent? Independent Domains of Local Synchrony Cortex Freeman - phase cones Plenz - avalanches Thalamus S. Makeig (2007)
20 !/Q(3"( 1+-G$G'( `"%*"(^(A-B'38:(=>>D(
21 Lb&34-.'"%(N31*.'#( 63"8.'(N31*.'( #*&+G'( 7&-.S#500'%+3G(N31*.'( #*&+G'(
22 Independent muscle signals S. Makeig, J. Onton 2005
23 445DQR% )3T*8#32)37%B#8%&'7:'N%
24 445DQR% )3T*8#32)37%B#8%&'7:'N%
25 Equivalent dipole density Visual Working Memory Sternberg letter memory task Onton et al., 2005 Onton et al., 05
26 Equivalent dipole density Auditory Novelty Auditory oddball plus novel sounds Onton et al., 2005 Onton et al., 05
27 Equivalent dipole density Emotion Imagination Emotion imagery task Onton et al., 2005 Onton et al., 05
28 Equivalent dipole density Task A Old/New Word Memory Word memory (old/new) task dipoledensity() Onton et al., 2005 Onton et al., 05
29 Equivalent dipole density Task B Cued finger movements Visually cued button press task dipoledensity() Onton et al., 2005 Onton et al., 05
30 Modeling Spatiotemporal Variability 3-Model AMICA Decomposition Model Log Likelihood CPT Flanker FAST EC EO EC EO Time-on-Task (1.5 hours) Grainne MacLoughlin & Jason Palmer, 2010
31 EEG States of Emotion Imagination Suggest the imaginative experience of 15 different emotions: -! initial relaxation -! alternate positive and negative emotions -! relaxation between emotion episodes -! obtained 1-5 min periods of eyes-closed spontaneous EEG -! 33 subjects `"%*"(^(A-B'38:(!"#$%&"'(=>>C(
32 Independent Modulators
33 Independent modes of spectral modulation `"%*"(^(A-B'38:(!"#$%&"'()$(*+,-$(.&+"#'/)&$/&(H>C(
34 Broadband high-frequency modes `"%*"(^(A-B'38:(!"#$%&"'()$(*+,-$(.&+"#'/)&$/&(H>C(
35 Changes in broadband power with imagined emotion Julie Onton & Scott Makeig, Frontiers in Human Neuroscience, 2009
36 Distribution of broadband modes `"%*"(^(A-B'38:(!"#$%&"'()$(*+,-$(.&+"#'/)&$/&(H>C(
37 Positive and negative valence modes `"%*"(^(A-B'38:(!"#$%&"'()$(*+,-$(.&+"#'/)&$/&(H>C(
38 U167V%Q%!1*7)%B#8%E:17)W%>*#:*3W%;)::#W% '3?%R8'*3% [*&+%F(!"%'+"-$*"-.(W/!(A''$"8( Q#3.*0-+(A''$"8(M+*&"N#:(O-G3VG(M+*4':(/Q( a&"':(=>?>(
39 I gaped I held I jumped... I swerved Brain Who Dynamic I? I threw. I ran I reached I shot I tossed Distributed I ducked Events I pointed I smiled S. Makeig, 2001
40 Distributed I wondered if All of a sudden... I looked to see if Brain Dynamic I noticed that I decided that I imagined I realized that It struck me that The feeling hit me that I looked again at. Events It occurred to me that I searched my memory for S. Makeig, 2001
41
42 Distributed muscle / movement events Onton & Makeig, submitted
43 Human Agency & Embodied Cognition Brain processes have evolved and function to optimize the outcome of our behavior in response to perceived challenges and opportunities. Scott Makeig, 2008
44 The brain & body together respond to the challenge of each moment. Mobile Brain/Body Imaging But human brain activity during natural motor actions and interactions in 3-D space has never been observed or modeled! S. Makeig, 2007
45 Brain imaging during natural behavior?!,'-+.5(-..(<+-3"(30-83"8(#%&n3'#(palm:(ol9:()at!:(-"n( LLMR((-+'(G*"N&G%'N(3"(+383N.5(#%-$G(#'-%'N(*+(1+*"'( 1*#3$*"#(;3%F(*".5(%F'(0*#%(03"30-.(V"8'+(0*4'0'"%#( ALM( LLM( )AT!( X=9K(( %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%='6%3)T)8%N))3%#N6)8T)?%#8%2#?):)?g( 6G*c(A-B'38(=>>K(
46 M I C R O ~1,000,000 GHz? BRAIN SPIKES LFP ECOG EEG MACRO ~1 MHz?! BEHAVIOR Recorded!? RT ~1 Hz Observable?? Hz S. Makeig 2007
47 M I C R O SPIKES MoBI LFP ECOG EEG MACRO ~1,000,000 GHz? BRAIN ~1 MHz?! BEHAVIOR Recorded!? Observable Mobile Brain/Body Imaging?? Hz! Record what the brain does,! What the brain experiences,! RT ~1 Hz And what the brain controls. S. Makeig 2007
48 MoBI Lab at SCCN, UCSD
49 MoBI Systems for Cognitive Monitoring! Alertness! Arousal! Attention! Anticipation! Agency! Affect S. Makeig, 2009
50 Brain-Computer Interface (BCI) Systems for willed control S. Makeig
51 Electrocortical source imaging (ESI) systems for clinical research, diagnosis, and treatment! EEG & MoBI are low-cost relative to other brain imaging systems;! This allows future wide distribution of these systems,! And their use in very large research studies.! Very large online data resources are possible (HeadIT);! These may be improve the speed and quality of BCI, cognitive monitoring, and clinical imaging systems.! Extremely high-density EEG & MoBI imaging systems are possible;! These may allow new discoveries in electro-accupuncture, etc. S. Makeig 2010
52 Electrocortical source imaging (ESI) systems for clinical research, diagnosis, and treatment Seizure Seizure J. Palmer, G. Worrell, Z. Akalin Acar, S. Makeig 2008
53 Electrocortical source imaging (ESI) Zeynep Akalin Acar 2009
54 Electrocortical source imaging (ESI) Zeynep Akalin Acar 2009
55 Electrocortical source imaging (ESI) Zeynep Akalin Acar 2009
56 Functional EEG brain imaging Paradigm shift!! EEG is something to use, not just something to inspect Goals: 1.! Modeling dynamic, distributed brain/body events to understand how the brain works! 2.! Mobile Brain/Body Imaging (MoBI) for cognitive monitoring. 3.! Brain-Computer Interface (BCI) for willed control. 4.! Electrocortical source imaging (ESI) for medical diagnosis and treatment.
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