Are you interested in studying networks and dynamics in your neuroimaging data?
Do you wish there was an open source Python toolbox that made this easy?
If so, check out our brand new toolbox: osl-dynamics (https://t.co/1m4WXBTOoM).
The toolbox paper: https://t.co/xb09xqtULn shows the dynamic functional networks you can detect in electrophy data and how you can use osl-dynamics to detect oscillatory bursts in single channel data.
If anyone is curious about optically pumped magnetometers and their use in MEG, hopefully this @TrendsNeuro review paper might be helpful!? Massive thanks to the @trendsNeuro editor, and of course the @UoN_MEG gang for all the help putting this together. https://t.co/X124bPzk5O
Next generation Dynamic Network Modes; Using deep learning to go beyond the HMM...
I hope everyone is ready for this!
Massive congrats to @ChetanG83095127 , Evan Roberts, @blobsonthebrain @markwoolrich and co-authors.
@OxfordWIN @OxPsychiatry
https://t.co/qoaPIBm3oI
"Connectomics of Human Electrophysiology"
for your enjoyment & feedback.
A true pleasure working on this review & position piece w/ @CONNECTlab_UIUC & @MattBrookesMEG.
https://t.co/4xxPTp8o4X
Non-sinusoidal shape is an important feature of neuronal oscillations - rich dynamics are visible by eye but hard to analyse...
Glad to present our preprint using EMD, instantaneous frequency and phase-alignment to quantify single-cycle shape profiles!
https://t.co/DX9YIH2Xhg
NEW Unit Paper | EMD: Empirical Mode Decomposition and Hilbert-Huang spectral analyses in Python. Out now in @JOSS_TheOJ. Great collab w/ @OxfordWIN @OxExpPsy. New tools for analysing brain oscillations. @NDCNOxford@The_MRC@OxNeuro https://t.co/gQGTCje1Pv
Our preprint on oscillatory network analysis with Spatio-Spectral Eigenmodes is up on #biorxiv_neursci https://t.co/pD0ip85tG4 @OxfordWIN @OxPsychiatry@UOY_YNiC
This was a long journey – My first git commit for this project in was in August 2014.... #BetterLateThanNever