用脑磁图解码想象中的音乐与诗歌，实现从想象到听觉感知的神经映射。
A Convolutional Framework for Mapping Imagined Auditory MEG into Listened Brain Responses
- 构建卷积神经网络，融合个体校准层实现跨被试稳定映射。
- 在多数被试上预测相关性显著高于零模型，验证方法有效性。
- 为想象语音/音乐的脑机接口提供可扩展的神经解码基础。
解码想象中的语言涉及复杂的神经过程，因时间不确定性及想象反应数据集稀缺而难以分析。本研究采集了受训音乐家在想象和聆听音乐与诗歌刺激时的脑磁图（MEG）数据。结果显示，想象与感知状态下的脑活动均包含一致且条件特异的信息。采用滑动窗口岭回归模型在单被试层面首次实现想象到听觉响应的映射，但跨被试泛化能力有限。在群体层面，我们设计了一个带有被试特异性校准层的编码-解码卷积神经网络，实现了稳定且可泛化的映射。该模型显著优于零模型，在几乎所有保留被试上都产生了更高的预测与真实听觉响应相关性。结果表明，想象神经活动可有效转化为类感知响应，为未来涉及想象语音与音乐的脑机接口应用奠定基础。
原文摘要 · Abstract (English)
Decoding imagined speech engages complex neural processes that are difficult to interpret due to uncertainty in timing and the limited availability of imagined-response datasets. In this study, we present a Magnetoencephalography (MEG) dataset collected from trained musicians as they imagined and listened to musical and poetic stimuli. We show that both imagined and perceived brain responses contain consistent, condition-specific information. Using a sliding-window ridge regression model, we first mapped imagined responses to listened responses at the single-subject level, but found limited generalization across subjects. At the group level, we developed an encoder-decoder convolutional neural network with a subject-specific calibration layer that produced stable and generalizable mappings. The CNN consistently outperformed the null model, yielding significantly higher correlations between predicted and true listened responses for nearly all held-out subjects. Our findings demonstrate that imagined neural activity can be transformed into perception-like responses, providing a foundation for future brain-computer interface applications involving imagined speech and music.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。