I\textsuperscript{2}RiMA: Spectral Riemannian Representation with Temporal Attention for Mental Stress Detection based on EEG Signals

• URL: http://arxiv.org/abs/2607.01279v1
• Date: Wed, 01 Jul 2026 07:23:32 GMT
• Title: I\textsuperscript{2}RiMA: Spectral Riemannian Representation with Temporal Attention for Mental Stress Detection based on EEG Signals
• Abstract summary: Cross-subject EEG stress detection is challenging because discriminative stress-related patterns are both subject-dependent and frequency-specific.<n>We propose method, an Intra-Inter Riemannian Manifold Attention Network for EEG-based stress detection.<n>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.
• Score: 14.944883272011445
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Beyond Random Partitioning: Unsupervised Spatio-Temporal Stratification for Cohort Balancing in Longitudinal Medical Imaging [0.9530026127073782]
  We formalize an unsupervised-temporal cohort-balancing standard operating procedure (SOP) that combines elbow-cluster K-means clustering over a standardized, six-dimensional joint intensity-temporal feature space.<n>On a longitudinal, contrast-enhanced $T1$-izable brain MRI cohort, the protocol reduces the maximum cross-subset intensity bias from 34.1% under conventional random shuffling to under 2.1%.
  arXiv  Detail & Related papers  (2026-07-29T09:33:19Z)
• Sequential RC-TGAN: Generating Relational Time Series with Spectral Envelope Loss [2.6599014990168834]
  We introduce Sequential RC-TGAN (Seq. RC-TGAN), a temporal extension of the RC-TGAN framework, equipped with a novel integrated loss function.<n>Our end-to-end approach significantly outperforms state-of-the-art systems in reproducing cyclic patterns and long-term seasonality.
  arXiv  Detail & Related papers  (2026-06-30T16:09:10Z)
• SRENet: Spectral Re-Entry Network for Point Cloud Action Recognition [49.508770157706486]
  We propose SRENet to learn both global context and fine-grained temporal dynamics of motion from a frequency perspective for action recognition.<n>SRENet achieves state-of-the-art performance, validating the effectiveness of frequency modeling in point cloud-based action understanding.
  arXiv  Detail & Related papers  (2026-06-02T05:11:47Z)
• VACE: Learning Geometrically Structured Representations for Time Series Anomaly Detection [0.788278500923222]
  VACE is a self-supervised anomaly detection method.<n>It represents a compact, directionally coherent region in the embedding space.<n>It significantly outperforms more complex methods trained on substantially larger budgets.
  arXiv  Detail & Related papers  (2026-05-22T11:07:09Z)
• TIE: Time Interval Encoding for Video Generation over Events [50.66585165263848]
  Director-style prompting, robotic action prediction, and interactive video agents demand temporal grounding over concurrent events.<n>Modern video generators represent time as discrete points through point-wise positional encodings.<n>Time Interval TIE is a principled, plug-and-play interval-aware generalization.
  arXiv  Detail & Related papers  (2026-05-11T13:23:14Z)
• Learning to Separate RF Signals Under Uncertainty: Detect-Then-Separate vs. Unified Joint Models [53.79667447811139]
  We show that a single deep neural architecture learns to jointly detect and separate when applied directly to the received signal.<n>These findings highlight UJM as a scalable and practical alternative to DTS, while opening new directions for unified separation under broader estimation.
  arXiv  Detail & Related papers  (2026-02-04T15:25:02Z)
• SKANet: A Cognitive Dual-Stream Framework with Adaptive Modality Fusion for Robust Compound GNSS Interference Classification [47.20483076887704]
  Global Navigation Satellite Systems (GNSS) face growing threats from sophisticated jamming interference.<n>We propose a cognitive deep learning framework built upon a dual-stream architecture that integrates Time-Frequency Images (TFIs) and Power Spectral Density (PSD)<n>We show that SKANet achieves an overall accuracy of 96.99%, exhibiting superior robustness for compound jamming classification.
  arXiv  Detail & Related papers  (2026-01-19T07:42:45Z)
• Hierarchical Self-Supervised Representation Learning for Depression Detection from Speech [51.14752758616364]
  Speech-based depression detection (SDD) is a promising, non-invasive alternative to traditional clinical assessments.<n>We propose HAREN-CTC, a novel architecture that integrates multi-layer SSL features using cross-attention within a multitask learning framework.<n>The model achieves state-of-the-art macro F1-scores of 0.81 on DAIC-WOZ and 0.82 on MODMA, outperforming prior methods across both evaluation scenarios.
  arXiv  Detail & Related papers  (2025-10-05T09:32:12Z)
• Robust Spectral Fuzzy Clustering of Multivariate Time Series with Applications to Electroencephalogram [6.62414474989199]
  We introduce a fuzzy clustering framework in the spectral domain to extract frequency-specific monotonic relationships across variables.<n>Our method takes advantage of dominant frequency-based cross-regional connectivity patterns to improve clustering accuracy.<n>As a flagship application, we analyze electroencephalogram recordings, where our approach uncovers frequency- and connectivity-specific markers of latent cognitive states.
  arXiv  Detail & Related papers  (2025-06-28T12:02:01Z)
• Omni-Dimensional Frequency Learner for General Time Series Analysis [12.473862872616998]
  We present Omni-Dimensional Frequency Learner (ODFL) model based on a in depth analysis among all the three aspects of the spectrum feature. Technically, our method is composed of a semantic-adaptive global filter with attention to the un-salient frequency bands and partial operation among the channel dimension. ODFL achieves consistent state-of-the-art in five mainstream time series analysis tasks, including short- and long-term forecasting, imputation, classification, and anomaly detection.
  arXiv  Detail & Related papers  (2024-07-15T03:48:16Z)
• Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality [39.476378833827184]
  Anomaly detection in dynamic graphs presents a significant challenge due to the temporal evolution of graph structures and attributes. We introduce a novel spatial- temporal memories-enhanced graph autoencoder (STRIPE) STRIPE significantly outperforms existing methods with 5.8% improvement in AUC scores and 4.62X faster in training time.
  arXiv  Detail & Related papers  (2024-03-14T02:26:10Z)
• Spatiotemporal k-means [39.98633724527769]
  We propose a twotemporal clustering method called k-means (STk) that is able to analyze multi-scale clusters. We show how STkM can be extended to more complex machine learning tasks, particularly unsupervised region of interest detection and tracking in videos.
  arXiv  Detail & Related papers  (2022-11-10T04:40:31Z)
• Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy [68.86835407617778]
  Anomaly Transformer achieves state-of-the-art performance on six unsupervised time series anomaly detection benchmarks. Anomaly Transformer achieves state-of-the-art performance on six unsupervised time series anomaly detection benchmarks.
  arXiv  Detail & Related papers  (2021-10-06T10:33:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.