Mental State Classification Using Multi-graph Features
This work addresses mental state classification for neuroscience applications, but it is incremental as it builds on existing multi-graph tools.
The paper tackled the problem of extracting features from multi-channel EEG data for classifying mental states like stress and cognitive load, finding that multi-graph features complement traditional band power-based features in classification tasks.
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 method leverages recently developed 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 compare the effectiveness of the proposed features to traditional band power-based features in the context of three classification experiments 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.