Journal: Frontiers in Human Neuroscience

Article Title: Detection of EEG-resting state independent networks by eLORETA-ICA method

doi: 10.3389/fnhum.2015.00031

Figure Lengend Snippet: eLORETA-ICA component 1, 2, 3, 7, 8 and 11 in above written frequency bands . These components correspond to artifacts of electromyogram or baseline shifts, based on spatial distributions of power and frequency ranges.

Article Snippet: Furthermore, ICA can remove artifacts such as electromyogram or base line shift by separating out artifact components (Custo et al., ).

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Article Title: Imaging Brain Dynamics Using Independent Component Analysis

doi: 10.1109/5.939827

Figure Lengend Snippet: (a) Grand mean evoked response to detected target stimuli in the detection task (average of responses from ten subjects and five attended locations). Response waveform at all 29 scalp channels and two EOG channels are plotted on a common axis. Topographic plots of the scalp distribution of the response at four indicated latencies show that the LPC topography is labile, presumably reflecting the summation at the electrodes of potentials generated by temporally overlapping activations in several brain areas each having broad but topographically fixed projections to the scalp. All scalp maps shown individually scaled to increase color contrast, with polarities at their maximum projection as indicated in the color bar. (b) Activation time courses and scalp maps of the four LPC components produced by Infomax ICA applied to 75 1-s grand-mean (10-Ss) ERPs from both tasks. Map scaling as in (a). The thick dotted line (left) indicates stimulus onset. Mean subject-median response times (RTs) in the Detection task (red) and Discrimination task (blue) are indicated by solid vertical bars. Three independent components (P3f, P3b, Pmp) accounted for 95%–98% of response variance in both tasks. In both tasks, median RT coincided with Pmp onset. The faint vertical dotted line near 250 ms shows that the P3f time courses for targets and “nogo” nontargets (presented in the target location) just at the onset of the left-sided Pnt component, which was active only in this condition. (c) Envelopes of the scalp projections of maximally independent component P3f, (red filled) superimposed on the mean response envelopes (black outlines) for all 5 × 5 response conditions of the Detection task. (d) The top panels show the grand mean target response at two scalp channels, Fz and Pz (thick traces), and the projections of the two largest ICA components, P3b and Pmp, to the same channels (thin traces). The central panel shows a scatter plot of ten average target ERPs at the two electrodes. The data contain two strongly radial (and, therefore, spatially fixed) features. The dashed lines (middle panel) show the directions associated with components P3b and Pmp in these data, as determined by the relative projection strengths of each component to these two scalp channels (shown below as black dots on the component scalp maps). The degree of data entropy attained by ICA training is illustrated by the (center right) plot insert, which shows the (31-channel) scatter-plotted data after logistic transformation and rotation to the two component axes (from [25] by permission).

Article Snippet: First, we will examine an application of ICA to natural images that supports an Infomax-based theory of perceptual brain organization [ 17 ] and also illustrates the nature of independence.

Techniques: Generated, Activation Assay, Produced, Transformation Assay