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Boosting factor‐specific functional historical models for the detection of synchronization in bioelectrical signals
Author(s) -
Rügamer David,
Brockhaus Sarah,
Gentsch Kornelia,
Scherer Klaus,
Greven Sonja
Publication year - 2018
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12241
Subject(s) - computer science , electroencephalography , boosting (machine learning) , artificial intelligence , machine learning , univariate , pattern recognition (psychology) , psychology , multivariate statistics , neuroscience
Summary The link between different psychophysiological measures during emotion episodes is not well understood. To analyse the functional relationship between electroencephalography and facial electromyography, we apply historical function‐on‐function regression models to electroencephalography and electromyography data that were simultaneously recorded from 24 participants while they were playing a computerized gambling task. Given the complexity of the data structure for this application, we extend simple functional historical models to models including random historical effects, factor‐specific historical effects and factor‐specific random historical effects. Estimation is conducted by a componentwise gradient boosting algorithm, which scales well to large data sets and complex models.