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首页> 外文期刊>Journal of Neuroscience Methods >EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features.
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EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features.

机译：使用神经模糊预测和小波分形特征的基于EEG的运动图像分类。

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摘要

In this paper, a feature extraction method through the time-series prediction based on the adaptive neuro-fuzzy inference system (ANFIS) is proposed for brain-computer interface (BCI) applications. The ANFIS time-series prediction together with multiresolution fractal feature vectors (MFFVs) is applied for feature extraction in motor imagery (MI) classification. The features are extracted from the electroencephalography (EEG) signals recorded from subjects performing left and right MI. Two ANFISs are trained to perform time-series predictions for respective left and right MI data. Features obtained from the difference of MFFVs between the predicted and actual signals are then calculated through a window of EEG signals. Finally, a simple linear classifier, namely linear discriminant analysis (LDA), is used for classification. The proposed method is estimated with classification accuracy and the area under the receiver operating characteristics curve (AUC) on six subjects from two data sets. I also assess the performance of proposed method by comparing it with well-known linear adaptive autoregressive (AAR) model, AAR time-series prediction, and neural network (NN) time-series prediction. The results indicate that ANFIS time-series prediction together with MFFV features is a promising method in MI classification.
机译：提出了一种基于自适应神经模糊推理系统（ANFIS）的时间序列预测特征提取方法，用于脑机接口（BCI）应用。 ANFIS时间序列预测与多分辨率分形特征向量（MFFV）一起用于运动图像（MI）分类中的特征提取。从从执行左右MI的受试者记录的脑电图（EEG）信号中提取特征。训练了两个ANFIS，以分别对左右MI数据执行时间序列预测。然后，通过EEG信号窗口计算从预测信号与实际信号之间的MFFV差异获得的特征。最后，使用简单的线性分类器，即线性判别分析（LDA）进行分类。利用分类精度和两个数据集对六个对象的接收器工作特性曲线（AUC）下的面积进行了估计。通过与著名的线性自适应自回归（AAR）模型，AAR时间序列预测和神经网络（NN）时间序列预测进行比较，我还评估了该方法的性能。结果表明，将ANFIS时间序列预测与MFFV特征一起在MI分类中是一种很有前途的方法。

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