基于LDA分类器自适应决策面的运动意象脑电分类方法

Banghua Yang, Du Li, Baiheng Ma, Xuelin Gu, Dewen Kong
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引用次数: 1

摘要

为了解决同一被试在不同时间段的脑电运动图像信号空间特征不同导致分类准确率降低的问题,本文提出了一种基于共同空间模式(CSP)和决策面自适应线性判别分析(DSALDA)的脑电特征提取与识别方法。通过保存不同日期测试数据的CSP空间特征来更新线性判别分析(LDA)分类器的决策面阈值,并利用参数控制CSP空间特征在训练数据集和不同日期测试数据集之间的比例。实验收集了24名受试者的脑电图数据,每个受试者在不同的日期收集数据。结果表明,与CSP-LDA方法相比,该方法的平均准确率提高了6.35%,有效提高了不同时段运动意象脑电分类的准确性和稳定性。为运动图像脑机接口(BCI)系统在康复领域的广泛应用创造了条件。
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Motor Imagery EEG Classification Method Based on Adaptive Decision Surface of LDA Classifier
In order to solve the problem of reduced classification accuracy caused by the different spatial features of EEG motor imagery signals between the same subjects on different days, this paper proposes an EEG feature extraction and recognition method based on common spatial pattern (CSP) and decision surface adaptive linear discriminant analysis (DSALDA). The decision surface threshold of the linear discriminant analysis (LDA) classifier was updated by saving the CSP spatial feature of the different day's test data, and use parameters to control the proportion of CSP spatial features between the training data set and the different day’ test data set. The experiment collected EEG data of 24 subjects, each subject collected data on different days. The results show that the average accuracy of this method is improved by 6.35% compared with the CSP-LDA method, which effectively improves the accuracy and stability of the different day's motor imagery EEG classification. It has created conditions for the wide application of motor imagery brain-computer interface (BCI) system in the field of rehabilitation.
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