Summary of Eeg-ssm: Leveraging State-space Model For Dementia Detection, by Xuan-the Tran et al.
EEG-SSM: Leveraging State-Space Model for Dementia Detection
by Xuan-The Tran, Linh Le, Quoc Toan Nguyen, Thomas Do, Chin-Teng Lin
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach to dementia classification using EEG data called EEG-SSM, which combines two primary innovations: temporal and spectral components. The temporal component efficiently processes EEG sequences of varying lengths, while the spectral component integrates frequency-domain information from EEG signals. This synergy allows EEG-SSM to manage complexities in multivariate EEG data, improving accuracy and stability across different temporal resolutions. The model demonstrates a remarkable 91.0 percent accuracy in classifying healthy control, frontotemporal dementia, and Alzheimer’s disease groups, outperforming existing models on the same dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EEG-SSM is a new way to use brain waves (EEG) to diagnose different types of dementia. It combines two ideas: one that helps with long data sequences and another that looks at frequency patterns in the brain. This combination makes it better than other methods for understanding EEG signals and classifying different kinds of dementia. |
Keywords
» Artificial intelligence » Classification
