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

arXiv: https://arxiv.org/abs/1609.06070 · HTML: https://mlsanity.com/paper/1609.06070/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2016.

## Built on by (most cited first, 5 of 5)

- [Functional additive models on manifolds of planar shapes and forms](https://mlsanity.com/paper/2109.02624/) (2021)
- [Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization](https://mlsanity.com/paper/2110.03513/) (2021)
- [Automatic Componentwise Boosting: An Interpretable AutoML System](https://mlsanity.com/paper/2109.05583/) (2021)
- [Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models](https://mlsanity.com/paper/2210.07723/) (2022)
- [A Functional Extension of Semi-Structured Networks](https://mlsanity.com/paper/2410.05430/) (2024)

## Abstract (arXiv)

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.