makeig ucsd i 10

!"#"#$%&'$#"()*%+,-"#%./#-0"12%3!

41'5%!-6*"$%

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

2#34(+$3&5!*)!/-.3)*+#3-!6-#!73(8*!

789:%;;<=>+%?',62:'@%A%B&4.%

C')*0D*,E%8F7F!

4G%!-6*"$%8F7F!

99:;<=!>3$&*+5!•!!?@@A!B!9C6D!EF-G(38H!

•!!?@@I!B!"#)*0-J!"/<!)*+!99:!EF-G(38K!=(..K!L'#8K!6(M#*N$G3H!

•!!?@@O!P!99:Q"/<!R**.S*J!ET#.U$-.GU(V'HK!"R/!W!9C/!

•!!?@@@!P!9CDP30-8(!1.*&$!EL'#8!W!F-G(38H!

•!!XYYY!P!R**.S*J!:2"!V($38#!E7(.*+0(H!

•!!XYYX!P!99:;<=!E$TT#U'T$VU(V'H!

•!!XYYZ!P!,">!$'11*+&K!-#V!E7(.*+0(!W!F-G(38H!+()(+(#T(!1-1(+!

•!!XYYZ!P!?$&!!99:;<=!N*+G$[*1!E2/67K!;-!L*..-K!/-.3)*+#3-H!!

•!!XYYZ!P!?$&!99:;<=!1.'8P3#$!

•!!XYYI!B!\*+G$[*1$!3#!D*+&*!ED*+&'8-.H!-#V!;3S*#!E6DCH!

•!!XYY]!B!\*+G$[*1!3#!63#8-1*+(!

•!!XYY]!P!?$&!6R27^!$&+'T&'+(!-#V!T*01*#(#&!T.'$&(+3#8!&**.$!

•!!XYYO!P!\*+G$[*1$!3#!<$1(&!E_+-#T(HK!;-!L*..-!E/-.3)*+#3-HK!!6-#%-8*!E/[3.(H!

•!!XYY`!P!,">!$'11*+&!+(#(N(Va!

•!!XYY@!B!,_R!B!,('+*(.(T&+*0-8#(%T!_*+N-+V!>(-V!F*V(.3#8!R**.S*J!

•!!XYY@!P!\*+G$[*1$!3#!=.**03#8&*#!"#V3-#-K!<$1(&!_+-#T(K!;-!L*..-!/-.3)*+#3-K!!65V#(5!<'$&+-.3-!

•!!XYY@!B!9C"/<!7-&-C34(+QD+*V'T(+!B!9J1(+30(#&-.!C(-.P%0(!"#&(+-T%4(!/*#&+*.!W!<#-.5$3$!

•!!XY?Y!B!\*+G$[*1$!3#!L54-$G5.-!_3#.-#VK!,/R2K!R-3N-#K!D*+&.-#V!b+(8*#K!-#V!2/67!

•!!XY?Y!P!>(-V"R!V-&-S-$(!%(V!&*!99:;<=!!

•!!XY??!B!99:;<=!/[-&!3#&+*V'T(V!

S. Makeig 2010

EEGLAB downloads for 20/06/2007

Total count is 34

Username Email Comments

Russia @mail.ru eeg, erp, bci

Company @nexstim.com EEG develop e r

Indonesia @tf.itb.ac.id Brain Computer Interface

Finland @psyka.jyu.fi

Australia @newcastle.edu.au Auditory Psychophysics Psychopathology

La Jolla @gmail.com Cogneuro

EEGlab is great!

Chinc a @126.com hi!

? @yahoo.com LFP in DBS patients

US Gov @pnl.gov

US EDU @bethelks.edu EEG and ERP responses to music stimuli

US EDU @wjh.harvard.edu Neuroscienc e

US EDU @wlu.edu olfaction ERP

Switzerland @student.ethz.ch

Sweden @neuro.gu.se EEG

Germany @med.uni-

muenchen.de

China? @163.com Signal Processing

China @sina.com ic a

Finland @helsinki.fi cognitive brain research

Spain @ugr.es

Netherland s @sdf.nl dfg

Company? @tom.com BCI

? painfulresult@.com

Franc e @hotmail.fr

Biomedical engineering

movement-related cortical potentials

brain-computer interfaces

•! ~200 EEGLAB downloads a week

… all together to at least 90 country domains

•! > 3,500 on the ‘eeglablist’ discussion list

•! 20++ EEGLAB plug-ins available

S. Makeig 2007

99:;<=!\*+G$[*1$!90 Participants (2007):

•! Canada

•! USA

•! L-1-#!

