@inproceedings{
song2024eegmacs,
title={{EEG}-{MACS}: Manifold Attention and Confidence Stratification for {EEG}-based Cross-Center Brain Disease Diagnosis under Unreliable Annotations},
author={Zhenxi Song and Ruihan Qin and Huixia Ren and Zhen Liang and Yi Guo and Min zhang and Zhiguo Zhang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=i7vrwAblCz}
}
Cross-center data heterogeneity and annotation unreliability significantly challenge the intelligent diagnosis of diseases using brain signals. A notable example is the EEG-based diagnosis of neurodegenerative diseases, which features subtler abnormal neural dynamics typically observed in small-group settings. To advance this area, in this work, we introduce a transferable framework employing **M**anifold **A**ttention and **C**onfidence **S**tratification (**MACS**) to diagnose neurodegenerative disorders based on EEG signals sourced from four centers with unreliable annotations. The MACS framework’s effectiveness stems from these features: 1) The _**Augmentor**_ generates various EEG-represented brain variants to enrich the data space; 2) The _**Switcher**_ enhances the feature space for trusted samples and reduces overfitting on incorrectly labeled samples; 3) The _**Encoder**_ uses the Riemannian manifold and Euclidean metrics to capture spatiotemporal variations and dynamic synchronization in EEG; 4) The _**Projector**_, equipped with dual heads, monitors consistency across multiple brain variants and ensures diagnostic accuracy; 5) The _**Stratifier**_ adaptively stratifies learned samples by confidence levels throughout the training process; 6) Forward and backpropagation in **MACS** are constrained by confidence stratification to stabilize the learning system amid unreliable annotations. Our subject-independent experiments, conducted on both neurocognitive and movement disorders using cross-center corpora, have demonstrated superior performance compared to existing related algorithms. This work not only improves EEG-based diagnostics for cross-center and small-setting brain diseases but also offers insights into extending MACS techniques to other data analyses, tackling data heterogeneity and annotation unreliability in multimedia and multimodal content understanding. We have released our code here: https://anonymous.4open.science/r/EEG-Disease-MACS-0B4A.
EEG-MACS: Manifold Attention and Confidence Stratification for EEG-based Cross-Center Brain Disease Diagnosis under Unreliable Annotations
Zhenxi Song Ruihan Qin Huixia Ren Zhen Liang Yi Guo Min zhang Zhiguo Zhang