Boosting Factor-Specific Functional Historical Models for the Detection of Synchronisation in Bioelectrical Signals

TL;DR

This paper introduces advanced factor-specific functional historical models with gradient boosting for analyzing complex EEG and EMG data during emotion episodes, revealing intricate psychophysiological relationships.

Contribution

It extends functional historical models to include random and factor-specific effects, enabling scalable analysis of complex bioelectrical data.

Findings

Effective modeling of EEG and EMG relationships during emotion episodes

Scalable estimation method using gradient boosting

Enhanced understanding of psychophysiological interactions

Abstract

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.