A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines
A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines
复制标题
基于受限玻尔兹曼机的运动意象分类深度学习方案
DOI：
10.1109/tnsre.2016.2601240
复制
发表时间：
2017-06-01
影响因子：
4.9
通讯作者：
Miao, Hongyu
中科院分区：
文献类型：
--
作者：
Lu, Na;Li, Tengfei;Miao, Hongyu
运动意象分类是脑机接口（BCI）研究中的一个重要课题，它能够识别被试的意图，例如实现假肢控制。运动图像的脑动力学通常是用脑电图（EEG）作为非平稳的低信噪比时间序列来测量的。虽然之前已经开发了多种方法来学习脑电信号特征，但很少探索深度学习思想来生成新的脑电信号特征表示并进一步提高运动图像分类的性能。本文提出了一种基于受限玻尔兹曼机（RBM）的深度学习方案。具体来说，通过快速傅里叶变换（FFT）和小波包分解（WPD）得到的脑电信号的频域表示来训练三个rbm。然后将这些rbm与一个额外的输出层叠加起来，形成一个四层神经网络，称为频率深度信念网络（FDBN）。输出层采用softmax回归来完成分类任务。此外，还采用共轭梯度法和反向传播法对FDBN进行了微调。在公共基准数据集上进行了广泛而系统的实验，结果表明，FDBN比其他选定的最先进方法的性能改进具有统计显著性。此外，本文还介绍了BCI社区可能非常感兴趣的几个发现。
Motor imagery classification is an important topic in brain-computer interface (BCI) research that enables the recognition of a subject's intension to, e.g., implement prosthesis control. The brain dynamics of motor imagery are usually measured by electroencephalography (EEG) as nonstationary time series of low signal-to-noise ratio. Although a variety of methods have been previously developed to learn EEG signal features, the deep learning idea has rarely been explored to generate new representation of EEG features and achieve further performance improvement for motor imagery classification. In this study, a novel deep learning scheme based on restricted Boltzmann machine (RBM) is proposed. Specifically, frequency domain representations of EEG signals obtained via fast Fourier transform(FFT) and wavelet package decomposition (WPD) are obtained to train three RBMs. These RBMs are then stacked up with an extra output layer to form a four-layer neural network, which is named the frequential deep belief network (FDBN). The output layer employs the softmax regression to accomplish the classification task. Also, the conjugate gradient method and backpropagation are used to fine tune the FDBN. Extensive and systematic experiments have been performed on public benchmark datasets, and the results show that the performance improvement of FDBN over other selected state-of-the-art methods is statistically significant. Also, several findings that may be of significant interest to the BCI community are presented in this article.