Swatantra Ghataka Visleshane

7/28/2019 Swatantra Ghataka Visleshane

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Independent Component Analysis (ICA)

In the context of EEG, MEG and applications in other modalities

Sriranga Kashyap

I6046171

Course: PSY4256 - Timing Neural Processing with EEG and MEG


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We can complain because rose bushes have thorns or rejoice because thorn bushes have roses

- Abraham Lincoln

Abstract

The phenomenal advancements in technology in the past decade have made it possible to

study non-invasively, various hitherto unthinkable, properties of the living human brain. This

results in a lot of data and thus, important to extract the essential features from this data to

allow interpretation of its properties. Traditional approaches to solve this feature extraction

include principal component analysis (PCA) and factor analysis (FA). This paper focuses on

using a novel data-driven method, independent component analysis (ICA) that allows blind

separation of sources by assuming their statistical independence. The first part of the paper

reviews ICA and compares ICA with PCA and FA (non-technically). The next part addressesICA in the context of vintage imaging methods such as EEG and MEG, its role in source

separation and artefact rejection. The final part emphasizes the versatility of ICA by

describing applications to functional imaging, combined modalities and discusses a recent

application to source characterization.

Keywordsindependent component analysis, artefact, source separation, EEG, MEG


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Table of Contents

Abstract ....................................................................................................................................... i

1 Introduction ........................................................................................................................ 1

1.1 Overview ..................................................................................................................... 1

1.2 ICA vs. PCA and FA ................................................................................................... 1

2 ICA applied to the neural cocktail party (Brown et al., 2001) ........................................ 2

2.1 In the context of EEG .................................................................................................. 2

2.1.1 Artefact Rejection ................................................................................................ 2

2.1.2 Artefact Rejection: ICA vs. conventional statistical/spectral analyses ................ 2

2.1.3 ICA Assumptions in context of EEG ................................................................... 3

2.2 In the context of MEG ................................................................................................. 4

2.2.1 Artefact Rejection ................................................................................................ 4

2.2.2 Artefact Rejection: ICA-based ............................................................................. 4

3 Applications in other modalities ........................................................................................ 6

3.1 Application to fMRI data ............................................................................................ 6

3.2 Application to concurrent EEG-fMRI data ................................................................. 7

3.2.1 Application to EEG-fMRI at 9.4T ....................................................................... 7

3.3 Application to fNIRS .................................................................................................. 7

3.4 Application to source characterization ........................................................................ 8

Concluding Remark ................................................................................................................... 8

References ................................................................................................................................ iii


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Table of Figures

Figure 1. Cocktail Party Problem............................................................................................... 1

Figure 2. Sources of ERP ........................................................................................................... 2

Figure 3. Artefact detection performance with the five methods (columns) applied to

five types of simulated artefacts (rows). Black traces: applied optimally to the

best single-channel data for each artefact type. Grey traces: applied to the

best single independent components computed from the data by Infomax ICA

(Delorme et al., 2007)................................................................................................ 3

Figure 4. Sample of MEG signals showing artefacts produced by blinking, saccades,

biting and cardiac cycle. For each of the 6 positions shown, the two

orthogonal directions of the sensors are plotted (Vigrio et al., 2000) ..................... 5

Figure 5. Six independent components extracted from the MEG data containing several

artifacts. For each component the left, back and right views of the field

patterns are shown. Full lines stand for magnetic flux coming from the head,

and dotted lines the flux inwards ............................................................................... 5

Figure 6. a) The need for higher order statistics, b) Comparison of GLM and spatial

ICA for fMRI data, c) Spatial ICA of fMRI data (Vince D Calhoun, Liu, &

Adali, 2009). .............................................................................................................. 6
http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851150http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851151http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851153http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851153http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851153http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851154http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851154http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851154http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851154http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851154http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851154http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851154http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851154http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851153http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851153http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851153http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851151http://c/Users/Kashyap/Desktop/EEG%20REPORT/Independent%20Component%20Analysis.docx%23_Toc356851150


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1 Introduction1.1 OverviewA problem often incorrectly phrased is that of an overload of information especially with new

technology. However, the problem in reality is that there is an overload of data and relativelysmall amount of useful information. Independent component analysis (ICA) is a method of

extracting this useful information from the data.

