Analyzing the waveshape of brain oscillations with bicoherence.

Oscillations are characteristic features of brain activity and have traditionally been categorized into frequency bands. Despite this categorization, brain oscillations have non-sinusoidal waveshape features, which have recently been discussed for their potential to mislead cross-frequency coupling measures. Waveshape characteristics deserve attention in their own right, as they are a direct reflection of the underlying neurophysiology and have shown to be altered in conditions such as Parkinson's disease. Here, we want to contribute to waveshape analysis in three steps: (1) While "shape" is most intuitively described in the time domain, complementary information is provided by frequency domain. In particular we show, that the bispectrum of an oscillation directly reflects waveshape properties such as differences in the steepness of its rise and decay phases, as well as differences in the duration of its crests and troughs. (2) Methods for the extraction of brain oscillations need to be chosen with care, as the ubiquitous use of bandpass filters causes waveshape distortions. We illustrate common problems and introduce a waveshape-preserving spatial filter for the purpose of waveshape analysis. (3) In an exemplary analysis of resting-state alpha rhythms, bicoherence provides evidence that shape characteristics of alpha rhythms exist on a spectrum. In addition, the bispectral view identifies significant mu rhythm anomalies in schizophrenia and suggests potential causes relating to waveshape.

Objective quality assessment of stereoscopic images with vertical disparity using EEG.

OBJECTIVE: Neurophysiological correlates of vertical disparity in 3D images are studied in an objective approach using EEG technique. These disparities are known to negatively affect the quality of experience and to cause visual discomfort in stereoscopic visualizations. APPROACH: We have presented four conditions to subjects: one in 2D and three conditions in 3D, one without vertical disparity and two with different vertical disparity levels. Event related potentials (ERPs) are measured for each condition and the differences between ERP components are studied. Analysis is also performed on the induced potentials in the time frequency domain. MAIN RESULTS: Results show that there is a significant increase in the amplitude of P1 components in 3D conditions in comparison to 2D. These results are consistent with previous studies which have shown that P1 amplitude increases due to the depth perception in 3D compared to 2D. However the amplitude is significantly smaller for maximum vertical disparity (3D-3) in comparison to 3D with no vertical disparity. Our results therefore suggest that the vertical disparity in 3D-3 condition decreases the perception of depth compared to other 3D conditions and the amplitude of P1 component can be used as a discriminative feature. SIGNIFICANCE: The results show that the P1 component increases in amplitude due to the depth perception in the 3D stimuli compared to the 2D stimulus. On the other hand the vertical disparity in the stereoscopic images is studied here. We suggest that the amplitude of P1 component is modulated with this parameter and decreases due to the decrease in the perception of depth.

Wedge MUSIC: a novel approach to examine experimental differences of brain source connectivity patterns from EEG/MEG data.

We introduce a novel method to estimate bivariate synchronization, i.e. interacting brain sources at a specific frequency or band, from MEG or EEG data robust to artifacts of volume conduction. The data driven calculation is solely based on the imaginary part of the cross-spectrum as opposed to the imaginary part of coherency. In principle, the method quantifies how strong a synchronization between a distinct pair of brain sources is present in the data. As an input of the method all pairs of pre-defined locations inside the brain can be used which is computationally exhaustive. In contrast to that, reference sources can be used that have been identified by any source reconstruction technique in a prior analysis step. We introduce different variants of the method and evaluate the performance in simulations. As a particular advantage of the proposed methodology, we demonstrate that the novel approach is capable of investigating differences in brain source interactions between experimental conditions or with respect to a certain baseline. For measured data, we first show the application on resting state MEG data where we find locally synchronized sources in the motor-cortex based on the sensorimotor idle rhythms. Finally, we show an example on EEG motor imagery data where we contrast hand and foot movements. Here, we also find local interactions in the expected brain areas.

Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index: a simulation study.

The investigation of functional neuronal synchronization has recently become a growing field of research. With high temporal resolution, electroencephalography and magnetoencephalography are well-suited measurement techniques to identify networks of interacting sources underlying the recorded data. The analysis of the data in terms of effective connectivity, nevertheless, contains intrinsic issues such as the problem of volume conduction and the non-uniqueness of the inverse solution. Here, we briefly introduce a series of existing methods assessing these problems. To determine the locations of interacting brain sources robust to volume conduction, all computations are solely based on the imaginary part of the cross-spectrum as a trustworthy source of information. Furthermore, we demonstrate the feasibility of estimating causal relationships of systems of neuronal sources with the phase slope index in realistically simulated data. Finally, advantages and drawbacks of the applied methodology are highlighted and discussed.