# Independent Component Analysis (ICA)

Independent component analysis (ICA) is a signal processing method to

separate Independent sources linearly mixed in several sensors. The first ICA

paper was published in 1986 and has been applied to EEG since 1996. At

Integrated Neuroscience Services we apply ICA to your EEG data using the

MATLAB based EEGLAB toolbox, developed by Arnaud Delorme and Scott

Makeig, to produce maximal statistically independent components that

present specific patterns of Coherence. Which can then be used to generate

a specialized protocol for your clients. Utilizing ICA with EEG data

distinguishes the issue of source localization from that of source

identification

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ICA works by unmixing brain sources and artifacts detected by sensors

to produce the true underlying source components or waveforms. From these

source components, brain components are retained and artifact components

are removed, producing a clean EEG. This process is necessary because the

waveforms observed at each electrode site is a weighted sum of many

source waveforms. The wave form observed at a given electrode site is a

sum of each source waveform multiplied by the weighting factor between

each component and that electrode site. This weighting factor is derived

from the position of the source relative to the electrode, and the conductivity

of the tissue. The distribution of source waveforms on the scalp can then be

understood as a matrix of weights, Source X Electrodes. Noise or artifact

which are independent from neurogenic source waveforms are independent

sources included in the mixing matrix.

After the weight matrix is applied to the EEG, ICA generates the time-course

of ICs, scalp topography maps and dipole locations for each IC. The time

course indicates the strength of each component at any given time in

microvolts, the scalp topography describes the strength of the IC at each

electrode site, and the dipole shows the modeled location and positon of ICs

in the brain. ICs will often display a dipolar distribution on scalp topography,

which is indicative of a good component. This dipole is different from but

consistent with the current dipole, which represents the activity of functional

brain regions. These images in combination with other information allows us

to distinguish IC components reflecting underlying true components of

functional brain regions from artifacts. The corrected EEG is produced by

multiplying the time-course of the ICs by the inverse of the weight matrix.

The artifacts are removed by zeroing out their time-course in the

multiplication process.

ICA unmixes these sources by forming a weight matrix, Components X

Electrodes. This matrix produces a set of weighted values which is then

multiplied by the EEG time-course to produce statistically independent

components (ICs) that may or may not correspond to true neural sources.

These IC waveforms are analogous to the original source waveforms pre-

mixing at sensors. However, because ICs are set equal to the number of

channels in the EEG, multiple source components can be collapsed into one

IC and one source component can be split into multiple ICs. Hence, the

quality of data at the recording of the EEG is important to reduce the

collapsing and splitting of artifacts into ICs and to maximize production of

true components.

The corrected EEG is then ran through a connectivity analysis of

coherence (multivariate grange-causality) and graph theory analysis of

connectivity; using MVGC MATLAB toolbox, which is based on advanced

vector auto-regression theory. Granger- Causality is a statistical notion of

causality applied to time-series data, whereby, cause precedes, and helps

predict effect. Defined in both time and frequency domains and allows for

conditioning out of common influences. Otherwise, a test for determining

whether one time-series is useful in predicting another. Modern neuroscience

takes a network-centric approach to describing brain function and cognition.

In which information, flows between multi-leveled, evolving network

structures. Using the dipoles produced by ICA as vectors in a network model,

multivariate granger-causality provides information about the level of

functional communication between regions; the flow of information, its effect

size, and the strength of communication between vectors.

An additional means of measuring brain connectivity is graph theory

analysis. When applied to EEG we can better understand the directional

connectivity of brain networks. We define the nodes of the graph as the

dipoles and the edges as the functional connectivity between these dipoles.

The graph describes the direction of communication between nodes, and

weights are applied to the edges to represent the strength of the links

between the nodes. Furthermore, network measures such as cluster

coefficient, and path length are extracted from the graph theory for further

interpretation. All of these data and images better allow us to produce a

specialized protocol for your clients. Interpretations of these data and images

are presented and reviewed during the online video consolation.

View a sample report below!