ICA belongs to a class of blind source separation (BSS) methods for separating raw data

(signal mixtures) into components of information (source signals). The blind in blind

source separation implies that the signal mixture can be separated into source signals without

a priori knowledge of the nature of the source signals.

ICA can be best understood with the

cocktail party problem (Stone, 2002).

Consider two people speaking at the same

time in a room with two microphones. If

each voice signal is examined on a fine time

scale it is observed that the amplitude of

one voice is unrelated to the amplitude of

the other voice at a given point in time. The

reason is because they are generated by two

unrelated physical processes (i.e. by two different people). Therefore, a strategy to separate

the voice mixtures is to look for the unrelated time-varying signals within these mixtures.

1.2 ICA vs. PCA and FAICA is an improvement over conventional methods such as Principal Component Analysis

(PCA) and Factor Analysis (FA). ICA identifies a set of independent source signals whereas

PCA and FA find set of signals that are uncorrelated with each other. With respect to the

aforementioned example, PCA and FA would extract a new set of voice mixtures which are

uncorrelated with each other but mixtures nonetheless. In contrast, ICA would extract a set of

independent signals which would be a set of individual voices. ICA is an improvement

because: it assumes independence (independence implies a lack of correlation but lack of

correlation does not imply independence) and based on higher order statistics (whereas PCA

is based on second order statistics). Therefore, the result obtained by ICA is expected to be

more meaningful than the one gained by PCA.

Figure 1. Cocktail Party Problem


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2 ICA applied to the neural cocktail party(Brown et al., 2001)2.1 In the context of EEG

Brain-generated EEG data is understood to represent

measure of the synchronous aspects of local fieldpotentials of radially arranged pyramidal cells in the

cortex. ICA identifies signals in recorded multi-

channel EEG data mixtures whose time courses are

maximally independent of one other.

The problem of source separation is an inductive

inference problem. There is not enough information

to deduce the solution, so one must use available

information to infer the most probable solution.

Therefore, it is important to realise that ICA is not a

solution to the general inverse problem in EEG.

However, what ICA does do is estimate the relative

projection weights of the maximally independent sources and the distinct signals in the

volume-conducted data, therefore, simplify the problem of source localization not necessarily

solve it (Makeig, Debener, Onton, & Delorme, 2004). However, EEG data also includes non-

brain signals or artefactual signals that are linearly mixed with brain EEG source activities at

the scalp electrodes.

2.1.1 Artefact RejectionICA can efficiently separate out stereotyped artefacts such as electromyographic (EMG),

electrooculographic (EOG), electrocardiographic (ECG) signals and single-channel noise

produced by loose connections between electrodes and the scalp (Jung et al., 2000). Non-

stereotyped artefacts are those produced by participant movements, tugs on electrode cables

etc. induce several of unique scalp maps and therefore, so these are best removed from the

data before ICA decomposition.

2.1.2 Artefact Rejection: ICA vs. conventional statistical/spectral analysesA quantitative comparison of 5 different statistical/spectral analysis methods available in the

EEGLAB toolbox (Extreme values, Linear trends, Data improbability, Kurtosis, Spectral

pattern) to detect artefacts in the data and the same procedures to the data decomposed using

ICA was carried out by Delorme, Sejnowski, & Makeig, 2007. The results are presented in

Figure 3.

Figure 2. Sources of ERP


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Figure 3. Artefact detection performance with the five methods (columns) applied to five types of

simulated artefacts (rows). Black traces: applied optimally to the best single-channel data for each

artefact type. Grey traces: applied to the best single independent components computed from the data by

Infomax ICA (Delorme et al., 2007).

It can be easily observed from Figure 3., that the artefact detection performance (artefacts

detected minus non-detected artefacts, divided by the total number of artefacts) for artefacts

less than 40dB, all the detection methods performed better when applied to the independent

component data.

