# Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals

arXiv: https://arxiv.org/abs/1808.06774 · HTML: https://mlsanity.com/paper/1808.06774/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2018.

## Built on by (most cited first, 2 of 2)

- [Beyond One-Size-Fits-All: A Survey of Personalized Affective Computing in Human-Agent Interaction](https://mlsanity.com/paper/2304.00377/) (2023)
- [Improving Personalisation in Valence and Arousal Prediction using Data Augmentation](https://mlsanity.com/paper/2404.09042/) (2024)

## Abstract (arXiv)

Currently there is no validated objective measure of pain. Recent neuroimaging studies have explored the feasibility of using functional near-infrared spectroscopy (fNIRS) to measure alterations in brain function in evoked and ongoing pain. In this study, we applied multi-task machine learning methods to derive a practical algorithm for pain detection derived from fNIRS signals in healthy volunteers exposed to a painful stimulus. Especially, we employed multi-task multiple kernel learning to account for the inter-subject variability in pain response. Our results support the use of fNIRS and machine learning techniques in developing objective pain detection, and also highlight the importance of adopting personalized analysis in the process.