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Oscillatory patterns are an important feature prominent in EEG, MEG or local field potentials (LFP). Oscillations in a certain frequency range are often considered as entities related to some function of the brain, such as alpha waves in relaxed wakefulness, sleep spindles or slow oscillations in sleep, or beta and gamma oscillations in some cognitive processes. For single channel data several timefrequency analysis methods exist to analyze such oscillations. Applied to multi-channel data this approach leads to a picture of assigning each channel one oscillator, which becomes somewhat arbitrary if the same oscillation appears in several channels simultaneously. Source localization can cope with this problem, but still assumes the oscillators being localized entities. Here we assume in contrast that they are collective modes of the underlying neural networks, either extended or localized. In [1] we showed how slow parameter changes in a network can be traced based on a method to detect oscillatory patterns in single channel data using linear dynamical models. The present work extends this work to multiple channel measurements in order to identify collective oscillatory modes and to quantify their degree of locality.

bmc neuroscience

Multichannel analysis of neural oscillations in a simple model network – towards a better understanding of the spatiotemporal structure of brain oscillations