She Qingshan, Gan Haitao, Ma Yuliang, Luo Zhizeng, Potter Tom, Zhang Yingchun
Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
Department of Biomedical Engineering, University of Houston, Houston, TX 77204, USA.
Neural Plast. 2016;2016:7431012. doi: 10.1155/2016/7431012. Epub 2016 Nov 3.
Motor imagery electroencephalography (EEG) has been successfully used in locomotor rehabilitation programs. While the noise-assisted multivariate empirical mode decomposition (NA-MEMD) algorithm has been utilized to extract task-specific frequency bands from all channels in the same scale as the intrinsic mode functions (IMFs), identifying and extracting the specific IMFs that contain significant information remain difficult. In this paper, a novel method has been developed to identify the information-bearing components in a low-dimensional subspace without prior knowledge. Our method trains a Gaussian mixture model (GMM) of the composite data, which is comprised of the IMFs from both the original signal and noise, by employing kernel spectral regression to reduce the dimension of the composite data. The informative IMFs are then discriminated using a GMM clustering algorithm, the common spatial pattern (CSP) approach is exploited to extract the task-related features from the reconstructed signals, and a support vector machine (SVM) is applied to the extracted features to recognize the classes of EEG signals during different motor imagery tasks. The effectiveness of the proposed method has been verified by both computer simulations and motor imagery EEG datasets.
运动想象脑电图（EEG）已成功应用于运动康复计划。虽然噪声辅助多变量经验模式分解（NA-MEMD）算法已被用于从所有通道中提取与任务特定的频段，且其尺度与本征模函数（IMF）相同，但识别和提取包含重要信息的特定IMF仍然困难。本文开发了一种新方法，无需先验知识即可在低维子空间中识别携带信息的成分。我们的方法通过使用核谱回归降低复合数据的维度，来训练由原始信号和噪声的IMF组成的复合数据的高斯混合模型（GMM）。然后使用GMM聚类算法区分信息丰富的IMF，利用共同空间模式（CSP）方法从重建信号中提取与任务相关的特征，并将支持向量机（SVM）应用于提取的特征，以识别不同运动想象任务期间的脑电信号类别。计算机模拟和运动想象脑电数据集均验证了该方法的有效性。