•! R-3N-#!

•! 6U!c*+(-!

•! <'$&+-.3-!

•! Germany Austria

•! Italy

•! Norway

•! Ireland

•! England

S. Makeig 2010

Portland

La Jolla

Bloomington

Santiago

Newcastle

Singapore

Taiwan

Porto Aspet

Jyväskylä

I jumped ...

I swerved …

I smiled …

I threw ….

I pointed …

I held …

I reached …

I ran …

I shot …

I ducked

I tossed …

I gaped …

Who

am I? S. Makeig 2001

All of a sudden ...

I looked to see if …

I searched the scene for …

I looked again at ….

It occurred to me that …

I wondered if …

I noticed that …

I decided that …

I imagined …

The feeling hit me like …

It struck me that …

I realized that …

?S. Makeig 2001

“When I went to the back of the house for a second

time, I looked around more carefully. Some light

came from the neighbors’ on the other side of the

grape-stake fence. I noticed that the back door of

Stanley Broadhurst’s house was slightly ajar. I

opened it all the way and turned on the kitchen

lights. There were marks around the lock which

showed that it had been jimmied. It occurred to me

that the guy who did the job might still be inside. …

I turned off the kitchen light and waited. The house

was silent. From outside I could hear the pulsing

hum of the arterial boulevard I had just left.”

- Ross MacDonald The Underground Man

Dynamic Brain Events

S. Makeig 2001

Brain processes

have evolved and function

to optimize the outcome

of the behavior

the brain organizes

in response to

perceived challenges

and opportunities.

Embodied Cognition & Agency

+,-"#2%0**9%9:*%1:-HH*#$*%'I%

9:*%0'0*#9J%

perception action

evaluation

Functional Brain Imaging

6U!F-G(38K!XYY@!4G%!-6*"$%8F7F!

4'0*%:K0-#%D,-"#%"0-$"#$%0"H*29'#*2!

!!!!?@X]!!!d?$&!!['0-#!99:!+(T*+V3#8!

EEG (+-%

!!!!?@A`!!!!!?$&!99:!$1(T&+-.!-#-.5$3$!

!!!!?@]X!!!d?$&!T*01'&(+!9CD!-4(+-83#8!E/<RH!

ERP (+-%

!!!!?@O@!!!!!?$&!(4(#&P+(.-&(V!V($5#T[+*#3e-%*#!

!!!!?@@A!!! !?$&!)FC"!=b;7!+(T*+V3#8$!

fMRI (+-%

!!!!?@@A!!!!!!?$&!S+*-VS-#V!9C6D!

!!!!?@@I!!!!!?$&!0'.%$*'+T(!99:!f.&(+3#8!S5!"/<!

!!!!XYY@!!!d?$&!T*00(+T3-.!V+5!(.(T&+*V(!99:!&*5$!

fEEG & BMI (+-!a%

Makeig (2006)

What is EEG?!

EEG (scalp surface fields)

ECOG (larger cortical

surface fields) Local

Extracellular

Fields

Intracellular and

peri-cellular fields

Synaptic and

other trans-

membrane potentials

Brain dynamics are

inherently multi-scale

At each spatial recording scale, the

signal is produced by active partial

coherence of distributed activities at the next smaller scale.

Scott Makeig 2007

Cross-scale coupling

is bi-directional!

Larger

Smaller

EEG (scalp surface fields)

ECOG (larger cortical

surface fields) Local

Extracellular

Fields

Intracellular and

peri-cellular fields

Synaptic and

other trans-

membrane potentials

Brain dynamics are

inherently multi-scale

At each spatial recording scale, the

signal is produced by active partial

coherence of distributed activities at the next smaller scale.

