Disambiguation of Cognitive Impairment Diagnosis with EEG-Based Dual-Contrastive Learning

脑电图 计算机科学 人工智能 推论 认知障碍 认知 对偶(语法数字) 监督学习 机器学习 模式识别(心理学) 心理学 神经科学 人工神经网络 文学类 艺术
作者
Zhenxi Song,Zian Pei,Huixia Ren,Lin Zhu,Yi Guo,Zhiguo Zhang
标识
DOI:10.1109/icassp49357.2023.10096583
摘要

The diagnosis of cognitive impairment (CI), here referred to as mild cognitive impairment (MCI) and probable Alzheimer’s disease (AD), is complicated in practice. Early AD diagnosis using electroencephalography (EEG) has attracted attention due to EEG’s advantages in data accessibility. Because of limited, sparse, and ambiguous labels, which are commonly encountered in the EEG-based diagnosis of CI, it is desirable to develop a learning framework to effectively capture CI-related representations beyond fully supervised learning. Therefore, this work explored the possibility of weakly-supervised learning in identifying MCI, AD, and normal aging patterns based on incompletely reliable labels. To address the problem, we proposed a framework containing a dual-contrastive learning structure and a multi-level temporal-spectral EEG encoder, which transformed EEG signals into embeddings and automatically updated the ambiguous labels through intra-subject and cross-subject contrastive learning. We verified the method’s performance based on 54 subjects (18 in each group). Our findings provide new insights into the accurate inference of refractory CI diseases based on non-ideal data sources.
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