将脑电数据转换到脑空间，实现更精准的非侵入式神经解码。
Non-invasive Neural Decoding in Source Reconstructed Brain Space
- 通过源重建将脑电传感器信号映射到脑区体素空间。
- 在跨数据集任务中实现零样本泛化，准确率提升显著。
- 适合需要跨模态融合与可解释性分析的研究者使用。
非侵入式脑波解码通常以脑磁图/脑电图（MEG/EEG）传感器测量值为输入，导致数据集组合与模型构建困难，因不同设备使用不同的扫描仪和传感器阵列，且其空间结构不直观。相比之下，功能磁共振成像（fMRI）直接在脑空间采集，以体素网格形式呈现，具有结构化的输入表示。通过利用已有技术对传感器源进行重建，可将MEG数据也映射至体素空间进行解码。实验表明，该方法支持空间归纳偏置、空间数据增强、更好的可解释性、跨数据集零样本泛化以及数据调和，显著提升了模型性能与迁移能力。
原文摘要 · Abstract (English)
Non-invasive brainwave decoding is usually done using Magneto/Electroencephalography (MEG/EEG) sensor measurements as inputs. This makes combining datasets and building models with inductive biases difficult as most datasets use different scanners and the sensor arrays have a nonintuitive spatial structure. In contrast, fMRI scans are acquired directly in brain space, a voxel grid with a typical structured input representation. By using established techniques to reconstruct the sensors' sources' neural activity it is possible to decode from voxels for MEG data as well. We show that this enables spatial inductive biases, spatial data augmentations, better interpretability, zero-shot generalisation between datasets, and data harmonisation.
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