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Fitgrid: A Python package for multi-channel event-related time series regression modeling


ABSTRACT: Summary Electrical brain activity related to external stimulation and internal mental events can be measured at the scalp as tiny time-varying electric potential waveforms (electroencephalogram; EEG), typically a few tens of microvolts peak to peak (Berger, 1930). Even tinier brain responses, too small to be seen by naked eye in the EEG, can be detected by repeating the stimulation, aligning the EEG recordings to the triggering event and averaging them at each time point (Dawson, 1951, 1954). Under assumptions that the brain response (signal) is the same in each recording and the ongoing background EEG (noise) varies randomly, averaging improves the estimate of the “true” brain response at each time point as the random variation cancels. The average event-related brain potential (ERP) and its counterpart for event-related magnetic fields (ERFs) are cornerstones of experimental brain research in human sensation, perception, and cognition (Luck & Kappenman, 2013). Smith and Kutas pointed out that the average ERP at each time t is mathematically identical to the estimated constant

SUBMITTER: Urbach T 

PROVIDER: S-EPMC9615366 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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