David Haydock
@davidghaydock.bsky.social
Post-doc doing Neuroimaging @ucl.ac.uk
Interested in Neurophenomenology, and how we can develop analysis methods that benefit it
https://linktr.ee/davidghaydock
Interested in Neurophenomenology, and how we can develop analysis methods that benefit it
https://linktr.ee/davidghaydock
Beyond this, we point out that existing methods which try to associate EEG microstates with fMRI patterns make sweeping assumptions when using GLM models by averaging the EEG time series to single values per TR, heavily simplifying the EEG signal.
February 21, 2025 at 9:03 AM
Beyond this, we point out that existing methods which try to associate EEG microstates with fMRI patterns make sweeping assumptions when using GLM models by averaging the EEG time series to single values per TR, heavily simplifying the EEG signal.
We argue that this common criticism can be investigated without throwing away microstate analysis by studying microstates in a continuous space (such as a t-SNE embedding, or similar). Note the information lost in the microstate representation!
February 21, 2025 at 9:02 AM
We argue that this common criticism can be investigated without throwing away microstate analysis by studying microstates in a continuous space (such as a t-SNE embedding, or similar). Note the information lost in the microstate representation!
Ever heard of EEG microstates? Usually defined as cluster centres of topography, the dynamics of MS sequences are referred to as "syntaxes". Our new review discusses syntax methods and show how they could be better associated with the underlying EEG signal: 🧵👇 www.sciencedirect.com/science/arti...
February 21, 2025 at 8:59 AM
Ever heard of EEG microstates? Usually defined as cluster centres of topography, the dynamics of MS sequences are referred to as "syntaxes". Our new review discusses syntax methods and show how they could be better associated with the underlying EEG signal: 🧵👇 www.sciencedirect.com/science/arti...