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 .%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。
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