Shahid Beheshti University Journal of Neurodevelopmental Cognition 2645-565X 1 1 2022 06 21 Autonomous Estimation of Patients’ Neuropsychological State Using Convolutional Neural Networks 82 89 103429 10.52547/jncog.2022.103429 EN Somaye Mohammadyan Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran Keivan Navi Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran Babak Majidi Department of Computer Engineering, Khatam University, Tehran, Iran Journal Article 2022 05 10 The number of patients with neuropsychological problems is increasing rapidly in the world. Autonomous methods are replacing the traditional diagnosis methods in detection and classification of many mental and neurological problems. Machine learning algorithms and especially deep neural networks are able to diagnose various neurological and psychological complications automatically. In this paper, a machine learning based framework is used for autonomous estimation of patients’ neuropsychological state. The proposed framework can automatically diagnose neuropsychological state of the patients and present a personalized solution for their problems. A convolutional neural networks is used for automatic profiling of patients and to classify their mental state according to their EEG signals. The proposed framework can be used to help patients to have better life experience. https://jncog.sbu.ac.ir/article_103429_8a7f3325cfeac84ddd117ac84ef0c124.pdf