Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment

TL;DR

This study introduces a multi-space alignment framework using deep learning to improve epileptic seizure detection across species and EEG modalities, achieving over 90% AUC with limited labeled data.

Contribution

It is the first to demonstrate effective integration of heterogeneous cross-species and cross-modality EEG data for seizure detection enhancement.

Findings

Achieved over 90% AUC in cross-species and cross-modality seizure detection.

Utilized domain adaptation and knowledge distillation to align EEG signals.

Significantly improved detection accuracy with limited labeled data.

Abstract

Epilepsy significantly impacts global health, affecting about 65 million people worldwide, along with various animal species. The diagnostic processes of epilepsy are often hindered by the transient and unpredictable nature of seizures. Here we propose a multi-space alignment approach based on cross-species and cross-modality electroencephalogram (EEG) data to enhance the detection capabilities and understanding of epileptic seizures. By employing deep learning techniques, including domain adaptation and knowledge distillation, our framework aligns cross-species and cross-modality EEG signals to enhance the detection capability beyond traditional within-species and with-modality models. Experiments on multiple surface 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. To our knowledge, 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. The approach may also be generalizable to different brain-computer interface paradigms, and suggests the possibility to combine data from different species/modalities to increase the amount of training data for large EEG models.