基于非凸log模型的脑电时-频-空特征选择方法

EEG time-frequency-spatial feature selection method based on non-convex log model

针对运动想象脑电时-频-空特征选择问题,提出了基于非凸log模型的稀疏特征选择方法(LOG方法)。首先,对原始脑电信号进行时-频分解,得到多个时-频段;其次,针对每个时-频段使用共空域模式(CSP)提取特征,得到时-频-空特征集合;最后,通过提出的基于log函数的非凸稀疏优化模型进行特征选择和分类,该模型可有效缓解L1范数正则化的有偏估计。为验证本方法的有效性,用3个公开的运动想象脑电数据集进行实验,相比现有的凸稀疏优化模型,非凸log模型取得了82.5%的平均分类准确率。实验结果表明,LOG方法不仅分类准确率高,且模型具有较好的稳定性和鲁棒性。

Aiming at the time-frequency-spatial feature selection problem of motor imagery electroencephalogram(EEG),a sparse feature selection method based on non-convex log model is proposed,which is called the LOG method.First,the original EEG signal is decomposed into multiple time-frequency segmentations.Second,the common spatial pattern(CSP)is used for feature extraction on each time-frequency segmentation to obtain a time-frequency-spatial feature set.Finally,a non-convex sparse optimization model based on the log function is proposed for feature selection and classification,which can effectively alleviate the biased estimation problem of L1 norm regularization.To verify the effectiveness of the proposed method,experiments are conducted using three publicly available motor imagery EEG datasets.Compared with the existing convex sparse optimization model,the non-convex log model achieves the highest average classification accuracy of 82.5%.The experimental results show that the LOG method not only achieves better classification accuracy,but also has better stability and robustness.