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

Brain-computer interface (BCI) is a communication system between humans andcomputers reflecting human intention without using a physical control device.Since deep learning is robust in extracting features from data, research ondecoding electroencephalograms by applying deep learning has progressed in theBCI domain. However, the application of deep learning in the BCI domain hasissues with a lack of data and overconfidence. To solve these issues, weproposed a novel data augmentation method, CropCat. CropCat consists of twoversions, CropCat-spatial and CropCat-temporal. We designed our method byconcatenating the cropped data after cropping the data, which have differentlabels in spatial and temporal axes. In addition, we adjusted the label basedon the ratio of cropped length. As a result, the generated data from ourproposed method assisted in revising the ambiguous decision boundary intoapparent caused by a lack of data. Due to the effectiveness of the proposedmethod, the performance of the four EEG signal decoding models is improved intwo motor imagery public datasets compared to when the proposed method is notapplied. Hence, we demonstrate that generated data by CropCat smooths thefeature distribution of EEG signals when training the model.

CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals