Spectral Priors vs. Attention: Investigating the Utility of Attention Mechanisms in EEG-Based Diagnosis

2026-05-14

AI Summary

This study addresses the challenge of classifying neurodegenerative diseases from electroencephalography (EEG) signals, which is hindered by their inherent high noise levels and low spatial resolution, causing existing deep learning models to struggle in distinguishing between healthy and affected individuals or among different disease types. To overcome this, the authors propose a spectral prior–based feature construction approach that transforms raw EEG data into highly discriminative frequency- and time-frequency–domain representations, substantially enhancing class separability. Experiments on three resting-state and one task-based public EEG datasets demonstrate that this method enables conventional machine learning models to achieve performance comparable to or even surpassing state-of-the-art deep learning approaches. Furthermore, the work reveals a fundamental limitation of attention mechanisms in stably capturing neural activity patterns, showing negligible improvement even when combined with frequency-selected inputs.

Abstract

Electroencephalograph (EEG) timeseries signals are characterized by significant noise and coarse spatial resolution, which complicates the classification of neurodegenerative diseases. Even SOTA deep learning architectures struggle to distinguish between healthy controls and diseased subjects, or between different disease types, due to high intergroup similarity. In this paper, we show that a spectrally selective approach to feature construction enhances class separability. By isolating signal strengths within the primary brainwave bands, we transform high dimensional raw data into high value spectral features. Our results demonstrate that a) features derived from frequency and time frequency domain allow traditional machine learning models to match or exceed the performance of SOTA deep learning models, b) Attention mechanism is unable to distill the stable feature signatures that characterize healthy neural activity in both resting and task EEGs, and c) the limitations of attention based models in finding relevant spectral features appear to be fundamental in that providing frequency selective time domain input do not appreciably improve their performance. We validate our methodology across three open source resting EEG datasets and one task EEG dataset, providing robust empirical evidence for our claims.

Problem

• EEG-based diagnosis
• neurodegenerative diseases
• class separability
• spectral features
• attention mechanisms

Innovation

• spectral priors
• attention mechanism
• EEG-based diagnosis
• time-frequency features
• class separability