论文标题
脑电图的fMRI只是深入学习：使用可解释的DL来解开eeg-fmri关系
fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships
论文作者
论文摘要
在依赖依赖意图的脑部计算机界面的情况下，获得潜在的皮层结构的活动为探索情感神经科学领域中广泛的认知现象提供了丰富的选择，包括复杂的决策过程和永恒的自由野外困境，并促进神经学探索范围的诊断。到目前为止，只有使用笨重，昂贵且固定的FMRI设备才有可能。在这里，我们提出了一种可解释的域接地解决方案，可从多通道EEG数据中恢复几个皮层区域的活性，并证明实际皮层下血氧化水平依赖性SBOLD信号与其eeg衍生的双胞胎之间的相关性高达60％。然后，使用新颖的和理论上合理的权重解释方法，我们恢复了皮层下核中血液动力学信号的头皮EEG的个体空间和时频模式。所描述的结果不仅铺平了通往可穿戴皮层运动扫描仪的道路，而且还展示了深度学习技术与可解释的域约束架构和适当的下游任务相结合的自动知识发现过程。
The access to activity of subcortical structures offers unique opportunity for building intention dependent brain-computer interfaces, renders abundant options for exploring a broad range of cognitive phenomena in the realm of affective neuroscience including complex decision making processes and the eternal free-will dilemma and facilitates diagnostics of a range of neurological deceases. So far this was possible only using bulky, expensive and immobile fMRI equipment. Here we present an interpretable domain grounded solution to recover the activity of several subcortical regions from the multichannel EEG data and demonstrate up to 60% correlation between the actual subcortical blood oxygenation level dependent sBOLD signal and its EEG-derived twin. Then, using the novel and theoretically justified weight interpretation methodology we recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei. The described results not only pave the road towards wearable subcortical activity scanners but also showcase an automatic knowledge discovery process facilitated by deep learning technology in combination with an interpretable domain constrained architecture and the appropriate downstream task.
