The prospect of future treatment warrants the development of cost-effective screening for Alzheimer's disease (AD). A promising candidate in this regard is electroencephalography (EEG), as it is one of the most economic imaging modalities. Recent efforts in EEG analysis have shifted towards leveraging spatial information, employing novel frameworks such as graph signal processing or graph neural networks. Here, we systematically investigate the importance of spatial information relative to spectral or temporal information by varying the proportion of each dimension for AD classification. To do so, we test various dimension resolution configurations on two routine EEG datasets. We find that spatial information is consistently more relevant than temporal information and equally relevant as spectral information. These results emphasise the necessity to consider spatial information for EEG-based AD classification. On our second dataset, we further find that well-balanced feature resolutions boost classification accuracy by up to 1.6%. Our resolution-based feature extraction has the potential to improve AD classification specifically, and multivariate signal classification generally.

通过改变每个维度的比例来测试不同维度分辨率配置对阿尔茨海默病（AD）分类的重要性，我们发现空间信息与频谱信息同样重要，并且始终比时间信息相关，这强调了在基于脑电图（EEG）的AD分类中考虑空间信息的必要性。

基于脑电图的阿尔茨海默病分类的光谱、时域和空间信息平衡