DISAMBIGUATION OF COGNITIVE IMPAIRMENT DIAGNOSIS WITH EEG-BASED DUAL-CONTRASTIVE LEARNING

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.