Correlation Assisted Strong Uncorrelating Transform Complex Common Spatial Patterns for Spatially Distant Channel Data

Kim, Youngjoo; You, Jiwoo; Lee, Hee Jun; Lee, Seung Min; Park, Cheolsoo

doi:10.1155/2018/4281230

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…Note that the BCI competition IV-2a and BCI competition III-3a datasets include up to four classes. Only two studies were excluded in this step because they did not use any of the mentioned datasets, but only the PhysioNet EEG dataset [114,115]. Per each dataset, a box-plot was built by relying on the respectively reported accuracies (mean across subjects).…”

Section: Promising Trendsmentioning

confidence: 99%

“…Then, since most studies tested their proposals on multiple datasets, 87 studies out of 89 were considered in this second step because they exploited at least one of the mentioned datasets. Instead, the remaining two studies could not be taken into consideration because they only tested their proposal on the PhysioNet EEG dataset [114,115]. Given the small number of proposals for that dataset, it was hence not possible to make a statistical analysis.…”

Section: Achievementsmentioning

confidence: 99%

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How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art

et al. 2022

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Objective. Processing strategies are analysed with respect to the classification of electroencephalographic signals related to brain-computer interfaces based on motor imagery. A review of literature is carried out to understand the achievements in motor imagery classification, the most promising trends, and the challenges in replicating these results. Main focus is placed on performance by means of a rigorous metrological analysis carried out in compliance with the international vocabulary of metrology. Hence, classification accuracy and its uncertainty are considered, as well as repeatability and reproducibility. Approach. The paper works included in the review concern the classification of electroencephalographic signals in motor-imagery- based brain-computer interfaces. Article search was carried out in accordance with the PRISMA standard and 89 studies were included. Main results. Statistically-based analyses show that brain-inspired approaches are increasingly proposed, and that these are particularly successful in discriminating against multiple classes. Notably, many proposals involve convolutional neural networks. Instead, classical machine learning approaches are still effective for binary classifications. Many proposals combine common spatial pattern, least absolute shrinkage and selection operator, and support vector machines. Regarding reported classification accuracies, performance above the upper quartile is in the 85 % to 100 % range for the binary case and in the 83 % to 93 % range for multi-class one. Associated uncertainties are up to 6 % while repeatability for a predetermined dataset is up to 8 %. Reproducibility assessment was instead prevented by lack of standardization in experiments. Significance. By relying on the analysed studies, the reader is guided towards the development of a successful processing strategy as a crucial part of a brain-computer interface. Moreover, it is suggested that future studies should extend these approaches on data from more subjects and with custom experiments, even by investigating online operation. This would also enable the quantification of results reproducibility.

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Section: Promising Trendsmentioning

confidence: 99%

Section: Achievementsmentioning

confidence: 99%