EEG-SSM: Leveraging State-Space Model for Dementia Detection

TL;DR

This paper introduces EEG-SSM, a novel state-space model that combines temporal and spectral analysis of EEG data to improve dementia classification accuracy, achieving 91% accuracy across multiple dementia types.

Contribution

EEG-SSM uniquely integrates spectral features into state-space models for EEG-based dementia detection, enhancing accuracy and robustness over existing methods.

Findings

Achieved 91% accuracy in classifying dementia types.

Outperformed existing models on the same dataset.

Effectively handled multivariate EEG data with improved stability.

Abstract

State-space models (SSMs) have garnered attention for effectively processing long data sequences, reducing the need to segment time series into shorter intervals for model training and inference. Traditionally, SSMs capture only the temporal dynamics of time series data, omitting the equally critical spectral features. This study introduces EEG-SSM, a novel state-space model-based approach for dementia classification using EEG data. Our model features two primary innovations: EEG-SSM temporal and EEG-SSM spectral components. The temporal component is designed to efficiently process EEG sequences of varying lengths, while the spectral component enhances the model by integrating frequency-domain information from EEG signals. The synergy of these components allows EEG-SSM to adeptly manage the complexities of multivariate EEG data, significantly improving accuracy and stability across different temporal resolutions. Demonstrating a remarkable 91.0 percent accuracy in classifying Healthy Control (HC), Frontotemporal Dementia (FTD), and Alzheimer's Disease (AD) groups, EEG-SSM outperforms existing models on the same dataset. The development of EEG-SSM represents an improvement in the use of state-space models for screening dementia, offering more precise and cost-effective tools for clinical neuroscience.