First steps towards quantum machine learning applied to the classification of event-related potentials

2023/02/06 by Grégoire Cattan, Cattan, Grégoire, Alexandre Quemy +3 · 1 citation
Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (stat.ML) #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2302.02648

openalex publication_date 2023/02/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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