[2604.04316] How Long short-term memory artificial neural network, synthetic data, and fine-tuning improve the classification of raw EEG data

Computer Science > Machine Learning arXiv:2604.04316 (cs) [Submitted on 5 Apr 2026] Title:How Long short-term memory artificial neural network, synthetic data, and fine-tuning improve the classification of raw EEG data Authors:Albert Nasybullin, Vladimir Maksimenko, Semen Kurkin View a PDF of the paper titled How Long short-term memory artificial neural network, synthetic data, and fine-tuning improve the classification of raw EEG data, by Albert Nasybullin and 2 other authors View PDF HTML (experimental) Abstract:In this paper, we discuss a Machine Learning pipeline for the classification of EEG data. We propose a combination of synthetic data generation, long short-term memory artificial neural network (LSTM), and fine-tuning to solve classification problems for experiments with implicit visual stimuli, such as the Necker cube with different levels of ambiguity. The developed approach increased the quality of the classification model of raw EEG data. Comments: Subjects: Machine Learning (cs.LG) MSC classes: 68T07, 92C55 ACM classes: I.2.6; J.3 Cite as: arXiv:2604.04316 [cs.LG]   (or arXiv:2604.04316v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2604.04316 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journal reference: 2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA) Related DOI: https://doi.org/10.1109/DCNA56428.2022.9923206 Focus to learn more DOI(s) linking to related resources Submission h...