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Title

Classification of Known and Unknown Study Items in a Memory Task Using Single-Trial Event-Related Potentials and Convolutional Neural Networks.

Authors

Delgado-Munoz, Jorge; Matsunaka, Reiko; Hiraki, Kazuo

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 the 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 62% for known items and 66% 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.

Subjects

LONG-term memory; CONVOLUTIONAL neural networks; EXPLICIT memory; EVOKED potentials (Electrophysiology); ONLINE education; MEMORIZATION

Publication

Brain Sciences (2076-3425), 2024, Vol 14, Issue 9, p860

ISSN

2076-3425

Publication type

Academic Journal

DOI

10.3390/brainsci14090860

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