No universal EEG decoding pipeline exists: covariance tangent-space projection and CSP rank best on average across three datasets, but per-subject winners vary — and the claimed 340,000+ unique configurations is really the count of subject-level evaluations.
First steps towards quantum machine learning applied to the classification of event-related potentials
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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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Rethinking Generalized BCIs: Benchmarking 340,000+ Unique Algorithmic Configurations for EEG Mental Command Decoding
No universal EEG decoding pipeline exists: covariance tangent-space projection and CSP rank best on average across three datasets, but per-subject winners vary — and the claimed 340,000+ unique configurations is really the count of subject-level evaluations.