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  • A new parameter tuning approach for enhanced motor imagery EEG signal classification

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    SharmaPUB253.pdf (490.0Kb)
    Author(s)
    Kumar, Shiu
    Sharma, Alok
    Griffith University Author(s)
    Sharma, Alok
    Year published
    2018
    Metadata
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    Abstract
    A brain-computer interface (BCI) system allows direct communication between the brain and the external world. Common spatial pattern (CSP) has been used effectively for feature extraction of data used in BCI systems. However, many studies show that the performance of a BCI system using CSP largely depends on the filter parameters. The filter parameters that yield most discriminating information vary from subject to subject and manually tuning of the filter parameters is a difficult and time-consuming exercise. In this paper, we propose a new automated filter tuning approach for motor imagery electroencephalography (EEG) ...
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    A brain-computer interface (BCI) system allows direct communication between the brain and the external world. Common spatial pattern (CSP) has been used effectively for feature extraction of data used in BCI systems. However, many studies show that the performance of a BCI system using CSP largely depends on the filter parameters. The filter parameters that yield most discriminating information vary from subject to subject and manually tuning of the filter parameters is a difficult and time-consuming exercise. In this paper, we propose a new automated filter tuning approach for motor imagery electroencephalography (EEG) signal classification, which automatically and flexibly finds the filter parameters for optimal performance. We have evaluated the performance of our proposed method on two public benchmark datasets. Compared to the existing conventional CSP approach, our method reduces the average classification error rate by 2.89% and 3.61% for BCI Competition III dataset IVa and BCI Competition IV dataset I, respectively. Moreover, our proposed approach also achieved lowest average classification error rate compared to state-of-the-art methods studied in this paper. Thus, our proposed method can be potentially used for developing improved BCI systems, which can assist people with disabilities to recover their environmental control. It can also be used for enhanced disease recognition such as epileptic seizure detection using EEG signals.
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    Journal Title
    Medical and Biological Engineering and Computing
    Volume
    56
    Issue
    10
    DOI
    https://doi.org/10.1007/s11517-018-1821-4
    Copyright Statement
    © 2018 Springer Berlin / Heidelberg. This is an electronic version of an article published in Medical and Biological Engineering and Computing, Vol. 56(10):1861-1874, 2018. Medical and Biological Engineering and Computing is available online at: http://link.springer.com/ with the open URL of your article.
    Subject
    Biomedical Engineering not elsewhere classified
    Biomedical Engineering
    Electrical and Electronic Engineering
    Brain-computer interface (BCI)
    Filter tuning
    Genetic algorithm (GA)
    Motor imagery (MI)
    Temporal filters
    Publication URI
    http://hdl.handle.net/10072/381846
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    • Journal articles

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