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

arXiv: https://arxiv.org/abs/2407.17801 · HTML: https://mlsanity.com/paper/2407.17801/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 25 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation](https://mlsanity.com/paper/2401.04722/) (2024)
- [Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining](https://mlsanity.com/paper/2402.03302/) (2024)

## Built on by (most cited first, 11 of 11)

- [CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model](https://mlsanity.com/paper/2506.09110/) (2025)
- [Quantizing Small-Scale State-Space Models for Edge AI](https://mlsanity.com/paper/2506.12480/) (2025)
- [ECG-RAMBA: Zero-Shot ECG Generalization by Morphology-Rhythm Disentanglement and Long-Range Modeling](https://mlsanity.com/paper/2512.23347/) (2025)
- [Cross-Subject Intracranial EEG Reconstruction from Scalp Recordings Using Multi-Scale Cross-Attention Transformers](https://mlsanity.com/paper/2605.18897/) (2026)
- [Can ChatGPT Diagnose Alzheimer's Disease?](https://mlsanity.com/paper/2502.06907/) (2025)
- [Cortical-SSM: A Deep State Space Model for Motor Imagery Decoding from EEG Signals](https://mlsanity.com/paper/2510.15371/) (2025)
- [EEG-Titans: Long-Horizon Seizure Forecasting via Dual-Branch Attention and Neural Memory](https://mlsanity.com/paper/2601.13748/) (2026)
- [Temporal Context and Architecture: A Benchmark for Naturalistic EEG Decoding](https://mlsanity.com/paper/2601.21215/) (2026)
- [Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals](https://mlsanity.com/paper/2605.18483/) (2026)
- [A Simple State Space Model Excels at Multivariate Time Series Classification](https://mlsanity.com/paper/2605.27406/) (2026)
- [EEG-to-Report: An Annotation and Feature-Text Framework for Training Language Models on Clinical EEG](https://mlsanity.com/paper/2608.26153/) (2026)

## Abstract (arXiv)

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.