UMind: A unified multitask network for zero-shot M/EEG visual decoding

Decoding visual information from time-resolved brain recordings, such as EEG and MEG, plays a pivotal role in real-time brain-computer interfaces. However, existing approaches primarily focus on direct brain-image feature alignment and are limited to single-task frameworks or task-specific models. In this paper, we propose a Unified MultItask Network for zero-shot M/EEG visual Decoding (referred to UMind), including visual stimulus retrieval, classification, and reconstruction within a shared representation space. Our method learns robust neural-visual and semantic representations through multimodal alignment with both image and text modalities. The integration of both coarse and fine-grained texts enhances the extraction of these neural representations, enabling more detailed semantic and visual decoding. These representations then serve as dual conditional inputs to a pre-trained diffusion model, guiding visual reconstruction from both visual and semantic perspectives. Extensive evaluations on MEG and EEG datasets demonstrate the effectiveness, robustness, and biological plausibility of our approach in capturing spatiotemporal neural dynamics. Our approach sets a multitask pipeline for brain visual decoding, highlighting the synergy of semantic information in visual feature extraction. The code is available at https://github.com/xuchengjian632/UMind.