EEG Decoding and Visual Reconstruction via 3D Geometric with Nonstationarity Modelling

Abstract

Electroencephalogram (EEG) signal processing has advanced in revealing the mechanisms of human visual perception, but existing methods often overlook two key EEG properties: (1) 3D geometric relationships between EEG electrodes, which reflects the ability to model the brain in stereoscopic terms; and (2) nonstationarity of EEG signals, which involves capturing the dynamic changes in the frequency spectrum. To address these limitations, we introduce the GeoCap framework in this paper. Specifically, to effectively model the 3D geometry of EEG electrodes, we propose spherical manifold encoding (SME), which represents EEG channels on a 3D spherical manifold. Additionally, drawing inspiration from the capacity of cumulative fractional derivatives to incorporate historical context, we introduce the discrete multiscale Caputo derivative (DMSCD) to more accurately capture the temporal dynamics of EEG signals across multiple scales. Experiments on EEG decoding and visual reconstruction tasks demonstrate that GeoCap outperforms other state-of-the-art methods.