A toolkit for EMA annotation & analysis

Prepare EMA recordings with head correction and smoothing, palate trace selection, and kinematics computation before annotation or analysis.

Configure automated annotation to quickly label achievements across subjects in Build Your Own Machine Annotator (BYOMA). Then review and correct outputs by hand in the Hand Annotator.

Analyze your datasets against experimental hypotheses using generalized additive mixed models (GAMMs) or linear regression, directly in R or Python.