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Practical Artifact Removal Brain-Computer Interface System: Application to Neuroprosthetics

Practical Artifact Removal Brain-Computer Interface System: Application to Neuroprosthetics
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Author(s): Wei-Yen Hsu (National Chung Cheng University, Taiwan)
Copyright: 2014
Pages: 13
Source title: Emerging Theory and Practice in Neuroprosthetics
Source Author(s)/Editor(s): Ganesh R. Naik (University of Technology Sydney (UTS), Australia) and Yina Guo (Taiyuan University of Science and Technology, China)
DOI: 10.4018/978-1-4666-6094-6.ch013

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Abstract

In this chapter, a practical artifact removal Brain-Computer Interface (BCI) system for single-trial Electroencephalogram (EEG) data is proposed for applications in neuroprosthetics. Independent Component Analysis (ICA) combined with the use of a correlation coefficient is proposed to remove the EOG artifacts automatically, which can further improve classification accuracy. The features are then extracted from wavelet transform data by means of the proposed modified fractal dimension. Finally, Support Vector Machine (SVM) is used for the classification. When compared with the results obtained without using the EOG signal elimination, the proposed BCI system achieves promising results that will be effectively applied in neuroprosthetics.

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