Scott Makeig 2007

Cross-scale coupling

is bi-directional!

Larger

Smaller

SCALE CHAUVINISM!

&',(1'1',(1-H%

&',(1'9:-H-0"1%

L-HK*%4/29*0%

!'MKH-('#2%

;@:-@(1%

!'MKH-('#2%

N:*%$*#*,-('#%-#M%0'MKH-('#%'I%;;<%O%=PQ%

"2%&R!Q=;S%-#M%#'9%T*HH%29KM"*M%

4G%!-6*"$%8FFU!

C*K,'$H"-HV%

;W1"9-9',/%3#:"D"9',/%

X*0'M/#-0"1%"0-$"#$%

g!30-83#8!S+-3#!

;#*,$/!

73+(T&!AP7!3#4(+$(!0*V(.K!

S'&!h'3&(!$.*N!W!3#V3+(T&!

Functional Brain Imaging

;H*19,'0-$#*(1%"0-$"#$%

g!30-83#8!.*T-.!T*+%T-.!

4/#1:,'#/!

AP7!30-83#8!+(h'3+($!0*V(.K!

S'&!h'3&(!)-$&!W!V3+(T&!0(-$'+(!!

*)!*#(!-$1(T&!*)!#('+-.!-T%43&5U!

6U!F-G(38K!XYY@!4G%!-6*"$%8F7F!

!D[-$(!T*#($!E_+((0-#H!

!<4-.-#T[($!ED.(#eH!

4G%!-6*"$%8F7F!

Scott Makeig 2008

Macro field dynamics are

spontaneous emergent

dynamic patterns – in both

outer space and cortex.

Alan Friedman

Big Bear Solar Observatory/NJIT

Alan Friedman

The spatiotemporal field

dynamics of cortex and brain

have not yet been imaged on

multiple spatial scales!

YG%>6-H"#%>1-,E%ZG%Q-H0*,E%<G%?',,*HHE%[%!-6*"$%8F7F!

21-H@%2"$#-H2%\%2'K,1*%2"$#-H2%J%

/*+&(J!

6G'..!

;*T-.!

65#T[+*#5!

;*T-.!

65#T[+*#5!

6G3#!C(.-%4(!

"#V(1(#V(#T(!

4G%!-6*"$%8FFU!

Brain EEG " Scalp EEG!

21-H@%2"$#-H2%\%2'K,1*%2"$#-H2%J%

/*+&(J!

6G'..!

;*T-.!

65#T[+*#5!

;*T-.!

65#T[+*#5!

6G3#!C(.-%4(!

"#V(1(#V(#T(!

4G%!-6*"$%8FFU!

Naïve 2-D interpretation of EEG signals?

?

? ?

?

?

? ?

Actual cortical source volume

conduction patterns (cartoon)

Cortical EEG signal projection

patterns as point processes

“Surely, Dr. Lowe, if there were gravity waves, we would have detected them by now.”

The very broad EEG point-spread function

iU!<G-.3#!<T-+!W!6U!F-G(38!XY?Y!

The very broad EEG point-spread function

iU!<G-.3#!<T-+!W!6U!F-G(38!XY?Y!Single spatially labile source

The Dome of the Sky

Scott Makeig 2008

Stephen L. Alvarez

3-D structure of the Universe

NASA 2009

The very broad EEG point-spread function

iU!<G-.3#!<T-+!W!6U!F-G(38!XY?Y!Spatially static cortical patch source

!D[-$(!T*#($!E_+((0-#H!

!<4-.-#T[($!ED.(#eH!

Scott Makeig 2007

@10 Hz, 20 cm

0° 360°

18°

The very broad EEG point-spread function

iU!<G-.3#!<T-+!W!6U!F-G(38!XY?Y!

Phase lag, center to edge: 18°

The very broad EEG point-spread function

iU!<G-.3#!<T-+!W!6U!F-G(38!XY?Y!

