GREEN: a lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration (2024)
1 Abstract
Spectral analysis using wavelets has proven useful for analyzing electroencephalographic (EEG) signals and identifying biomarkers in a clinical context. Over the past decade, Riemannian geometry has been used to analyze EEG signals, showing promising results in EEG classification. In this paper, we introduce GREEN (Gabor Riemann EEGNet), a lightweight neural network that combines wavelet transforms and Riemannian geometry for interpretable EEG biomarker exploration.
2 NOTES
Zotero parent/PDF: MXJ24RQX / 59QTXGF3. spdlearn implements this as the Green model.
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