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

TL;DR

CropCat is a novel data augmentation technique for EEG signals that improves deep learning model performance by smoothing feature distributions and addressing data scarcity and overconfidence issues in BCI applications.

Contribution

The paper introduces CropCat, a new data augmentation method for EEG signals that enhances model accuracy by concatenating cropped data with adjusted labels across spatial and temporal axes.

Findings

Improved decoding accuracy on two public EEG datasets.

Enhanced feature distribution smoothing in EEG signal training.

Effective in reducing overconfidence and data scarcity issues.

Abstract

Brain-computer interface (BCI) is a communication system between humans and computers reflecting human intention without using a physical control device. Since deep learning is robust in extracting features from data, research on decoding electroencephalograms by applying deep learning has progressed in the BCI domain. However, the application of deep learning in the BCI domain has issues with a lack of data and overconfidence. To solve these issues, we proposed a novel data augmentation method, CropCat. CropCat consists of two versions, CropCat-spatial and CropCat-temporal. We designed our method by concatenating the cropped data after cropping the data, which have different labels in spatial and temporal axes. In addition, we adjusted the label based on the ratio of cropped length. As a result, the generated data from our proposed method assisted in revising the ambiguous decision boundary into apparent caused by a lack of data. Due to the effectiveness of the proposed method, the performance of the four EEG signal decoding models is improved in two motor imagery public datasets compared to when the proposed method is not applied. Hence, we demonstrate that generated data by CropCat smooths the feature distribution of EEG signals when training the model.