mlsanityData to 7 Oct 2026

Mental State Classification Using Multi-graph Features

arXiv 2203.00516 ↗First posted 25 Feb 2022eess.SP cs.LG stat.MEcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 12 of its 47 references that are in the map. First posted on arXiv 25 Feb 2022.

Builds on

12 of 47 references.

References · 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 1; 2026: 1.

7 papers.

  1. 2023Comparing Foundation Models using Data Kernelscited by 14
  2. 2024Tracking the perspectives of interacting language modelscited by 14
  3. 2024Consistent estimation of generative model representations in the data kernel perspective spacecited by 10
  4. 2024Statistical inference on black-box generative models in the data kernel perspective spacecited by 9
  5. 2025Detecting Perspective Shifts in Multi-agent Systemscited by 4
  6. 2023Approximately optimal domain adaptation with Fisher's Linear Discriminantcited by 2
  7. 2026Black-box model classification under the discriminative factorizationcited by 1