2.1.3 ICA Assumptions in context of EEG1. ICA component projections are summed linearly at scalp electrodes.

- This assumption is fulfilled in the case of EEG recordings which is why during anICA decomposition, the selection of a reference electrode (or re-referencing) is

not very important

2. Sources are independent- This assumption is more plausible than the one that brain networks are physically

isolated from one another (therefore, assuming it to be possible to clearly localize

the source signals)


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- "sources" of ICA components are possibly distributed brain networks and may befunctionally linked.

- If during the decomposition of spontaneous or event-related single-trial EEG data,an ICA component map turns out to be compatible with a possible compact

generator region in cortex then it is imperative to understand that this is not

because ICA had this as an objective but because a coherent signal source,

independent of other sources, projected to the electrodes in this pattern

- Since the time courses of artefacts and the triggering brain events are differentacross most trials, they will be accounted for by different independent

components (Jung et al., 2000).

3. non-Gaussianity of the activity distributions- This assumption is plausible for artefactual activity as they are quite sparsely

active and therefore, not-Gaussian.

2.2 In the context of MEGMEG data consists of an unknown mixture of noise, artefact and signals from unknown brain

electric sources. As was the case with EEG, these sources are cortical networks that are

sequentially activated to perform simple or complex tasks. However, MEG is designed to

pick up very minute magnetic activity and therefore, MEG recorded source activity is not

unique to the true distribution of brain sources but contains unique artefacts. This makes it

challenging to remove such artefacts usual generated by electrical activity in the body and

necessitates and implementation of ICA for MEG data.

2.2.1 Artefact RejectionThe most commonly used artefact correction method is rejection, based on just blatantly

discarding portions of MEG that coincide with artefacts. Other methods are to instruct the

subject to refrain from producing the artefacts (fixate on a target to avoid eye-related artefacts

or relax to avoid muscular artefacts). The effectiveness of these methods and their end result

is loss of data and questionable design in studies of neurological patients or other non-co-

operative subjects.

2.2.2 Artefact Rejection: ICA-basedSuccess of ICA to EEG data suggests an application to MEG, based on the assumption that

the brain activity and the artefacts are anatomically and physiologically separate processes

and that their independence is reflected in the statistical relation between the magnetic signals

generated by these processes.


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Artefact identification in MEG

recordings, using ICA, has been

reported by Vigrio, Srel,

Jousmki, Hmlinen, & Oja,

2000.

Figure 4. shows a subset of 12

MEG signals from a total of 122,

from frontal, occipital and

temporal regions. Several artefacts

such as eye and muscle activity

have been shown to be possible to

extract using ICA and therefore,

applied to the dataset.

Figure 5. shows these artefacts

extracted by ICA. IC1 and IC2 are

clearly muscle artefacts identifiable

by their high frequency. IC3 and

IC5 are characteristic of horizontal

eye movements and blinks

respectively. Interestingly, other

disturbances with weaker SNR such

as heart beat and even a watch can

be extracted (IC4 and IC6).

Therefore, the basic assumption of

ICA that the brain and artefact

waveforms are independent can be

verified by the known differences in

physiological origins of those

signals. In some event-related

designs it can be very challenging,

for example, when presenting

Figure 4. Sample of MEG signals showing artefacts produced

by blinking, saccades, biting and cardiac cycle. For each of the

6 positions shown, the two orthogonal directions of the sensors

are plotted (Vigrio et al., 2000)

Figure 5. Six independent components extracted from the MEG

data containing several artifacts. For each component the left,

back and right views of the field patterns are shown. Full lines

stand for magnetic flux coming from the head, and dotted lines

the flux inwards


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infrequent or painful stimuli, because both the cerebral and ocular signals would be time-

locked to the presented stimulus. However, the property of independence of the two signals is

a measure of the similarity between the joint amplitude distribution and the product of

individual signal distribution calculated along the entire signal, not only in the vicinity of the

presented stimulus. Therefore, we can expect the local relation between the signals, during

the stimulus presentation period, to not affect theirglobal statistical relation.

3 Applications in other modalitiesAs we have seen earlier, ICA has proven to be a powerful and versatile data-driven approach

for studying the brain. This versatility allows us to use ICA in different modalities used in

neuroscience research. Another remarkable feature that enables ICA usage is, although the

nature of the type of signal measured by each modality does not affect ICA, which as

discussed earlier is an excellent Blind Source Separation method.