Phase lag, center to edge: 0°

MRI Segmentation

Solve the forward problem

using realistic

head models (BEM)

Mesh generation

EEG/MEG

Source

Image

Inverse

Problem

Signal

Processing Sensor

Localization

Electromagnetic

source localization

Zeynep Akalin Acar, & Scott Makeig ‘06

Simple

Map

ERP !" EEG !" LFP !" #Spikes

1960 " Response

averaging

2000" 1993"

Brain Electrophysiology

S. Makeig, TINS 2002

?

MICRO

MACRO

RT

SPIKES

LFP

ECOG

EEG Recorded !?

~1,000,000 GHz

~1 Hz

BEHAVIOR BRAIN ?

~1 MHz

S. Makeig 2007

?

ERP

Scott Makeig, 2008

Studying ‘cognitive perception’ using ERPs

Data ! Average + “Background”

But, this linear decomposition is veridical

if & only if:

1. The Average appears in each trial.

2. The “Background” is not perturbed in other ways by the time locking events.

The response averaging model:

ERP EEG “noise” EEG

BOLD ERB BOLD “noise”

Not True / Not Defined

Not True

S. Makeig 2004

Conceptual legacies of

single sensor response/rate averaging

-! Reduction of the time series data at each channel

to a single average response time series.

-! Reduction of the data collected at each channel

to an isolated spatial point process.

How to capture more of the event-related brain

dynamics contained in high-density EEG data?

CSF

EEG Cocktail Party

Blind EEG Source Separation by

Independent Component Analysis

S. Makeig (2000)

Spatial Source Filtering!

CSF

EEG Cocktail Party

Blind EEG Source Separation by

Independent Component Analysis

S. Makeig (2000)

Makeig et al., NIPS95

Infomax ICA

Cortex

Domains

of Local Synchrony

Independent

Thalamus

Are EEG source outputs (nearly) independent?

Freeman - phase cones

Plenz - avalanches

S. Makeig (2007)

L'.3(!b#&*#!W!6U!F-G(38!EXYY]H!

3#M*@*#M*#9%D,-"#%;;<%2'K,1*2%

7'-.P$500(&+3T!V31*.(!

$*'+T(!

63#8.(!V31*.(!

$*'+T(!

9h'34-.(#&!V31*.($!

Independent muscle signals

S. Makeig, J. Onton 2005

Onton & Makeig, submitted

Distributed

muscle /

movement

events

N. BigdelyS. Makeig, 2009

ICA is a linear data decomposition method

W * Channel_Data = Activations

W-1 * Activations= Channel_Data

Independent Components of Human EEG are Dipolar

"/<!3#!

1+-T%T(!

(100 channels,

~500k time points)

b#&*#!W!F-G(38K!XYY]!

24 Subjects –Frontal Midline Theta Sources

Onton & Makeig 2006

Equivalent dipole density

Visual Working Memory

Onton et al., 2005

Sternberg

letter

memory task

Onton et al., ‘05

Onton et al., 2005

Auditory

oddball

plus novel

sounds

Onton et al., ‘05

Equivalent dipole density

Auditory Novelty

Onton et al., 2005

Emotion

imagery

task

Onton et al., ‘05

Equivalent dipole density

Emotion Imagination

Onton et al., 2005

Word

memory

(old/new) task

Onton et al., ‘05

Equivalent dipole density

Task A – Old/New Word Memory

dipoledensity()

Onton et al., 2005

Visually

cued

button press

task

Onton et al., ‘05

Equivalent dipole density

Task B – Cued finger movements

dipoledensity()

CPT Flanker FAST EC EO EO EC

Modeling Spatiotemporal Variability

3-Model AMICA Decomposition

Time-on-Task (1.5 hours)

Mod

el Log

Lik

elih

ood

Grainne MacLoughlin & Jason Palmer, 2010

1.! Consider, in so far as possible, the multi-dimensional dynamics of

the brain as expressed in the whole recorded signals.

2.! Un-mix source (and artifact) contributions of individual source

areas using independent component analysis (ICA).

3.! Visualize trial-by-trial relationships of source component activities

to experimental variables (using 2-D ‘ERP-image’ plots).

4.! Model the event-related dynamics of the source components

(using time/frequency analysis).

5.! Localize the separated source areas using biophysical inverse

modeling.

