About us

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.

Our projects


OHBA Software Library

OSL is a python package built on MNE-python for the analysis of MEG and EEG data. Includes RHINO, which uses FSL for surface extraction (as opposed to Freesurfer), as part of the coregistration. Includes volumetric beamforming.


Models for inferring dynamics and functional connectivity in neuroimaging data.

OSL-dynamics is a python package for inferring 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.


Empirical Mode Decomposition for Neuronal Oscillations

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


Segmentation and characterisation of transient connectivity

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