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Updated: May 14, 2026

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SS-RIME: A Scale-Stabilized Approach to EEG Cognitive Workload Classification.

Kais Khaldi1, Afrah Alanazi2, Inam Alanazi2

  • 1Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
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This summary is machine-generated.

A new EEG analysis method, SS-RIME, accurately decodes cognitive workload by stabilizing frequency and weighting brain signals. This robust, explainable framework is ideal for real-time human-machine interaction and neuroergonomics.

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biomedical Engineering

Background:

  • Accurate cognitive workload assessment from EEG is crucial for neuroergonomics and human-machine interaction.
  • Existing methods like EMD and CEEMDAN have limitations including instability and amplitude sensitivity.
  • There is a need for a physiologically grounded and interpretable EEG feature extraction framework.

Purpose of the Study:

  • To introduce Scale-Stabilized Relative Intrinsic Mode Energy (SS-RIME), a novel EEG feature extraction framework.
  • To address limitations of existing methods by integrating frequency stabilization and spectral weighting.
  • To provide a robust, explainable, and computationally efficient solution for cognitive workload decoding.

Main Methods:

  • Developed SS-RIME integrating instantaneous frequency stabilization, delta/theta spectral weighting, and cross-IMF energy normalization.
Keywords:
CEEMDANEEGSS-RIMEcognitive workloadintrinsic mode functionstime–frequency analysis

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  • Applied SS-RIME to 64-channel EEG data recorded during N-back tasks.
  • Compared SS-RIME performance against classical machine learning and deep learning models (EEGNet, DeepConvNet, ShallowConvNet).

    • Main Results:

    • SS-RIME achieved high accuracies: 99.12% (0 vs. 2-back), 97.84% (0 vs. 3-back), 92.31% (2 vs. 3-back).
    • Demonstrated strong cross-subject generalization.
    • Identified theta-dominant IMFs in frontal midline regions as key discriminative components.
    • Inference time was below 20 ms per epoch, indicating computational efficiency.

    Conclusions:

    • SS-RIME offers a theoretically motivated and physiologically informed approach for EEG-based cognitive workload decoding.
    • The framework is robust, explainable, and suitable for real-time applications.
    • SS-RIME outperforms existing EMD/CEEMDAN and deep learning methods, addressing key methodological gaps.