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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.16491 |
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| _version_ | 1866913143145365504 |
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| author | Gkikas, Stefanos Cruz, Christian Arzate Fang, Yu Cao, Lu Khan, Muhammad Umar Kassiotis, Thomas Giannakakis, Giorgos Rojas, Raul Fernandez Gomez, Randy |
| author_facet | Gkikas, Stefanos Cruz, Christian Arzate Fang, Yu Cao, Lu Khan, Muhammad Umar Kassiotis, Thomas Giannakakis, Giorgos Rojas, Raul Fernandez Gomez, Randy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_16491 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Lightweight Transformer for Pain Recognition from Brain Activity Gkikas, Stefanos Cruz, Christian Arzate Fang, Yu Cao, Lu Khan, Muhammad Umar Kassiotis, Thomas Giannakakis, Giorgos Rojas, Raul Fernandez Gomez, Randy Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | A Lightweight Transformer for Pain Recognition from Brain Activity |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2604.16491 |