Developing new analysis tools for understanding human brain activity
The OHBA Analysis Group develops novel computational methodologies for analysing neuroimaging data, in order to investigate the human brain in fundamental and clinical neuroscience research. We use techniques from Bayesian statistics, machine learning, pattern recognition and image/signal processing.
OSL-ephys is a python package built on MNE-python for the analysis of MEG and EEG data. OSL-ephys includes configurations and batching for easy parallel pipeline processing over multiple sessions of data. It also includes a purely volumetric source reconstruction pipeline using RHINO, which uses FSL to handle surfaces (as opposed to Freesurfer).
OSL-dynamics is a python package for inferring (network) dynamics and functional connectivity in neuroimaging data. Includes python version of the HMM, and Dynemo (Dynamic Network Modes). Can also compute static power spectra and functional connectivity. Uses Tensorflow.
EMD is an analysis approach for characterising non-stationary and non-sinusoidal oscillations. The EMD toolbox is a python package containing a range of decompositions and functions for instantaneous spectral analyses
HMM-MAR is a Matlab toolbox to identify recurrent brain states of distinct multi-region spectral properties, providing parametric and nonparametric estimations of power, coherence and partial directed coherence for each state
See OSL-dynamics for a Python version of the HMM, and includes alternative models of dynamics.
