The Subspace Regularization Method Improves ErrP Detection by EEGNET in BCI Experiments

Andrea Farabbi, Luca Mainardi

2023

Abstract

In this study, the subspace regularization method was applied on the Electroencephalographic (EEG) signal recorded during stimulation of the Error Potential (ErrP) in order to improve the detection of the latter. The ErrP is stimulated through the presentation of an erroneous event to the subject. The recorded signals were processed with the subspace regularization method to remove the background EEG not related to the erroneous event. Then, the ErrP and Non–ErrP epochs (both raw and processed with the proposed method) were classified using EEGNET, a Convolutional Neural Network considered golden standard for EEG classification. The results show that elaborating the signals with the proposed method highlight the typical characteristics of the ErrP epochs both in temporal and frequency domain. Moreover, the classification metrics evaluated, always increase if compared to not processed signal (i.e. maximum increase in accuracy, balanced accuracy and F1-score are of 7.7%, 10.1% and 11% respectively). These findings suggest that the subspace regularization method can improve the performance of ErrP-based Brain Computer Interfaces (BCI) and can be used also in real time application and for asynchronous classification of erroneous events.

Paper Citation

in Harvard Style

Farabbi A. and Mainardi L. (2023). The Subspace Regularization Method Improves ErrP Detection by EEGNET in BCI Experiments. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS; ISBN 978-989-758-631-6, SciTePress, pages 260-264. DOI: 10.5220/0011682400003414

in Bibtex Style

@conference{biosignals23,
author={Andrea Farabbi and Luca Mainardi},
title={The Subspace Regularization Method Improves ErrP Detection by EEGNET in BCI Experiments},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS},
year={2023},
pages={260-264},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011682400003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS
TI - The Subspace Regularization Method Improves ErrP Detection by EEGNET in BCI Experiments
SN - 978-989-758-631-6
AU - Farabbi A.
AU - Mainardi L.
PY - 2023
SP - 260
EP - 264
DO - 10.5220/0011682400003414
PB - SciTePress