An extended binary subband canonical correlation analysis detection algorithm oriented to the radial contraction-expansion motion steady- state visual evoked paradigm

The radial contraction-expansion motion paradigm is a novel steady-state visual evoked experimental paradigm,and the electroencephalography(EEG)evoked potential is different from the traditional luminance modulation paradigm.The signal energy is concentrated chiefly in the fundamental frequency,while the higher harmonic power is lower.Therefore,the conventional steady-state visual evoked potential recognition algorithms optimizing multiple harmonic response components,such as the extended canonical correlation analysis(eCCA)and task-related component analysis(TRCA)algorithm,have poor recognition performance under the radial contraction-expansion motion paradigm.This paper proposes an extended binary subband canonical correlation analysis(eBSCCA)algorithm for the radial contraction-expansion motion paradigm.For the radial contraction-expansion motion paradigm,binary subband filtering was used to optimize the weighting coefficients of different frequency response signals,thereby improving the recognition performance of EEG signals.The results of offline experiments involving 13 subjects showed that the eBSCCA algorithm exhibits a better performance than the eCCA and TRCA algorithms under the stimulation of the radial contraction-expansion motion paradigm.In the online experiment,the average recognition accuracy of 13 subjects was 88.68%±6.33%,and the average information transmission rate(ITR)was 158.77±43.67 bits/min,which proved that the algorithm had good recognition effect signals evoked by the radial contraction-expansion motion paradigm.

基于改进扩展典型相关分析的SSVEP信号识别方法

现有的稳态视觉诱发电位(SSVEP)的信号识别方法没有充分关注信号的相位特征在识别过程中的重要作用,为此提出一种扩展典型相关分析(eCCA)的改进方法。将联合频率-相位调制编码的刺激范式中的相位参数添加到由受试者训练数据所构造的参考信号,以此来实现对eCCA的相位约束,从而提升eCCA方法对SSVEP信号的识别性能。通过在公开数据集上与现有的SSVEP信号识别方法进行对比实验,表明所提方法对SSVEP信号的平均识别率提高到82.76%,信息传输速率提高至116.18 bits/min,且具有更好的稳定性。

A Method of SSVEP Signal Identification Based on Improved eCAA

Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the existing signal recognition methods for SSVEP do not fully pay attention to the important role of signal phase characteristics in the recognition process.Therefore,an improved method based on extended Canonical Correlation Analysis(eCCA)is proposed.The phase parameters are added from the stimulus paradigm encoded by joint frequency phase modulation to the reference signal constructed from the training data of the subjects to achieve phase constraints on eCCA,thereby improving the recognition performance of the eCCA method for SSVEP signals,and transmit the collected signals to the robotic arm system to achieve control of the robotic arm.In order to verify the effectiveness and advantages of the proposed method,this paper evaluated the method using SSVEP signals from 35 subjects.The research shows that the proposed algorithm improves the average recognition rate of SSVEP signals to 82.76%,and the information transmission rate to 116.18 bits/min,which is superior to TRCA and traditional eCAA-based methods in terms of information transmission speed and accuracy,and has better stability.

增强典型相关分析及其在多模态生物特征识别特征层融合中的应用

提出了增强典型相关分析(ECCA)的概念,并将ECCA用于多模态生物特征的特征层融合.ECCA不仅保持了CCA的本质特征,而且利用了类别信息,能够找到两个特征空间对分类更有意义的投影方向.开集测试表明,ECCA用于特征层融合时,可以获得比广义典型相关分析、串行融合、并行融合特征层融合算法和加法规则、乘法规则等分数层融合算法更好的性能.

引入权重系数重构个体模板的稳态视觉诱发电位识别

针对空间滤波相关算法在数据校准阶段通常采用直接平均化训练数据的方式,本文提出了一种更为细致的操作方法。首先针对电话拨号稳态视觉诱发电位(SSVEP)数据集,在扩展典型相关分析(eCCA)的基础上,重新选择适合本数据集的系数特征组合;其次引入各试次训练数据权重系数,采用两种计算方式和两种信号评估指标,分别对相关分析算法中的个体模板重新构造得到一种新的方法,即coef-eCCA(coefficient eCCA)。实验结果表明,重新选择系数特征后的相关分析算法与标准eCCA相比,识别准确率在不同时间窗下均有提高,并且在减小计算成本方面的提升尤为显著;重新构造个体模板后,coef-eCCA在固定时间窗下的识别准确率最高提升至99%,同时训练消耗时间并没有受到较大影响,验证了该方法的有效性。