A lightweight one-block transformer architecture for EEG-based cognitive workload classification that uses under 0.5 million parameters and 0.02 GFLOPs.
Efficient emotion-aware iconic gesture prediction for robot co-speech
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
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.