The transformation steps before applying the metric.

<p>a) The original EFP. b) Expanding y-axes to a minimum resolution of 1Hz. c) Collapsing y-axes to a uniform frequency band division. d) Reshaping EFP to a vector.</p

Down-regulating the common EFP signal amplitude.

<p>a) Mean results of the amygdala common EFP-NF. The <i>y</i> axis shows the mean cEFP amplitude during BL (left columns) and NF (right columns). Only the test group (red columns, <i>n</i> = 7) had significantly reduced cEFP amplitude during NF relative to BL (F(1,11) = 24.46, **<i>p</i><0.01). b) Individual results of the common EFP-NF. The <i>y</i> axis shows the cEFP amplitude during NF and the <i>x</i> axis shows the cEFP amplitude during BL. Markers (red = test; blue = sham) below the diagonal represent subjects that during NF reduced cEFP activity relative to BL. 6 out of 7 subjects from the test group could significantly reduce cEFP activity during NF relative to BL compared with only 1 out 6 subjects in the sham group. <i>*p</i><0.05, <i>**p</i><0.01, and <i>n</i> = 13. For illustration purposes, the cEFP amplitude of the BL for each subject was multiplied by the NF mean. The actual range of the cEFP amplitude during BL was (-0.2)-(0.34).</p

Comparison between the cEFP and the EFP performances.

<p>a) Depicts the performance of the individual model constructed for the first session when testing on the second session. It compared with the cEFP performance on the same sessions. The results are an average over subjects whose first session was included in the common model construction process (<i>n</i> = 9). b) Compares the cEFP performance with the performance of two ‘optimal’ EFPs, when applied to a group of new subjects (<i>n</i> = 18, 9 subjects). In Fig 5a and 5b, the star's color represents the method that obtained significance (*<i>p</i> < 0.05). The error bars are standard deviations over sessions. c) Depicts the cEFP percentage change histogram (relative to EFP).</p

Scheme of the common model construction framework.

<p>The samples were divided in a leave-one-out manner into training and testing sets. The training set was used for model selection and the testing set was used for model validation. An inner cross-validation was used for choosing the optimal model (i.e. finding the model coefficients and the best regularization parameter) based on regularized ridge-regression training. The training input was the time-frequency representation of the EEG data and the training target was the fMRI BOLD signal in the amygdala. Each time-point in the BOLD signal corresponded to a time-window in the EEG. The resultant model coefficients suggest frequency bands and time delays that correlate to the BOLD activity in the amygdala.</p

Scheme of the virtual machine used during EEG-NF training.

<p>The virtual machine receives the last 3 seconds in the EEG data and returns the predicted BOLD value that corresponds to the last change. a) New EEG segment is attached. b) Preprocessing of the buffer. c-d) Last 12 seconds are multiplied by the cEFP matrix coefficients to calculate the next point on the cEFP signal.</p

Common EFP characteristics.

<p>a) Individual EFP prediction correlation coefficient on the validation set using different electrodes on the back of the brain, averaged over all 'successful' sessions (i.e., those with prediction correlation coefficients greater than 0.6 on the validation set using any electrode, <i>n</i> = 26). The electrodes are sorted according to their signal-to-noise ratio (<i>μ∕σ</i>). b) The dendrogram of the clustering results and the EFPs’ coefficients in the leaves. The different clusters are marked in different colors. The 10 selected sessions, belonging to the biggest cluster, are marked in red. c) The cEFP frequency bands divide the averaged spectral logarithmic mean of the EEG data across the 10 selected sessions to 10 equal areas.</p