Motor Imagery EEG Classification Using Capsule Networks
Motor Imagery EEG Classification Using Capsule Networks
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DOI：
10.3390/s19132854
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发表时间：
2019-07-01
期刊：
影响因子：
3.9
通讯作者：
Jeong, Jin-Woo
中科院分区：
文献类型：
--
作者：
Ha, Kwon-Woo;Jeong, Jin-Woo
最近提出了各种基于卷积神经网络（CNN）的方法来提高基于运动想象的脑机接口（BCI）的性能。然而，当目标数据失真时，CNN的分类精度会受到影响。特别是对于运动想象脑电图（EEG），即使来自同一个人，所测量的信号也不一致，并且可能显著失真。为了克服这些限制，我们建议应用胶囊网络（CapsNet）来学习EEG信号的各种属性，从而实现比以前的CNN方法更好，更鲁棒的性能。建议CapsNet为基础的框架分类的两类运动想象，即右手和左手的动作。运动想象EEG信号首先使用短时傅立叶变换（STFT）算法转换为2D图像，然后用于训练和测试胶囊网络。在BCI竞争IV 2b数据集上评估了所提出的框架的性能。所提出的框架优于最先进的基于CNN的方法和各种传统的机器学习方法。实验结果证明了该方法对运动想象脑电信号分类的可行性。
Various convolutional neural network (CNN)-based approaches have been recently proposed to improve the performance of motor imagery based-brain-computer interfaces (BCIs). However, the classification accuracy of CNNs is compromised when target data are distorted. Specifically for motor imagery electroencephalogram (EEG), the measured signals, even from the same person, are not consistent and can be significantly distorted. To overcome these limitations, we propose to apply a capsule network (CapsNet) for learning various properties of EEG signals, thereby achieving better and more robust performance than previous CNN methods. The proposed CapsNet-based framework classifies the two-class motor imagery, namely right-hand and left-hand movements. The motor imagery EEG signals are first transformed into 2D images using the short-time Fourier transform (STFT) algorithm and then used for training and testing the capsule network. The performance of the proposed framework was evaluated on the BCI competition IV 2b dataset. The proposed framework outperformed state-of-the-art CNN-based methods and various conventional machine learning approaches. The experimental results demonstrate the feasibility of the proposed approach for classification of motor imagery EEG signals.