Boosting Factor-Specific Functional Historical Models for the Detection of Synchronisation in Bioelectrical Signals
This work addresses the challenge of analyzing complex psychophysiological data for researchers in neuroscience and psychology, though it is incremental as it builds on existing functional models.
The authors tackled the problem of understanding the functional relationship between EEG and EMG signals during emotion episodes by applying and extending historical function-on-function regression models to data from 24 participants in a gambling task, achieving improved detection of synchronization through factor-specific and random effects.
The link between different psychophysiological measures during emotion episodes is not well understood. To analyse the functional relationship between electroencephalography (EEG) and facial electromyography (EMG), we apply historical function-on-function regression models to EEG and EMG data that were simultaneously recorded from 24 participants while they were playing a computerised 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 component-wise gradient boosting algorithm, which scales well to large data sets and complex models.