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

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

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

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.

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!