Version 1
: Received: 12 July 2024 / Approved: 15 July 2024 / Online: 17 July 2024 (04:13:49 CEST)
How to cite:
Delgado-Munoz, J. A.; Matsunaka, R.; Hiraki, K. Classification of Known and Unknown Study Items in a Memory Task using Single-Trial Event-Related Potentials and Convolutional Neural Networks. Preprints2024, 2024071239. https://doi.org/10.20944/preprints202407.1239.v1
Delgado-Munoz, J. A.; Matsunaka, R.; Hiraki, K. Classification of Known and Unknown Study Items in a Memory Task using Single-Trial Event-Related Potentials and Convolutional Neural Networks. Preprints 2024, 2024071239. https://doi.org/10.20944/preprints202407.1239.v1
Delgado-Munoz, J. A.; Matsunaka, R.; Hiraki, K. Classification of Known and Unknown Study Items in a Memory Task using Single-Trial Event-Related Potentials and Convolutional Neural Networks. Preprints2024, 2024071239. https://doi.org/10.20944/preprints202407.1239.v1
APA Style
Delgado-Munoz, J. A., Matsunaka, R., & Hiraki, K. (2024). Classification of Known and Unknown Study Items in a Memory Task using Single-Trial Event-Related Potentials and Convolutional Neural Networks. Preprints. https://doi.org/10.20944/preprints202407.1239.v1
Chicago/Turabian Style
Delgado-Munoz, J. A., Reiko Matsunaka and Kazuo Hiraki. 2024 "Classification of Known and Unknown Study Items in a Memory Task using Single-Trial Event-Related Potentials and Convolutional Neural Networks" Preprints. https://doi.org/10.20944/preprints202407.1239.v1
Abstract
This study examines the feasibility of using event-related potentials (ERPs) obtained from electroencephalographic (EEG) recordings as biomarkers for long-term memory item classification. Previous studies have identified old/new effects in memory paradigms associated with explicit long-term memory and familiarity. Recent advancements in convolutional neural networks (CNNs) have enabled the classification of ERP trials under different conditions and identification of features related to neural processes at the single-trial level. We employed this approach to compare three CNN models with distinct architectures using experimental data. Participants (N = 25) performed an association memory task while recording ERPs that were used for training and validation of the CNN models. The EEGNET-based model achieved the most reliable performance in terms of precision, recall, and specificity compared with the shallow and deep convolutional approaches. The classification accuracy of this model reached 70% for known items and 62% for unknown items. Good overall accuracy requires a trade-off between recall and specificity and depends on the architecture of the model and the dataset size. These results suggest the possibility of integrating ERP and CNN into online learning tools and identifying the underlying processes related to long-term memorization.
Keywords
Long term memory; Familiarity; Electroencephalography; Event-Related Potentials; Convolutional Neural Networks
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
