English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Low information transfer rate is a major bottleneck for brain-computerinterfaces based on non-invasive electroencephalography (EEG) for clinicalapplications. This led to the development of more robust and accurateclassifiers. In this study, we investigate the performance of quantum-enhancedsupport vector classifier (QSVC). Training (predicting) balanced accuracy ofQSVC was 83.17 (50.25) %. This result shows that the classifier was able tolearn from EEG data, but that more research is required to obtain higherpredicting accuracy. This could be achieved by a better configuration of theclassifier, such as increasing the number of shots.

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