<ul><li>Researchers propose a multi-space alignment approach for cross-species and cross-modality epileptic seizure detection.</li><li>The approach employs deep learning techniques, including domain adaptation and knowledge distillation.</li><li>Experiments on human and canine EEG datasets show significant improvements in detection accuracy.</li><li>The study highlights the effectiveness of integrating data from different species and modalities for EEG-based seizure detection.</li></ul>

Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment

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