6.! Compare similarities in source dynamics and locations across

subjects using cluster analysis.

7.! Model transient source network dynamics and the contexts in

which they appear.

Mining Event-Related Brain Dynamics

S. Makeig 2010

Mining Event-Related Brain

Dynamics!

1.! Consider, in so far as possible, the multi-dimensional dynamics of

the brain as expressed in the whole recorded signals.

2.! Un-mix source (and artifact) contributions of individual source

areas using independent component analysis (ICA).

3.! Visualize trial-by-trial relationships of source component activities

to experimental variables (using 2-D ‘ERP-image’ plots).

4.! Model the event-related dynamics of the source components

(using time/frequency analysis).

5.! Localize the separated source areas using biophysical inverse

modeling.

6.! Compare similarities in source dynamics and locations across

subjects using cluster analysis.

7.! Model transient source network dynamics and the contexts in

which they appear.

Mining Event-Related Brain Dynamics

S. Makeig 2010

99:;<=!/*#T(1&$!Q!F(-$'+($!

•! 9C6D!B!(4(#&P+(.-&(V!$1(T&+-.!1*N(+!

•! "R/!B!3#&(+P&+3-.!T*[(+(#T(!E1[-$(!.*TG3#8H!

•! 9C/!B!(4(#&P+(.-&(V!T*[(+(#T(!

99:;<=!F(-$'+($!Q!j3$'-.3e-%*#$!

•! (+130-8(EH!B!$*+&(V!&+3-.PS5P&+3-.!V5#-03T$!

•! (#4&*1*EH!B!9CD$!-#V!T*01*#(#&$!

•! k&*1*EH!B!(4(#&P+(.-&(V!$1(T&+-.!1*N(+!T[-#8($!

S. Makeig 2004

ERP-Image Plotting

1.! Display single trials as

color-coded horizontal

lines (e.g., red is +!V,

blue is -!V, green is 0).

2.! Sort all trials according

to some variable of

interest (here, subject

RT).

3.! Smooth vertically.

Jung et al., Human Brain Mapping, 2001.

dB

Time (ms)

Fre

qu

ency (

Hz)

10

ERSP

Makeig et al., PLOS ‘04

timef() tftopo()

EVENT LOCAL PHASE

Inter-trial Coherence (ITC) (“phase-locking factor”)

•! Significant consistency of local phase of a

physiological waveform across successive trials.

delay

EVENT LOCAL PHASE

EVENT LOCAL PHASE

frequency

PHASE LOCKING

AVERAGE ERP

SINGLE TRIALS µV

P = 0.02

P = 0.02

INTER-TRIAL COHERENCE

NO AMPLITUDE INCREASE

400 SIM. TRIALS ...

ERP-IMAGE PLOT

INTER-TRIAL COHERENCE

(phase resetting)

erpimage()

NO AMPLITUDE INCREASE

ITC / PHASE LOCKING

TIME

FR

EQ

UE

NC

Y

ERSP

ITC

dB

µV2

r timef()

Space of time / frequency changes …

ERS ERD Baseline

ITC

N

o IT

C

ERPs are produced by ITC>0, not by power increases

“True” ERP (visual

P1)

ITC

ERP

S. Enghoff

“True” PPR (visual ‘alpha

ringing’)

Event-Related Coherence (ERC)

•! Significant consistency of local phase difference

between two concurrent physiological waveforms.

delay

frequency

PHASE1 EVENT PHASE2

"

PHASE1 EVENT PHASE2 "

PHASE1 EVENT PHASE2 "

Event-related Coherence

TWO SIMULATED THETA PROCESSES

FR

EQ

UE

NC

Y

TIME

EVENT-RELATED COHERENCE

COHERENCE LAG

r

deg

ERC

crossf()

J Klopp, K Marinkovic, P Chauvel, V Nenov, E Halgren Hum Br Map

11:286-293 (2000)

Ant. Cing.

Fusiform

Post. Cing.

Rossen, Makeig, et al..!

I;;<]%PK#1('#-H%;;<%4'K,1*%30-$"#$!

N,-#2"*#9%;;<%C*9T',6%&'##*1()"9/!

