基于改进独立成分分析权重矩阵的肌电图和数据手套数据的手指屈伸分类
Classification of finger extension and flexion of EMG and Cyberglove data with modified ICA weight matrix.
作者信息
Naik Ganesh R, Acharyya Amit, Nguyen Hung T
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:3829-32. doi: 10.1109/EMBC.2014.6944458.
This paper reports the classification of finger flexion and extension of surface Electromyography (EMG) and Cyberglove data using the modified Independent Component Analysis (ICA) weight matrix. The finger flexion and extension data are processed through Principal Component Analysis (PCA), and next separated using modified ICA for each individual with customized weight matrix. The extension and flexion features of sEMG and Cyberglove (extracted from modified ICA) were classified using Linear Discriminant Analysis (LDA) with near 90% classification accuracy. The applications of this study include Human Computer Interface (HCI), virtual reality and neural prosthetics.
本文报道了使用改进的独立成分分析（ICA）权重矩阵对表面肌电图（EMG）和数据手套数据的手指屈伸进行分类。手指屈伸数据通过主成分分析（PCA）进行处理，然后使用针对每个个体定制权重矩阵的改进ICA进行分离。使用线性判别分析（LDA）对表面肌电图和数据手套的屈伸特征（从改进的ICA中提取）进行分类，分类准确率接近90%。本研究的应用包括人机交互（HCI）、虚拟现实和神经假体。
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