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Novel joint algorithm based on EEG in complex scenarios.
Computer Assisted Surgery ( IF 2.1 ) Pub Date : 2019-08-10 , DOI: 10.1080/24699322.2019.1649078
Dongwei Chen 1 , Weiqi Yang 1 , Rui Miao 2 , Lan Huang 1 , Liu Zhang 1 , Chunjian Deng 1 , Na Han 3
Affiliation  

At present, in the field of electroencephalogram (EEG) signal recognition, the classification and recognition in complex scenarios with more categories of EEG signals have gained more attention. Based on the joint fast Fourier transform (FFT) and support vector machine (SVM) methods, this study proposed a novel EEG signal-processing joint method for the complex scenarios with 10 classifications of EEG signals. Moreover, a comprehensive efficiency formula was put forward. The formula considered the accuracy and time consumption of the joint method. This new joint method could improve the accuracy and comprehensive efficiency of multiclass EEG signal recognition. The new joint approach used standardization for data preprocessing. Feature extraction was performed by combining FFT and principal component analysis methods. EEG signals were classified using the weighted k-nearest nenighbour method. In this study, experiments were conducted using public datasets of brainwave 0-9 digits classification. The result demonstrated that the accuracy and comprehensive efficiency of the novel joint method were 84% and 87%, respectively, which were better than those of the existing methods. The precision rate, recall rate, and F1 score of the novel joint method were 89%, 85%, and 0.85, respectively. In conclusion, the proposed joint method was effective in a complex scenario for multiclass EEG signal recognition.



中文翻译:

在复杂场景下基于脑电图的新型联合算法。

目前,在脑电信号识别领域,脑电信号类别较多的复杂场景下的分类识别已经引起了越来越多的关注。基于联合快速傅里叶变换(FFT)和支持向量机(SVM)方法,本研究针对具有10种脑电信号分类的复杂场景,提出了一种新颖的脑电信号处理联合方法。提出了综合效率公式。该公式考虑了联合方法的准确性和时间消耗。这种新的联合方法可以提高多类脑电信号识别的准确性和综合效率。新的联合方法将标准化用于数据预处理。特征提取是通过结合FFT和主成分分析方法进行的。脑电信号使用加权k近邻nenighbour方法分类。在这项研究中,实验是使用脑波0-9位数分类的公共数据集进行的。结果表明,该联合方法的准确性和综合效率分别为84%和87%,优于现有方法。新型关节法的准确率,召回率和F1分数分别为89%,85%和0.85。总之,所提出的联合方法在复杂的情况下对于多类脑电信号识别是有效的。分别优于现有方法。新型关节法的准确率,召回率和F1分数分别为89%,85%和0.85。总之,所提出的联合方法在复杂的情况下对于多类脑电信号识别是有效的。分别优于现有方法。新型关节法的准确率,召回率和F1分数分别为89%,85%和0.85。总之,所提出的联合方法在复杂的情况下对于多类脑电信号识别是有效的。

更新日期:2019-08-10
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