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ScaleSpecter: a frequency-aware multi-scale patch framework for robust physiological classification under
Zhouyang Xu1, Hongwei Li1, Wenchao Liu1
1Faculty of Computing, Harbin Institute of Technology, Harbin, China.
Frontiers in Human Neuroscience
|August 11, 2026
Summary
A new framework, ScaleSpecter, enhances electroencephalogram (EEG) analysis for early detection of neurodegenerative diseases. It improves classification accuracy by integrating frequency information with multiscale temporal data, aiding in disease progression monitoring.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Early detection of cognitive impairment in neurodegenerative diseases is crucial for slowing progression and enhancing quality of life.
- Electroencephalography (EEG) offers high temporal resolution for neural oscillation analysis, showing promise for early disease identification.
- Extracting robust features from EEG is challenging due to weak pathological signals obscured by non-stationary background rhythms.
Purpose of the Study:
- To introduce ScaleSpecter, a novel frequency-aware multiscale patch framework for classifying EEG signals related to neurodegenerative diseases.
- To address the challenge of extracting subtle pathological abnormalities from complex EEG data.
- To improve the accuracy and robustness of EEG-based classification for conditions like Alzheimer's and Parkinson's disease.
Main Methods:
- ScaleSpecter employs multiscale temporal representations to capture both local abnormalities and long-term variations.
- A cross-scale attention mechanism facilitates interaction between fine-grained temporal data and broader scale summaries.
- An amplitude-phase-aware spectral modulation module recalibrates spectral responses using learnable complex-valued weights for frequency-domain guidance.
Main Results:
- ScaleSpecter demonstrated competitive performance across three public EEG datasets (ADFTD, APAVA, TDBRAIN) covering Alzheimer's disease, frontotemporal dementia, and Parkinson's disease.
- Ablation studies and visualizations confirmed the effectiveness of multiscale temporal modeling, cross-scale interaction, and spectral modulation.
- The framework achieved favorable results on key evaluation metrics for neurodegenerative disease classification.
Conclusions:
- Integrating frequency-domain guidance with multiscale temporal representations enhances EEG classification robustness under non-stationary conditions.
- ScaleSpecter offers a potentially generalizable framework for analyzing physiological signals in the context of neurodegenerative diseases.
- The findings support the use of advanced signal processing techniques for improved early diagnosis and management of cognitive impairment.
