I\textsuperscript{2}RiMA: Spectral Riemannian Representation with Temporal Attention for Mental Stress Detection based on EEG Signals
This paper proposes I²RiMA, a network that constructs spatial covariance matrices at each frequency point, maps them to the SPD tangent space, and uses frequency cluster aggregation and intra-inter slice attention to improve cross-subject EEG stress detection accuracy, achieving 82.78% balanced accuracy on three datasets with only 1.60M parameters.
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[Submitted on 1 Jul 2026]
Title:I\textsuperscript{2}RiMA: Spectral Riemannian Representation with Temporal Attention for Mental Stress Detection based on EEG Signals
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Abstract:Cross-subject EEG stress detection remains challenging because discriminative stress-related patterns are both subject-dependent and frequency-specific. Conventional Riemannian methods model spatial covariance mainly in the time domain, overlooking neural oscillations that are critical for high-level cognitive state decoding, while standard temporal tokenization often fragments inter-slice temporal coherence. To address these limitations, we propose \method{}, an Intra-Inter Riemannian Manifold Attention Network for EEG-based stress detection. \method{} constructs spatial covariance matrices independently at each frequency point and maps them to the SPD tangent space, preserving channel-wise geometry together with frequency-specific discriminative cues. It further introduces frequency cluster aggregation to select informative spectral components and reduce redundancy by forming compact, data-driven frequency clusters aligned with EEG rhythms. Finally, an intra-inter slice attention module adaptively integrates local slice-level spectral dynamics and global temporal context across EEG sequences. Experiments on three datasets show that \method{} consistently outperforms five state-of-the-art baselines, achieving up to 82.78\% balanced accuracy while remaining efficient with only 1.60M parameters and 31.95M FLOPs.
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2607.01279 [cs.LG]
(or arXiv:2607.01279v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.01279
arXiv-issued DOI via DataCite
Submission history
From: Likun Xia [view email] [v1] Wed, 1 Jul 2026 07:23:32 UTC (12,434 KB)
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