Extended Abstract: A Bayesian approach to phases for frequency-tagged encephalography in the cognitive neuroscience of language

Anonymous

Keywords: EEG, Bayesian analysis, grammar, phrase

TL;DR: Bayesian modelling have not been previously applied to phase data from EEG or MEG experiments in neurolinguistics: it works really well.

Abstract: Electroencephalography and magnetoencephalography recordings are non-invasive and temporally precise, making them an invaluable tool in the cognitive neuroscience of language. They are, however, very noisy. One fruitful response to this noisiness has been to use stimuli with a specific frequency and to look for the signal of interest in the response at that frequency. Typically this involves measuring the coherence of response phase. Here a novel Bayesian approach to measuring phase coherence is described and illustrated using an example from neurolinguistics. It gives a better and more data-efficient description than more traditional statistical approaches.