# One-Block Transformer (1BT) for EEG-Based Cognitive Workload Assessment

arXiv: https://arxiv.org/abs/2605.00856 · HTML: https://mlsanity.com/paper/2605.00856/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 50 references that are in the map.
- First posted on arXiv 21 Apr 2026.

## Builds on (most cited first, 20 of 20)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [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)
- [Multi-Representation Diagrams for Pain Recognition: Integrating Various Electrodermal Activity Signals into a Single Image](https://mlsanity.com/paper/2507.21881/) (2025)
- [CLARE: Cognitive Load Assessment in REaltime with Multimodal Data](https://mlsanity.com/paper/2404.17098/) (2024)
- [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)
- [A Lightweight Transformer for Pain Recognition from Brain Activity](https://mlsanity.com/paper/2604.16491/) (2026)
- [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)
- [Efficient Emotion-Aware Iconic Gesture Prediction for Robot Co-Speech](https://mlsanity.com/paper/2604.11417/) (2026)
- [Measuring Cognitive Workload Using Multimodal Sensors](https://mlsanity.com/paper/2205.04235/) (2022)
- [Neural Networks Meet Neural Activity: Utilizing EEG for Mental Workload Estimation](https://mlsanity.com/paper/2408.13930/) (2024)
- [Enhancing Cognitive Workload Classification Using Integrated LSTM Layers and CNNs for fNIRS Data Analysis](https://mlsanity.com/paper/2407.15901/) (2024)
- [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, 1 of 1)

- [Towards a Unified Modality-Agnostic Multimodal Framework for Cognitive Workload Assessment](https://mlsanity.com/paper/2609.20199/) (2026)

## Abstract (arXiv)

Accurate and continuous estimation of cognitive workload is fundamental to creating adaptive human-machine systems. However, designing architectures that balance representational capacity with computational efficiency has been challenging for practical deployment. This paper introduces 1BT, a One-Block Transformer for compact and efficient EEG-based cognitive workload assessment. The model aggregates multi-channel temporal sequences via a minimal latent bottleneck, using a single cross-attention module followed by lightweight self-attention. A controlled study involving 11 participants performing three cognitively diverse tasks (abstract reasoning, numerical problem-solving, and an interactive video game) was conducted with continuous EEG recordings across two workload levels. Systematic architectural analysis identifies the most compact configuration that preserves high performance, while substantially lowering computational cost. The final model achieves high workload classification performance with under 0.5 million parameters and 0.02 GFLOPs, paving the way for a design direction for real-time cognitive workload monitoring in resource-constrained settings.