Applying stochastic spike train theory for high-accuracy MEG/EEG

Niels T. Haumann, Minna Huotilainen, Peter Vuust, Elvira Brattico

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Sammanfattning

The accuracy of electroencephalography (EEG) and magnetoencephalography (MEG) is challenged by overlapping sources from within the brain. This lack of accuracy is a severe limitation to the possibilities and reliability of modern stimulation protocols in basic research and clinical diagnostics. As a solution, we here introduce a theory of stochastic neuronal spike timing probability densities for describing the large-scale spiking activity in neural networks, and a novel spike density component analysis (SCA) method for isolating specific neural sources. Three studies are conducted based on 564 cases of evoked responses to auditory stimuli from 94 human subjects each measured with 60 EEG electrodes and 306 MEG sensors. In the first study we show that the large-scale spike timing (but not non-encephalographic artifacts) in MEG/EEG waveforms can be modeled with Gaussian probability density functions with …
Originalspråkengelska
Artikelnummer532879
TidskriftbioRxiv : the preprint server for biology
Volym2019
Utgåva28.1.2019
Antal sidor39
DOI
StatusPublicerad - 28 jan 2019
MoE-publikationstypEj behörig

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