The Deep-Match Framework for Event-Related Potential Detection in EEG
Reliable detection of event-related potentials (ERPs) at the single-trial level remains a major challenge due to the low signal-to-noise ratio EEG recordings. In this work, we investigate whether incorporating prior knowledge about ERP templates into deep learning models can improve detection performance. We employ the Deep-Match framework for ERP detection using multi-channel EEG signals. The model is trained in two stages. First, an encoder-decoder architecture is trained to reconstruct input EEG signals, enabling the network to learn compact signal representations. In the second stage, the decoder is replaced with a detection module, and the network is fine-tuned for ERP identification...
| Authors | Marek Zylinski, Bartosz Tomasz Smigielski, Gerard Cybulski |
|---|---|
| Primary category | eess.SP |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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