Canine EEG helps human: cross-species and cross-modality epileptic seizure detection via multi-space alignment
Peer-Reviewed Publication
Updates every hour. Last Updated: 5-May-2025 11:09 ET (5-May-2025 15:09 GMT/UTC)
A recent paper published in National Science Review presents a multi-space alignment approach for cross-species and cross-modality electroencephalogram (EEG) based epileptic seizure detection. By employing deep learning techniques, including domain adaptation and knowledge distillation, it aligns cross-species and cross-modality EEG signals to enhance the detection capability beyond traditional within-species and within-modality models. Experiments on multiple scalp and intracranial EEG datasets of humans and canines demonstrated substantial improvements in the detection accuracy, achieving over 90% AUC scores for cross-species and cross-modality seizure detection with extremely limited labeled data from the target species/modality. This is the first study that demonstrates the effectiveness of integrating heterogeneous data from different species and modalities to improve EEG-based seizure detection performance.
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