Objective

The main objective of this paper is to propose a novel technique, called filter bank maximum a-posteriori common spatial pattern (FB-MAP-CSP) algorithm, for online classification of multiple motor imagery activities using electroencephalography (EEG) signals. The proposed technique addresses the overfitting issue of CSP in addition to utilizing the spectral information of EEG signals inside the framework of filter banks while extending it to more than two conditions.

Materials and methods

The classification of motor imagery signals is based upon the detection of event-related de-synchronization (ERD) phenomena in the μ and β rhythms of EEG signals. Accordingly, two modifications in the existing MAP-CSP technique are presented: (i) The (pre-processed) EEG signals are spectrally filtered by a bank of filters lying in the μ and β brainwave frequency range, (ii) the framework of MAP-CSP is extended to deal with multiple (more than two) motor imagery tasks classification and the spatial filters thus obtained are calculated for each sub-band, separately. Subsequently, the most imperative features over all sub-bands are selected and un-regularized linear discriminant analysis is employed for classification of multiple motor imagery tasks.

Results

Publicly available dataset (BCI Competition IV Dataset I) is used to validate the proposed method i.e. FB-MAP-CSP. The results show that the proposed method yields superior classification results, in addition to be computationally more efficient in the case of online implementation, as compared to the conventional CSP based techniques and its variants for multiclass motor imagery classification.

Conclusion

The proposed FB-MAP-CSP algorithm is found to be a potential / superior method for classifying multi-condition motor imagery EEG signals in comparison to FBCSP based techniques.

中文翻译：

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使用基于滤波器组的最大后验共同空间图案滤波器对多个运动图像任务进行在线分类

目的

本文的主要目的是提出一种新技术，称为滤波器组最大后验通用空间模式（FB-MAP-CSP）算法，用于使用脑电图（EEG）信号对多个运动图像活动进行在线分类。提出的技术除了在滤波器组框架内利用EEG信号的频谱信息，同时将其扩展到两个以上的条件之外，还解决了CSP的过拟合问题。

材料和方法

运动图像信号的分类基于对EEG信号的μ和β节律中事件相关的失步（ERD）现象的检测。因此，对现有的MAP-CSP技术进行了两种修改：（i）（预处理的）EEG信号通过位于μ和β处的一组滤波器进行频谱滤波脑波频率范围，（ii）扩展了MAP-CSP的框架以处理多个（两个以上）运动图像任务分类，并分别为每个子带计算由此获得的空间滤波器。随后，选择所有子带上最重要的功能，并使用非正规线性判别分析对多个运动图像任务进行分类。

结果

公开可用的数据集（BCI竞赛IV数据集I）用于验证所提出的方法，即FB-MAP-CSP。结果表明，与传统的基于CSP的技术及其用于多类运动图像分类的变体相比，所提出的方法除在线执行时在计算上更有效之外，还产生了更好的分类结果。

结论

与基于FBCSP的技术相比，所提出的FB-MAP-CSP算法被认为是对多条件运动图像EEG信号进行分类的一种潜在/优越的方法。