Filter Bank Feature Combination (FBFC) approach for brain-computer interface

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Filter Bank Feature Combination (FBFC) approach for brain-computer interface
Title:
Filter Bank Feature Combination (FBFC) approach for brain-computer interface
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The 2011 International Joint Conference on Neural Networks (IJCNN)
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Publication Date:
01 August 2011
Citation:
Zheng Yang Chin; Kai Keng Ang; Cuntai Guan; Chuanchu Wang; Haihong Zhang, "Filter Bank Feature Combination (FBFC) approach for brain-computer interface," Neural Networks (IJCNN), The 2011 International Joint Conference on , vol., no., pp.1352,1357, July 31 2011-Aug. 5 2011 doi: 10.1109/IJCNN.2011.6033381
Abstract:
The Filter Bank Common Spatial Pattern (FBCSP) algorithm constructs and selects subject-specific discriminative CSP features from a filter bank of spatial-temporal filters in a motor imagery brain-computer interface (MI-BCI). However, information from other types of features could be extracted and combined with CSP features to enhance the classification performance. Hence this paper proposes a Filter Bank Feature Combination (FBFC) approach and investigates the use of CSP and Phase Lock Value (PLV) features, where the latter measures the phase synchronization between the EEG electrodes. The performance of the FBFC using CSP and PLV features is evaluated on four-class motor imageries from the publicly available BCI Competition IV Dataset IIa. The experimental results showed that the proposed FBFC using CSP and PLV features yielded a significant improvement in cross-validation accuracies on the training data (p=0.008) and better session-to-session transfer accuracies to the evaluation data compared to the use of CSP features using the FBCSP algorithm. This motivates the research of FBFC using a battery of other features that could possibly benefit EEG-based BCIs and multi-modal BCI systems.
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