论文标题
基于神经网络的特征提取用于多级运动图像分类
Neural Network-Based Feature Extraction for Multi-Class Motor Imagery Classification
论文作者
论文摘要
电脑脑电图（EEG）的运动成像（MI）是大脑计算机界面（BCI）系统的重要组成部分，该系统可以通过外部设备通过外部设备与外部世界互动。开发基于EEG的BCI的主要问题是由于脑电图数据的非平稳特征而引起的信息混乱。在这项工作中，首次提出了将脑电图信号转换为无监督神经网络的体重向量的创新思想，该思想首次提出了解决该问题的自动编码器。单独的自动编码器接受了单个脑电图数据的培训。然后，针对各个脑电图信号优化了权重矢量。因此，EEG信号以单个自动编码器的权重向量形式的新域表示。然后，重量向量用于提取自回归系数（ARS），香农熵（SE）和小波领导者等特征。实现了基于窗口的功能提取技术来捕获脑电图数据的本地功能。最后，使用分类器网络对提取的功能进行分类。在两个公共访问的脑电图数据集（BCI竞争-III和竞争-IV）上测试了所提出的方法，以确保其成功并优于先前发表的方法。提出的技术的数据集-IIIA的平均准确性为95.33％，对于基于四级EEG的MI分类，BCI-IIA的数据集IIA的平均准确性为97％。实验结果表明，提出的方法是提高BCI性能的一种有希望的方法。
Decoding of motor imagery (MI) from Electroencephalogram (EEG) is an important component of the Brain-Computer Interface (BCI) system that helps motor-disabled people interact with the outside world via external devices. The main issue in developing the EEG based BCI is the informative confusion due to the non-stationary characteristics of EEG data. In this work, an innovative idea of transforming an EEG signal into the weight vector of an unsupervised neural network called the autoencoder is proposed for the first time to solve that problem. Separate autoencoders are trained for the individual EEG data. The weight vectors are then optimized for the individual EEG signals. The EEG signals are thus represented in a new domain that is in the form of weight vectors of the individual autoencoder. The weight vectors are then used to extract features such as autoregressive coefficients (ARs), Shannon entropy (SE), and wavelet leader. A window-based feature extraction technique is implemented to capture the local features of the EEG data. Finally, extracted features are classified using a classifier network. The proposed approach is tested on two publicly accessible EEG datasets (BCI competition-III and Competition-IV) to ensure that it is as successful as and superior to the previously published methods. The proposed technique achieves a mean accuracy of 95.33 % for dataset-IIIa from BCI-III and a mean accuracy of 97% for dataset-IIa from BCI-IV for four-class EEG-based MI classification. The experimental outcomes show that the proposed approach is a promising way to increase BCI performance.
