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 Manifold Attention and Confidence Stratification (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.

使用脑信号进行神经退行性疾病的基于脑电图的智能诊断面临数据异质性和标注不可靠性的挑战。本研究引入了 Manifold Attention and Confidence Stratification (MACS) 的可转移框架，通过来自四个中心的脑电图信号进行神经退行性疾病的诊断，解决了这一问题。实验结果表明，MACS 相较于现有算法，在跨中心和小样本的脑疾病诊断中具有卓越性能。同时，该研究还为扩展 MACS 技术到其他数据分析领域提供了新的思路，解决了多媒体和多模态内容理解中的数据异质性和标注不可靠性的问题。