A lightweight one-block transformer architecture for EEG-based cognitive workload classification that uses under 0.5 million parameters and 0.02 GFLOPs.
Pain assessment using multi-kernel-fcn-lstm and haemoglobin difference in fnirs
2 Pith papers cite this work. Polarity classification is still indexing.
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Lightweight transformer fuses raw and spectral fNIRS representations via unified tokenization for competitive pain recognition on the AI4Pain dataset while remaining computationally compact.
citing papers explorer
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One-Block Transformer (1BT) for EEG-Based Cognitive Workload Assessment
A lightweight one-block transformer architecture for EEG-based cognitive workload classification that uses under 0.5 million parameters and 0.02 GFLOPs.
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A Lightweight Transformer for Pain Recognition from Brain Activity
Lightweight transformer fuses raw and spectral fNIRS representations via unified tokenization for competitive pain recognition on the AI4Pain dataset while remaining computationally compact.