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Subject: search.filters.subject.Brain-Computer Interface

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    Regularizing multi-bands Common Spatial Patterns (RMCSP): A data processing method for brain-computer interface
    (2015-05-13)
    Thang, Le Quoc
    ;
    Temiyasathit, Chivalai
    In this paper, we propose a novel approach which is called the Regularizing Multi-bands Common Spatial Patterns (RMCSP) that particularly used for processing motor-imagery based Electroencephalography (EEG) data in Brain-computer Interface (BCI). The usage of BCI is severely limited due to the inconvenience of large number of channels used in recording devices. Moreover, Common Spatial Patterns (CSP) is a very well-known algorithm for its efficiency, but it just can extract the spatial information of the brain signals. To address these issues, we introduce the RMCSP method that exploits data in spectral, temporal and spatial domains in order to increase the classification accuracy in BCI. In addition, RMCSP is designed to handle EEG with small number of channels. To verify the efficacy of our approach, we rigorously tested the performances of the method in 17 subjects, from BCI competition datasets, in both two-class and four-class problems. Results show that RMCSP approach can outperform normal CSP method by nearly 10% in terms of median classification accuracy. It also enables us to significantly reduce the number of channels used in the datasets without decreasing the performances of the subjects.
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    Increase performance of four-class classification for motor-imagery based brain-computer interface
    (2014-01-01)
    Thang, Le Quoc
    ;
    Temiyasathit, Chivalai
    Brain computer interface (BCI) is a system that provide a direct communication between human brain and external devices. BCIs which based on mental tasks of users are widely used for disabled or paralyzed patients, in order to help their mobility. Preprocessing techniques have been extensively developed to increase the signal-to-noise ratio and spatial distribution of the signals. Common Spatial Pattern (CSP) has shown to be a robust and effective method for processing Electroencephalogram (EEG) data. However, the results of CSP filter are still far from being completely explored. CSP was originally designed for two-class problem despite the fact that a practical application of Motor-imagery (MI) based BCI contains numbers of activities. It is necessary to design the classification algorithm which applicable to more than two-class problem. In this paper we investigate the performance of CSP by selecting optimal time slice and components for training CSP filters in four-class BCI by separating the four-class problem into multiple binary classifications. Our method is verified in the testing phase with four different types of classification approaches which are linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), linear support vector machines (LSVM), and support vector machines with radial basis function kernel (RBF-SVM). The result showed that, under the optimal time slice and components, the classification accuracy reach 78.82% for the best untrained subject in this dataset. © 2014 IEEE.