期刊
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
卷 29, 期 -, 页码 368-379出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2021.3051958
关键词
Electroencephalography; Decoding; Feature extraction; Task analysis; Classification algorithms; Deep learning; Data mining; Motor imagery electroencephalograph (EEG) decoding; central distance loss (CD-loss); central vector shift; central vector update; circular translation strategy
资金
- National Natural Science Foundation of China [52075177]
- Joint Fund of the Ministry of Education for Equipment Pre-Research [6141A02033124]
- Research Foundation of Guangdong Province [2019A050505001, 2018KZDXM002]
- Guangzhou Research Foundation [202002030324, 201903010028]
This paper proposes a new EEG decoding method with discriminative feature learning strategy and data augmentation method, and experimental results show that it achieves the highest accuracy and stability on two public datasets.
With the rapid development of deep learning, more and more deep learning-based motor imagery electroencephalograph (EEG) decoding methods have emerged in recent years. However, the existing deep learning-based methods usually only adopt the constraint of classification loss, which hardly obtains the features with high discrimination and limits the improvement of EEG decoding accuracy. In this paper, a discriminative feature learning strategy is proposed to improve the discrimination of features, which includes the central distance loss (CD-loss), the central vector shift strategy, and the central vector update process. First, the CD-loss is proposed to make the same class of samples converge to the corresponding central vector. Then, the central vector shift strategy extends the distance between different classes of samples in the feature space. Finally, the central vector update process is adopted to avoid the non-convergence of CD-loss and weaken the influence of the initial value of central vectors on the final results. In addition, overfitting is another severe challenge for deep learning-based EEG decoding methods. To deal with this problem, a data augmentation method based on circular translation strategy is proposed to expand the experimental datasets without introducing any extra noise or losing any information of the original data. To validate the effectiveness of the proposed method, we conduct some experiments on two public motor imagery EEG datasets (BCI competition IV 2a and 2b dataset), respectively. The comparison with current state-of-the-art methods indicates that our method achieves the highest average accuracy and good stability on the two experimental datasets.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据