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

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

Research output: Contribution to journalArticleScientific

Abstract

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 …
Original languageEnglish
Article number532879
JournalbioRxiv : the preprint server for biology
Volume2019
Issue number28.1.2019
Number of pages39
DOIs
Publication statusPublished - 28 Jan 2019
MoE publication typeNot Eligible

Fields of Science

  • 515 Psychology

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