SpikeEEG-3D: Spiking Neural Networks for 3D Visual Decoding from EEG

EEG-based 3D visual decoding is an important yet highly challenging task that aims to recover three-dimensional visual content from neural activity. Spiking neural networks (SNNs), with their brain-inspired and energy-efficient computation, have been increasingly studied for EEG decoding, yet their potential for EEG-to-3D visual decoding remains unexplored. In this work, we present SpikeEEG-3D, to the best of our knowledge, the first SNN framework for EEG-based 3D visual decoding, spanning the complete pipeline from EEG representation learning to colored point-cloud reconstruction. For EEG representation learning, SpikeEEG-3D employs Learnable Dynamics Leaky Integrate-and-Fire (LD-LIF) neurons, whose input gain and membrane time constant are jointly optimized to provide adaptive and diverse neuronal dynamics. We further propose Event-Enhanced Cross-Attention (EECA), which exploits spike-state transitions to facilitate information exchange between the static and dynamic EEG streams. For 3D reconstruction, we propose SpikeBit, which leverages bit-weighted virtual timesteps to provide finer-grained numerical representations while preserving spike-driven computation. Experiments on EEG-3D demonstrate excellent EEG classification performance and effective 3D reconstruction, with 20.8$\times$ and 3.1$\times$ lower energy consumption than Neuro-3D, respectively. These results demonstrate the feasibility of extending SNNs to EEG-based 3D visual decoding.