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Medical diagnostic robot systems have been paid more and more attention dueto 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 areexpensive and the diagnosis results rely on the knowledge of doctors.Therefore, it is necessary to develop a robot diagnostic system to eliminatethe influence of human factors and obtain a higher accuracy rate. In thispaper, we propose a novel Group Feature Domain Adversarial Neural Network(GF-DANN) for amnestic MCI (aMCI) diagnosis, which involves two importantmodules. A Group Feature Extraction (GFE) module is proposed to reduceindividual differences by learning group-level features through adversariallearning. A Dual Branch Domain Adaptation (DBDA) module is carefully designedto reduce the distribution difference between the source and target domain in adomain adaption way. On three types of data set, GF-DANN achieves the bestaccuracy compared with classic machine learning and deep learning methods. Onthe DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and thesensitivity and specificity are 90% and 89%. In addition, by comparing threeEEG data collection paradigms, our results demonstrate that the DMS paradigmhas the potential to build an aMCI diagnose robot system.

Group Feature Learning and Domain Adversarial Neural Network for aMCI Diagnosis System Based on EEG