Dataset size considerations for robust acoustic and phonetic speech encoding models in EEG.

ABSTRACT: In many experiments that investigate auditory and speech processing in the brain using electroencephalography (EEG), the experimental paradigm is often lengthy and tedious. Typically, the experimenter errs on the side of including more data, more trials, and therefore conducting a longer task to ensure that the data are robust and effects are measurable. Recent studies used naturalistic stimuli to investigate the brain's response to individual or a combination of multiple speech features using system identification techniques, such as multivariate temporal receptive field (mTRF) analyses. The neural data collected from such experiments must be divided into a training set and a test set to fit and validate the mTRF weights. While a good strategy is clearly to collect as much data as is feas

SUBMITTER: Desai M 

PROVIDER: S-EPMC9895838 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature