EVOKE: Efficient and High-Fidelity EEG-to-Video Reconstruction via Decoupling Implicit Neural Representation

Abstract

Visual neural decoding is an important research topic at the intersection of cognitive neuroscience and machine learning. While recent progress has been made in EEG-based neural decoding, reconstructing dynamic visual content remains challenging. In the field of EEG decoding, current models either utilize pre-trained encoders for feature extraction or employ graph neural networks to represent the spatio-temporal information embedding, resulting in poor model representation and high complexity. We propose EVOKE -- an innovative framework for zero-shot decoding of high-fidelity videos from EEG signals. EVOKE employs Implicit Neural Representations to perform complete spatial modeling of EEG and continuously decouples information in the EEG-INR perceptual space. Additionally, we construct a Hierarchical-aware Attention Module (HAM) to decode EEG from three feature anchors: visual, semantic, motion, and progressively control task inference. The Motion Attention Flow (MAF) we developed overcomes the limitations of capturing motion features in dynamic stimuli, creating a more robust representation that enhances reconstruction consistency. Comprehensive experiments prove that SOTA performance of EVOKE (0.353 SSIM, 0.715 CLIP-pcc). We provide an effective method for converting brain activity into rich visual experiences and set a new benchmark for brain multimodal generation.