Tim Mullen, S. Makeig et al. unpublished

43PN]%>%9''HD'W%I',%0'M*H"#$%

;;<%2'K,1*%"#I',0-('#%^'T%

1 ! Alertness

2 ! Attention

3 ! Arousal

4 ! Anticipation

5 ! Affect

6 ! Awareness

7 ! Agency

8 ! Aha!

What can EEG measure?

Uses for EEG?!

1 ! Alertness

2 ! Attention

3 ! Arousal

4 ! Anticipation

5 ! Affect

6 ! Awareness

7 ! Agency

8 ! Aha!

What can EEG measure?

EEG-based Cognitive-State Monitoring

DSP and Display module

2.5 x 1.5 in

Lin at. al., Proc. IEEE, July 2008.

Estimating Cognitive

states of the drivers

Sample Results

Attended Location

left center right

Fre

qu

necy (

Hz)

10

20

40

left center right

left center right left center right

10

20

40

10

20

40

10

20

40

Baseline Spectrum

(1 s before stimulus)

Right Alpha Left Alpha

Central Alpha

Frontocentral

Component

EEG and Attention

Westerfield & Makeig, 2001

Load 3

Load 5

Load 7

dB

Onton & Makeig, 2006

EEG and Attention

K. Gramann, J. Onton, & S. Makeig, 2008

Clusters distinguishing Turners & Nonturners

Julie Onton & Scott Makeig, Frontiers in Human Neuroscience, 2009

Changes in distribution of broadband high-frequency

EEG power with imagined emotion

+&3=>+]%>%9''HD'W%I',%DK"HM"#$%D,-"#_

1'0@K9*,%"#9*,I-1*%0'M*H2%

_*'+&[!"#&(+#-%*#-.!=/"!F((%#8!<$3.*0-+!F((%#8!:+*'#V$K!D-T3fT!:+*4(K!/<!

L'#(K!XY?Y!

cU!:+-0-##K!RPD!L'#8!W!6!U!F-G(38!!EXYY`H!

!'D"H*%+,-"#O+'M/%30-$"#$%N*1:#'H'$/%

•! /'++(#&!99:K!(5(!&+-TG3#8K!-#V!

0*%*#!T-1&'+(!$5$&(0$!-,*%

@',9-DH*U!!

•! "#!-!)(N!5(-+$K!!"#$#%&"'

03T+*(.(T&+*#3T%!'+3%2/29*02!N3..!

S(!)(-$3S.(U!

Q-,-M"$0%2:"`%

%>=;aN%JJ%!!!!! ! %%b;;<%"2%2'0*9:"#$%9'%K2*E%

%%% % % %#'9%cK29%2'0*9:"#$%9'%29KM/de%

AlanBauer.com

<.&[*'8[!&+-V3%*#-.!99:!-#-.5$3$!0(&[*V$![-4(!'$(V!

+(.-%4(.5!.3l.(!*)!&[(!-S'#V-#&!3#)*+0-%*#!-S*'&!

V3$&+3S'&(V!S+-3#!V5#-03T$!T*#&-3#(V!3#!99:!V-&-!a!

a!#(N!+(T*+V3#8!&(T[#*.*85K!-#V!#(N!-#-.5$3$!

0(&[*V$!S-$(V!3#!S+-3#!1[5$3*.*85!-..*N!03#3#8!

0'T[!0*+(!*)!&[3$!3#)*+0-%*#K!-#V!0-G(!1*$$3S.(!

&[(!-#-.5$3$!*)!#(N!(J1(+30(#&-.!1-+-V380$U!

C*T%I*-2"DH*%-@@H"1-('#2%'I%;;<%3#T.'V(m!

•!!)*+!99:!)((VS-TG!)*+!-l(#%*#!&+-3#3#8!

•!!)*+!>38[PV(f#3%*#!)99:!30-83#8!)*+!T.3#3T-.!+($(-+T[!

•!!)*+!nP[38[PV(f#3%*#!(.(T&+*T*+%T-.!$*'+T(!30-83#8!

•!!)*+!F*S3.(!S+-3#QS*V5!30-83#8!EF*="H!

New dimensions of EEG research and application