  Medical diagnostic robot systems have been paid more and more attention due
to its objectivity and accuracy. The diagnosis of mild cognitive impairment
(MCI) is considered an effective means to prevent Alzheimer's disease (AD).
Doctors diagnose MCI based on various clinical examinations, which are
expensive and the diagnosis results rely on the knowledge of doctors.
Therefore, it is necessary to develop a robot diagnostic system to eliminate
the influence of human factors and obtain a higher accuracy rate. In this
paper, we propose a novel Group Feature Domain Adversarial Neural Network
(GF-DANN) for amnestic MCI (aMCI) diagnosis, which involves two important
modules. A Group Feature Extraction (GFE) module is proposed to reduce
individual differences by learning group-level features through adversarial
learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed
to reduce the distribution difference between the source and target domain in a
domain adaption way. On three types of data set, GF-DANN achieves the best
accuracy compared with classic machine learning and deep learning methods. On
the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the
sensitivity and specificity are 90% and 89%. In addition, by comparing three
EEG data collection paradigms, our results demonstrate that the DMS paradigm
has the potential to build an aMCI diagnose robot system.
