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.
翻译:脑计算机界面(BCI)是人类与计算机之间的通信系统,在不使用物理控制装置的情况下反映人类的意图。由于深层次的学习在从数据中提取特征方面十分有力,因此在BCI领域应用深层学习解码电子脑图的研究取得了进展。然而,在BCI领域应用深层学习存在缺乏数据和过度自信的问题。为了解决这些问题,我们提议了一种新的数据增强方法,即CropCat。CropCat由两种版本组成:CropCat空间和CropCat时空两个版本。我们设计了方法,在对数据进行裁剪裁后对作物数据进行聚合,在空间轴和时轴上有不同的标签。此外,我们根据作物长度的比例调整了标签。结果,我们拟议方法产生的数据有助于将模糊的决定界限修改为因缺乏数据而明显的结果。由于拟议方法的有效性,4个EEG信号解码模型在2个汽车图像公共数据集中的性能比在不采用拟议方法时得到改进。因此,我们用EGSmalt 的分布特征显示EG 。