  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.
