{"id":"doi:10.3390/s26175444","kind":"article","title":"The Deep-Match Framework for Event-Related Potential Detection in EEG","authors":["Żyliński M","Śmigielski BT","Cybulski G"],"author_count":3,"year":2026,"published":"2026-08-28","venue":"Sensors (Basel, Switzerland)","cited_by":0,"doi":"10.3390/s26175444","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3390/s26175444","archive_url":"https://space-kitty.com/papers/doi:10.3390/s26175444","updated":null,"pmid":"42740064","pmcid":"PMC13567992","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC13567992/pdf","abstract":"Reliable detection of event-related potentials (ERPs) at the single-trial level remains a challenge due to low signal-to-noise ratio and high variability in electroencephalography (EEG) recordings. This work investigates the use of the Deep-Match framework (Deep-MF) for ERP detection. We examine whether incorporating prior knowledge of an ERP template into deep learning models improves detection performance. As a proof-of-concept study, the framework was evaluated on a single dataset with multi-channel EEG recordings during laser stimulation. The model was trained in two stages. First, an encoder-decoder architecture was trained to reconstruct input EEG signals in order to learn compact signal representations. In the second stage, the decoder was replaced with a detection module and the network was fine-tuned for ERP identification. Two model variants were evaluated: a standard model with randomly initialized filters and a Deep-MF model in which input kernels were initialized using ERP templates. Models performance was assessed on a single-trial ERP detection task during leave-one-out validation, and then compared with matched filter detector. The neural network models outperformed the matched filter detector and proposed that the Deep-MF model slightly outperformed the detector with standard kernel initialization for the majority of held-out subjects. Although both approaches exhibited substantial inter-subject variability, Deep-MF achieved a higher average F1-score (0.37) compared to the standard network (0.34), indicating improved robustness to cross-subject differences. Performance varied considerably across participants. The best performance obtained by Deep-MF reached an F1-score of 0.71, exceeding the maximum score achieved by the standard model (0.59). These results showed that ERP-informed kernel initialization provides improvements in single-trial ERP detection under subject-independent evaluation. These findings demonstrate that integrating domain knowledge with deep learning architectures can improve single-trial ERP detection. The proposed approach provides a step towards practical wearable EEG and passive brain-computer interface applications, as well as towards real-time monitoring of cognitive processes.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"The Deep-Match Framework for Event-Related Potential Detection in EEG\" by Żyliński M, Śmigielski BT, Cybulski G, https://doi.org/10.3390/s26175444, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"brain-computer-interfaces","name":"Brain-Computer Interfaces","domain_slug":"neuroscience"}],"cited_in_dossiers":[]}