[2604.04313] Convolutional Neural Network and Adversarial Autoencoder in EEG images classification

Computer Science > Machine Learning arXiv:2604.04313 (cs) [Submitted on 5 Apr 2026] Title:Convolutional Neural Network and Adversarial Autoencoder in EEG images classification Authors:Albert Nasybullin, Semen Kurkin View a PDF of the paper titled Convolutional Neural Network and Adversarial Autoencoder in EEG images classification, by Albert Nasybullin and 1 other authors View PDF HTML (experimental) Abstract:In this paper, we consider applying computer vision algorithms for the classification problem one faces in neuroscience during EEG data analysis. Our approach is to apply a combination of computer vision and neural network methods to solve human brain activity classification problems during hand movement. We pre-processed raw EEG signals and generated 2D EEG topograms. Later, we developed supervised and semi-supervised neural networks to classify different motor cortex activities. Comments: Subjects: Machine Learning (cs.LG) MSC classes: 68T07, 92C55 ACM classes: I.2.6; I.4.7; J.3 Cite as: arXiv:2604.04313 [cs.LG]   (or arXiv:2604.04313v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2604.04313 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journal reference: Proc. 5th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR), 2021 Related DOI: https://doi.org/10.1109/DCNA53427.2021.9586891 Focus to learn more DOI(s) linking to related resources Submission history From: Albert Nasyb...