First steps towards quantum machine learning applied to the classification of event-related potentials
Human-Computer Interaction
2023-02-07 v1 Machine Learning
Abstract
Low information transfer rate is a major bottleneck for brain-computer interfaces based on non-invasive electroencephalography (EEG) for clinical applications. This led to the development of more robust and accurate classifiers. In this study, we investigate the performance of quantum-enhanced support vector classifier (QSVC). Training (predicting) balanced accuracy of QSVC was 83.17 (50.25) %. This result shows that the classifier was able to learn from EEG data, but that more research is required to obtain higher predicting accuracy. This could be achieved by a better configuration of the classifier, such as increasing the number of shots.
Cite
@article{arxiv.2302.02648,
title = {First steps towards quantum machine learning applied to the classification of event-related potentials},
author = {Grégoire Cattan and Alexandre Quemy and Anton Andreev},
journal= {arXiv preprint arXiv:2302.02648},
year = {2023}
}
Comments
in French language