Chen Guodong, Helm Hayden S, Lytvynets Kate, Yang Weiwei, Priebe Carey E
Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, United States.
Microsoft Research, Microsoft, Redmond, WA, United States.
Front Hum Neurosci. 2022 Jul 8;16:930291. doi: 10.3389/fnhum.2022.930291. eCollection 2022.
We consider the problem of extracting features from passive, multi-channel electroencephalogram (EEG) devices for downstream inference tasks related to high-level mental states such as stress and cognitive load. Our proposed feature extraction method uses recently developed spectral-based multi-graph tools and applies them to the time series of graphs implied by the statistical dependence structure (e.g., correlation) amongst the multiple sensors. We study the features in the context of two datasets each consisting of at least 30 participants and recorded using multi-channel EEG systems. We compare the classification performance of a classifier trained on the proposed features to a classifier trained on the traditional band power-based features in three settings and find that the two feature sets offer complementary predictive information. We conclude by showing that the importance of particular channels and pairs of channels for classification when using the proposed features is neuroscientifically valid.
我们考虑从被动式多通道脑电图（EEG）设备中提取特征的问题，以用于与压力和认知负荷等高级心理状态相关的下游推理任务。我们提出的特征提取方法使用了最近开发的基于频谱的多图工具，并将其应用于由多个传感器之间的统计依赖结构（例如相关性）所隐含的图的时间序列。我们在两个数据集的背景下研究这些特征，每个数据集至少由30名参与者组成，并使用多通道EEG系统进行记录。我们在三种设置下将基于所提出特征训练的分类器的分类性能与基于传统带功率特征训练的分类器进行比较，发现这两个特征集提供了互补的预测信息。我们通过表明在使用所提出的特征时特定通道和通道对在分类中的重要性在神经科学上是有效的来得出结论。
