1BT: One-Block Transformer for EEG-Based Cognitive Workload Assessment

2026-04-21

AI Summary

This work addresses the challenge of achieving both high accuracy and computational efficiency in cognitive workload assessment under resource-constrained conditions. The authors propose an extremely lightweight single-block Transformer architecture (1BT), which, for the first time, applies a single Transformer block to multi-channel EEG time-series modeling. By incorporating a latent bottleneck to compress input signals and integrating lightweight self-attention and cross-attention mechanisms, the model enables efficient discriminative learning. Requiring only 0.5 million parameters and 0.02 GFLOPs, the proposed method attains competitive cognitive workload classification performance while drastically reducing model size and computational overhead, making it well-suited for real-time, low-power deployment scenarios.

Abstract

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.

Problem

• cognitive workload
• EEG
• computational efficiency
• real-time monitoring
• resource-constrained

Innovation

• One-Block Transformer
• EEG-based cognitive workload
• lightweight attention
• computational efficiency
• real-time monitoring