Department of Information Management, National Chung Cheng University, Taiwan.
Clin EEG Neurosci. 2013 Oct;44(4):257-64. doi: 10.1177/1550059413477090. Epub 2013 Mar 26.
In this study, grey-based Hopfield neural network (GHNN), is proposed for the unsupervised analysis of motor imagery (MI) electroencephalogram (EEG) data. Combined with segment selection and feature extraction, GHNN is used for the recognition of left and right MI data. A Gaussian-like filter is proposed to reduce noise, to further enhance performance of active segment selection. Features are extracted by coherence from wavelet data, and then discriminated by GHNN, which is an unsupervised approach suitable for the online classification of nonstationary biomedical signals. Compared to EEG data without segment selection, several usual features, and classifiers, the proposed system is potentially an analytic approach in brain-computer interface (BCI) applications.
在这项研究中，提出了基于灰色的 Hopfield 神经网络（GHNN），用于对运动想象（MI）脑电图（EEG）数据进行无监督分析。GHNN 结合了分段选择和特征提取，用于识别左右 MI 数据。提出了一种类高斯滤波器来减少噪声，以进一步提高主动分段选择的性能。特征是从小波数据的相干性中提取出来的，然后由 GHNN 进行区分，GHNN 是一种适合非平稳生物医学信号在线分类的无监督方法。与没有分段选择、常用特征和分类器的 EEG 数据相比，所提出的系统在脑机接口（BCI）应用中是一种潜在的分析方法。
