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