Due to the high complexity and subject variability of motor imagery electroencephalogram, its decoding is limited by the inadequate accuracy of traditional recognition models. To resolve this problem, a recognition model for motor imagery electroencephalogram based on flicker noise spectrum (FNS) and weighted filter bank common spatial pattern ( wFBCSP) was proposed. First, the FNS method was used to analyze the motor imagery electroencephalogram. Using the second derivative moment as structure function, the ensued precursor time series were generated by using a sliding window strategy, so that hidden dynamic information of transition phase could be captured. Then, based on the characteristic of signal frequency band, the feature of the transition phase precursor time series and reaction phase series were extracted by wFBCSP, generating features representing relevant transition and reaction phase. To make the selected features adapt to subject variability and realize better generalization, algorithm of minimum redundancy maximum relevance was further used to select features. Finally, support vector machine as the classifier was used for the classification. In the motor imagery electroencephalogram recognition, the method proposed in this study yielded an average accuracy of 86.34%, which is higher than the comparison methods. Thus, our proposed method provides a new idea for decoding motor imagery electroencephalogram.
针对运动想象脑电信号复杂度高、受试者个体差异大、传统识别模型精度欠佳的问题，本文提出了基于闪噪谱方法及加权滤波器组共空间模式（ wFBCSP）的运动想象脑电信号识别模型。首先，采用闪噪谱方法对运动想象脑电信号进行解析，以二阶差矩为结构函数，采用滑窗策略生成前兆时间序列，以发掘过渡阶段的隐匿动态变化。其次，从信号频带特点出发，利用 wFBCSP分别对过渡阶段前兆时间序列及反应阶段序列进行特征提取，生成表征过渡阶段及反应阶段的特征向量。进一步，利用最小冗余最大相关算法对特征向量进行局部筛选，使所选特征能自适应于受试者的个体差异，具有更好的泛化性。最后，以支持向量机为分类器进行分类判别。实验结果表明，本文所提方法在运动想象脑电信号识别中取得了86.34%平均分类准确率，较对照方法性能更优，为运动想象脑电信号解码研究提供了新思路。.
Keywords: Brain-computer interface; Common spatial pattern; Electroencephalogram signal recognition; Feature selection; Flicker noise spectroscopy.