# A Lightweight Transformer for Pain Recognition from Brain Activity

arXiv: https://arxiv.org/abs/2604.16491 · HTML: https://mlsanity.com/paper/2604.16491/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 68 references that are in the map.
- First posted on arXiv 13 Apr 2026.

## Builds on (most cited first, 16 of 16)

- [Transformer Encoder with Multiscale Deep Learning for Pain Classification Using Physiological Signals](https://mlsanity.com/paper/2303.06845/) (2023)
- [Twins-PainViT: Towards a Modality-Agnostic Vision Transformer Framework for Multimodal Automatic Pain Assessment using Facial Videos and fNIRS](https://mlsanity.com/paper/2407.19809/) (2024)
- [Synthetic Thermal and RGB Videos for Automatic Pain Assessment utilizing a Vision-MLP Architecture](https://mlsanity.com/paper/2407.19811/) (2024)
- [A Full Transformer-based Framework for Automatic Pain Estimation using Videos](https://mlsanity.com/paper/2412.15095/) (2024)
- [Efficient Pain Recognition via Respiration Signals: A Single Cross-Attention Transformer Multi-Window Fusion Pipeline](https://mlsanity.com/paper/2507.21886/) (2025)
- [PainFormer: a Vision Foundation Model for Automatic Pain Assessment](https://mlsanity.com/paper/2505.01571/) (2025)
- [Faces of Experimental Pain: Transferability of Deep Learned Heat Pain Features to Electrical Pain](https://mlsanity.com/paper/2406.11808/) (2024)
- [Multi-Representation Diagrams for Pain Recognition: Integrating Various Electrodermal Activity Signals into a Single Image](https://mlsanity.com/paper/2507.21881/) (2025)
- [Tiny-BioMoE: a Lightweight Embedding Model for Biosignal Analysis](https://mlsanity.com/paper/2507.21875/) (2025)
- [Multi-task Neural Networks for Pain Intensity Estimation using Electrocardiogram and Demographic Factors](https://mlsanity.com/paper/2407.19475/) (2024)
- [Transformer with Leveraged Masked Autoencoder for video-based Pain Assessment](https://mlsanity.com/paper/2409.05088/) (2024)
- [A View on Edge caching Applications](https://mlsanity.com/paper/1907.12359/) (2019)
- [Data Augmentation for 3DMM-based Arousal-Valence Prediction for HRI](https://mlsanity.com/paper/2410.00349/) (2024)
- [A Pain Assessment Framework based on multimodal data and Deep Machine Learning methods](https://mlsanity.com/paper/2505.05396/) (2025)
- [When and How to Express Empathy in Human-Robot Interaction Scenarios](https://mlsanity.com/paper/2509.25200/) (2025)
- [Virtual Reflections on a Dynamic 2D Eye Model Improve Spatial Reference Identification](https://mlsanity.com/paper/2412.07344/) (2024)

## Built on by (most cited first, 7 of 7)

- [A Unified Tokenization Framework for Pain Recognition using Heterogeneous 3D Modalities](https://mlsanity.com/paper/2607.19716/) (2026)
- [ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment](https://mlsanity.com/paper/2607.19722/) (2026)
- [Towards a Unified Modality-Agnostic Multimodal Framework for Cognitive Workload Assessment](https://mlsanity.com/paper/2609.20199/) (2026)
- [Beyond the Raw Waveform: Fusing Visual Representations of EDA for Stress Detection](https://mlsanity.com/paper/2609.22095/) (2026)
- [A Multi-Scale Temporal Framework with Dynamic Fusion for EEG-Based Emotion Recognition](https://mlsanity.com/paper/2608.09088/) (2026)
- [One-Block Transformer (1BT) for EEG-Based Cognitive Workload Assessment](https://mlsanity.com/paper/2605.00856/) (2026)
- [MUPA2E: Multimodal Unified Perception with Asymmetric Attention for Emotion Assessment](https://mlsanity.com/paper/2608.15999/) (2026)

## Abstract (arXiv)

Pain is a multifaceted and widespread phenomenon with substantial clinical and societal burden, making reliable automated assessment a critical objective. This paper presents a lightweight transformer architecture that fuses multiple fNIRS representations through a unified tokenization mechanism, enabling joint modeling of complementary signal views without requiring modality-specific adaptations or increasing architectural complexity. The proposed token-mixing strategy preserves spatial, temporal, and time-frequency characteristics by projecting heterogeneous inputs onto a shared latent representation, using a structured segmentation scheme to control the granularity of local aggregation and global interaction. The model is evaluated on the AI4Pain dataset using stacked raw waveform and power spectral density representations of fNIRS inputs. Experimental results demonstrate competitive pain recognition performance while remaining computationally compact, making the approach suitable for real-time inference on both GPU and CPU hardware.