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.

翻译：低信息传输速率是基于非侵入性脑电图（EEG）的脑机接口在临床应用中面临的主要瓶颈，这促使研究者开发更鲁棒、更精确的分类器。本研究探讨了量子增强支持向量分类器（QSVC）的性能表现。QSVC的训练（预测）均衡准确率分别为83.17%（50.25%）。该结果表明分类器能够从EEG数据中学习，但需进一步研究以提高预测准确率。通过优化分类器配置（如增加量子测量次数），有望实现这一目标。