{"ID":5675566,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-04T06:25:51.571775532Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01279","arxiv_id":"2607.01279","title":"I\\textsuperscript{2}RiMA: Spectral Riemannian Representation with Temporal Attention for Mental Stress Detection based on EEG Signals","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.","short_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 de...","url_abs":"https://arxiv.org/abs/2607.01279","url_pdf":"https://arxiv.org/pdf/2607.01279v1","authors":"[\"Cheng He\",\"Kunyu Peng\",\"Shangen Han\",\"Jinming Ma\",\"Jinhong Ding\",\"Likun Xia\"]","published":"2026-07-01T07:23:32Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}