3.1 Application to fMRI data

Figure 6. a) The need for higher order statistics, b) Comparison of GLM and spatial ICA for fMRI data,

c) Spatial ICA of fMRI data (Vince D Calhoun, Liu, & Adali, 2009).

Figure 6a. shows that principle component analysis (PCA) finds orthogonal directions which

explain the maximum variance (second order) whereas ICA identifies maximally independentdirections utilising higher order statistics. In Figure 6b. we can see a comparison of GLM (the

conventional method) with ICA, where the spatial ICA identifies temporally coherent,

systematically non-overlapping brain regions without requiring a modelled temporal

response. Figure 6c. illustrates the application of ICA to the fMRI data, which is assumed to

be a composed of linearly mixed sources, which are then extracted along with their respective

time courses.

The strength of ICA in fMRI is its ability to reveal temporal dynamics even when a model is

not available.


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3.2 Application to concurrent EEG-fMRI dataThe 21st century has seen exponential improvements in technology that have enabled

collection of multiple types of imaging data from participants and it has become popular to

do so, simultaneously using two different modalities providing information in two separate

domains. For instance, EEG informs us more efficiently on a temporal domain and fMRI

spatially.

ICA has been implemented for parallel decomposition of EEG and fMRI and joint ICA of the

multimodal data. It is important to note that this involves a strong assumption that the linear

covariation for both modalities is the same. However, it has the advantage of providing a

parsimonious way to link multiple data types (fusing ERP and haemodynamic data) as

demonstrated by V D Calhoun, Adali, Pearlson, & Kiehl, 2006.

3.2.1 Application to EEG-fMRI at 9.4TThis decade has had several firsts. So was for EEG -fMRI, Neuner et al. who investigated

the feasibility of recording EEG inside a 9.4 T static magnetic field, specifically to determine

whether meaningful EEG information could be recovered from the data after removal of the

cardiac-related artefact using ICA.

The progression to higher field strengths will not affect the temporal resolution of the EEG,

therefore, it is still as valuable as it was at lower field strengths. Also, fMRI signals will be

acquired at ultra-high fields resulting in increased localisation and sub-millimetre precision.

The price to be paid, however, is with the simultaneous acquisition. Artefacts from fMRI

gradient switching as well as physiological cardiac artefacts will contaminate the EEG signal

(Mullinger, Brookes, & Stevenson, 2008).

The artefact resulting from gradient switching can be corrected for rather easily. This s

because it is generated by the MR scanner and will therefore be consistently reproducible

across a session (Allen, Josephs, & Turner, 2000). The cardiac artefacts are naturally far more

variable. Neuner et al. were able to correct for the cardiac-related artefact and identify

auditory event-related responses at 9.4 T in 75% of subjects using ICA. This shows that ICA

is opening up new horizons for research questions that were hitherto extremely difficult to

address.

3.3 Application to fNIRSZhang et al. showed that ICA is an excellent method for the detection of resting-state brain

functional connectivity from fNIRS measurements. They show that ICA performs better than

the traditional approach (seed correlation) and achieves this with higher sensitivity and

greater specificity. ICA was also effective in cleaning artefacts from the data thereby


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producing reliable functional connectivity results. Therefore, in totality ICA seems to be a

promising approach to investigate functional connectivity based on fNIRS (Zhang et al.,

2010).

3.4 Application to source characterizationICA has been widely accepted to remove artefactual data from EEG and other neuroimaging

data. However, its use to isolate and characterize cortical sources only just beginning to

increase.

It can be said that the far-field signal that arises from a patch of local spatiotemporal field

synchrony should nearly be independent from any such signal that arises anywhere else in the

cortex and the net far-field projection of such a patch of cortex will be nearly equal to a single

equivalent current dipole located in the vicinity or ideally beneath the generating cortical

patch (Delorme, Palmer, Onton, Oostenveld, & Makeig, 2012). This concept does not seem

counterintuitive and can be explained by the high anatomic bias in cortical connectivity

toward connections in the vicinity (


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Calhoun, V D, Adali, T., Pearlson, G. D., & Kiehl, K. a. (2006). Neuronal chronometry of

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Swatantra Ghataka Visleshane