Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …

As the EEG inverse problem does not have a unique solution, the sources reconstructed
from EEG and their connectivity properties depend on forward and inverse modeling …

Many different analysis techniques have been developed and applied to EEG recordings
that allow one to investigate how different brain areas interact. One particular class of …

Due to its high temporal resolution, electroencephalography (EEG) is widely used to study
functional and effective brain connectivity. Yet, there is currently a mismatch between the …

Exploratory analysis of time series data can yield a better understanding of complex
dynamical systems. Granger causality is a practical framework for analysing interactions in …

The brain's remarkable capacity for language requires bidirectional interactions between
functionally specialized brain regions. We used magnetoencephalography to investigate …

Nasal breathing generates a rhythmic signal which entrains cortical network oscillations in
widespread brain regions on a cycle-to-cycle time scale. It is unknown, however, how …

Aggregating voxel-level statistical dependencies between multivariate time series is an
important intermediate step when characterising functional connectivity (FC) between larger …

Data from experiments and theoretical arguments are the two pillars sustaining the job of
modeling physical systems through inference. In order to solve the inference problem, the …

The quantification of resting‐state electroencephalography (EEG) is associated with a
variety of measures. These include power estimates at different frequencies, microstate …