基于HHT和CSSD的多域融合自适应脑电特征提取方法  

An Adaptive Multi-Domain Fusion Feature Extraction with Method HHT and CSSD

作　　者：李明爱[1] 崔燕[1] 杨金福[1] 郝冬梅[2] 

机构地区：[1]北京工业大学电子信息与控制工程学院,北京100124 [2]北京工业大学生命科学与生物工程学院,北京100124

出　　处：《电子学报》2013年第12期2479-2486,共8页Acta Electronica Sinica

基　　金：北京市教委项目面上项目(No.KM201110005005);国家自然科学基金(No.61201362);北京市自然科学基金(No.7132021;No.4112011);北京工业大学基础研究基金(No.X4002011201101)

摘　　要：为改善运动想象脑电信号特征提取的自适应性和实时性,提出一种基于希尔伯特-黄变换(HHT)与共空域子空间分解算法(CSSD)的特征提取方法(HCSSD).在对脑电信号进行预处理的基础上,定义一种相对距离准则优选脑电极组合;计算脑电的Hilbert瞬时能量谱和边际能量谱,以获取脑电的时-频特征,并基于CSSD提取其空域特征,采用串行特征融合策略得到脑电的时-频-空特征;设计学习矢量量化神经网络分类器,实现脑电数据分类.在训练集与测试集间隔一周且减少导联数量的情况下,基于HCSSD对左手小指和舌头的运动想象ECoG脑电数据的平均识别率为92%.实验结果表明:HCSSD在增强特征提取方法的自适应性、改善实时性的同时,提高了脑电信号识别率,为便携式BCI系统在康复领域的应用创造了条件.The adaptivity and real-time performance of feature extraction method are crucial in brain-computer interface . Based on Hilbert-Huang transform （HHT ） and common spatial subspace decomposition （CSSD ） algorithm ,a novel feature extrac-tion method ,denoted as HCSSD ,was proposed .Firstly ,the motor imagery electroencephalography （EEG ）/ electrocorticography （ECoG ） was preprocessed ,and a relative distance criterion was defined to select the optimal combination of channels .Secondly , Hilbert instantaneous energy spectrum and marginal energy spectrum of EEG/ECoG were calculated to extract time feature and fre-quency feature respectively .Then CSSD was applied to extract spatial feature .Furthermore ,serial feature fusion strategy was adopted to obtain time-frequency-spatial feature .Finally ,learning vector quantization neural network was designed to classify the EEG/ECoG data .The average recognition accuracy was 92% for the left small finger and tongue motor imagery ECoG tasks .Experiment results show that HCSSD can enhance the adaptivity and real-time performance of feature extraction ,with the recognition accuracy im-proved .This method provides a new idea for the application of portable BCI system in rehabilitation field .

关 键 词：脑机接口 运动想象 希尔伯特-黄变换 共空域子空间分解 特征融合 自适应 brain-computer interface (BCI) motor imagery (MI) hilbert-huang transform (HHT) common spatial sub-space decomposition (CSSD ) 

分 类 号：R318[医药卫生—生物医学工程]