Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b

Kai Keng Ang, Zheng Yang Chin, Chuanchu Wang, Cuntai Guan, Haihong Zhang, Kai Keng Ang, Zheng Yang Chin, Chuanchu Wang, Cuntai Guan, Haihong Zhang

Abstract

The Common Spatial Pattern (CSP) algorithm is an effective and popular method for classifying 2-class motor imagery electroencephalogram (EEG) data, but its effectiveness depends on the subject-specific frequency band. This paper presents the Filter Bank Common Spatial Pattern (FBCSP) algorithm to optimize the subject-specific frequency band for CSP on Datasets 2a and 2b of the Brain-Computer Interface (BCI) Competition IV. Dataset 2a comprised 4 classes of 22 channels EEG data from 9 subjects, and Dataset 2b comprised 2 classes of 3 bipolar channels EEG data from 9 subjects. Multi-class extensions to FBCSP are also presented to handle the 4-class EEG data in Dataset 2a, namely, Divide-and-Conquer (DC), Pair-Wise (PW), and One-Versus-Rest (OVR) approaches. Two feature selection algorithms are also presented to select discriminative CSP features on Dataset 2b, namely, the Mutual Information-based Best Individual Feature (MIBIF) algorithm, and the Mutual Information-based Rough Set Reduction (MIRSR) algorithm. The single-trial classification accuracies were presented using 10 × 10-fold cross-validations on the training data and session-to-session transfer on the evaluation data from both datasets. Disclosure of the test data labels after the BCI Competition IV showed that the FBCSP algorithm performed relatively the best among the other submitted algorithms and yielded a mean kappa value of 0.569 and 0.600 across all subjects in Datasets 2a and 2b respectively.

Keywords: Bayesian classification; brain-computer interface; electroencephalogram; feature selection; mutual information.

Figures

Figure 1
Figure 1
Architecture of the filter bank common spatial pattern (FBCSP) algorithm for the training and evaluation phases.
Figure 2
Figure 2
The illustration on the extraction of a single-trial EEG segment from the training data for the multi-class FBCSP training phase in Dataset 2a, and the generation of the classification outputs using the multi-class extension to FBCSP on the entire time segment of a single-trial for the evaluation phase.
Figure 3
Figure 3
The illustration on the extraction of a single-trial EEG segment from the training data for the FBCSP training phase in Dataset 2b, and the generation of the classification outputs using FBCSP on the entire time segment of a single-trial for the evaluation phase.

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