Third order spectral analysis robust to mixing artifacts for mapping cross-frequency interactions in EEG/MEG.

We present a novel approach to the third order spectral analysis, commonly called bispectral analysis, of electroencephalographic (EEG) and magnetoencephalographic (MEG) data for studying cross-frequency functional brain connectivity. The main obstacle in estimating functional connectivity from EEG and MEG measurements lies in the signals being a largely unknown mixture of the activities of the underlying brain sources. This often constitutes a severe confounder and heavily affects the detection of brain source interactions. To overcome this problem, we previously developed metrics based on the properties of the imaginary part of coherency. Here, we generalize these properties from the linear to the nonlinear case. Specifically, we propose a metric based on an antisymmetric combination of cross-bispectra, which we demonstrate to be robust to mixing artifacts. Moreover, our metric provides complex-valued quantities that give the opportunity to study phase relationships between brain sources. The effectiveness of the method is first demonstrated on simulated EEG data. The proposed approach shows a reduced sensitivity to mixing artifacts when compared with a traditional bispectral metric. It also exhibits a better performance in extracting phase relationships between sources than the imaginary part of the cross-spectrum for delayed interactions. The method is then applied to real EEG data recorded during resting state. A cross-frequency interaction is observed between brain sources at 10Hz and 20Hz, i.e., for alpha and beta rhythms. This interaction is then projected from signal to source level by using a fit-based procedure. This approach highlights a 10-20Hz dominant interaction localized in an occipito-parieto-central network.

Calibration of a multichannel MEG system based on the signal space separation method.

For an efficient use of multichannel MEG systems, an accurate sensor calibration is extremely important. This includes the knowledge of both channel sensitivities and channel arrangement, which can deviate from original system plans, e.g., because of thermal stresses. In this paper, we propose a new solution to the calibration of a multichannel MEG sensor array based on the signal space separation (SSS) method. It has been shown that an inaccurate knowledge of sensor calibration limits the performances of the SSS method, resulting in a mismatch between the measured neuromagnetic field and its SSS reconstruction. Given a set of known magnetic sources, we show that an objective function, which strongly depends on sensor geometry, can be derived from the principal angle between the measured vector signal and the SSS basis. Hence, the MEG sensor array calibration is carried out by minimizing the objective function through a standard large-scale optimization technique. Details on the magnetic sources and calibration process are presented here. Finally, an application to the calibration of the 153-channel whole-head MEG system installed at the University of Chieti is discussed.