基于微分熵及卷积神经网络的脑电运动想象分类识别
Motor imagery EEG classification and recognition based on differential entropy and convolutional neural network
廉小亲 1蔡沫豪 1高超 1罗志宏 1吴叶兰1
作者信息
- 1. 北京工商大学人工智能学院,北京 100048;北京工商大学中国轻工业工业互联网与大数据重点实验室,北京 100048
- 折叠
摘要
针对基于运动想象的脑电信号多分类识别准确率不高的问题,提出一种基于微分熵及卷积神经网络对运动想象四分类的识别方法.首先,将脑电信号通过滤波器提取为Alpha、Beta、Theta、Gamma 4个频段,分别计算各个频段的微分熵特征,并按照脑电极空间特征对数据结构进行重构为三维脑电信号特征立方体.最后,将其输入卷积神经网络进行四分类,该方法基于BCI Competition IV-2a公开数据集,准确率达到95.88%,并在试验室建立四分类运动想象数据集进行相同的处理,准确率达到94.50%.测试结果表明本文所提方法具有更好的识别效果.
Abstract
To address the problem of low accuracy in multi-classification recognition of motor imagery electroencephalogram(EEG)signals,a recognition method is proposed based on differential entropy and convolutional neural network for 4-class classification of motor imagery.EEG signals are extracted into 4 frequency bands(Alpha,Beta,Theta,and Gamma)through the filter,followed by the computation of differential entropy for each frequency band.According to the spatial characteristics of brain electrodes,the data structure is reconstructed into three-dimensional EEG signal feature cube which is input into convolutional neural network for 4-class classification.The method achieves an accuracy of 95.88%on the BCI Competition IV-2a public dataset.Additionally,a 4-class classification motor imagery dataset is established in the laboratory for the same processing,and an accuracy of 94.50%is obtained.The test results demonstrate that the proposed method exhibits superior recognition performance.
关键词
运动想象脑电信号/卷积神经网络/微分熵/特征提取Key words
motor imagery EEG signal/convolutional neural network/differential entropy/feature extraction引用本文复制引用
出版年
2024
NETL
NSTL
万方数据