迈向视觉的神经基础模型：对齐EEG、MEG和fMRI表征以进行解码、编码和模态转换
Towards Neural Foundation Models for Vision: Aligning EEG, MEG, and fMRI Representations for Decoding, Encoding, and Modality Conversion
AI总结：
本文提出利用对比学习对齐EEG、MEG和fMRI多模态神经数据与视觉刺激的基础模型，通过解码、编码和模态转换实验验证了其跨脑成像技术捕获语义信息的能力。
摘要：
本文提出了一种利用对比学习来对齐神经数据和视觉刺激，以创建跨多模态脑活动基础模型的新方法。我们使用了脑电图（EEG）、脑磁图（MEG）和功能磁共振成像数据。通过三个关键实验展示了该框架的能力：从神经数据中解码视觉信息、将图像编码为神经表征，以及在神经模态之间进行转换。结果突显了该模型准确捕获不同脑成像技术间语义信息的能力，展示了其在解码、编码和模态转换任务中的潜力。
英文摘要：
This paper presents a novel approach towards creating a foundational model for aligning neural data and visual stimuli across multimodal representationsof brain activity by leveraging contrastive learning. We used electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) data. Our framework's capabilities are demonstrated through three key experiments: decoding visual information from neural data, encoding images into neural representations, and converting between neural modalities. The results highlight the model's ability to accurately capture semantic information across different brain imaging techniques, illustrating its potential in decoding, encoding, and modality conversion tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
