# Distinguishing Startle from Surprise Events Based on Physiological Signals

arXiv: https://arxiv.org/abs/2509.09799 · HTML: https://mlsanity.com/paper/2509.09799/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 39 references that are in the map.
- First posted on arXiv 11 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [Distinguishing Target and Non-Target Fixations with EEG and Eye Tracking in Realistic Visual Scenes](https://mlsanity.com/paper/2508.01853/) (2025)
- [Implicit Search Intent Recognition using EEG and Eye Tracking: Novel Dataset and Cross-User Prediction](https://mlsanity.com/paper/2508.01860/) (2025)

## Abstract (arXiv)

Unexpected events can impair attention and delay decision-making, posing serious safety risks in high-risk environments such as aviation. In particular, reactions like startle and surprise can impact pilot performance in different ways, yet are often hard to distinguish in practice. Existing research has largely studied these reactions separately, with limited focus on their combined effects or how to differentiate them using physiological data. In this work, we address this gap by distinguishing between startle and surprise events based on physiological signals using machine learning and multi-modal fusion strategies. Our results demonstrate that these events can be reliably predicted, achieving a highest mean accuracy of 85.7% with SVM and Late Fusion. To further validate the robustness of our model, we extended the evaluation to include a baseline condition, successfully differentiating between Startle, Surprise, and Baseline states with a highest mean accuracy of 74.9% with XGBoost and Late Fusion.