The Deep-Match Framework for Event-Related Potential Detection in EEG

Marek Żyliński, Bartosz Śmigielski, Gerard Cybulski

arXiv · PDF · Open in the Atlas

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• Marower/DeepMF_ERP_Detection_in_EEG-main

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Abstract

Objective Reliable detection of event-related potentials (ERPs) at the single-trial level remains a major challenge due to low signal-to-noise ratio and high variability in electroencephalography (EEG) recordings. TThis work investigates the use of the Deep-Match framework for ERP detection. We examine whether incorporating prior knowledge about ERP template into deep learning models improves detection performance. Approach We employed the Deep-Match framework for ERP detection using multi-channel EEG recordings. 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 single-trial ERP detection task during leave-one-out validation. Main results The proposed 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, with both models showing the lowest performance for one subject (F1-score = 0.01). 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 demonstrate that ERP-informed kernel initialization provides consistent improvements in single-trial ERP detection under subject-independent evaluation. Significance These findings demonstrate that integrating domain knowledge with deep learning architectures can substantially improve single-trial ERP detection. The proposed approach provides a step toward practical wearable EEG and passive brain-computer interface applications, enabling real-time monitoring of cognitive processes.

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