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http://dx.doi.org/10.5391/JKIIS.2012.22.1.62

Development of Mirror Neuron System-based BCI System using Steady-State Visually Evoked Potentials  

Lee, Sang-Kyung (중앙대학교 전자전기공학부)
Kim, Jun-Yeup (중앙대학교 전자전기공학부)
Park, Seung-Min (중앙대학교 전자전기공학부)
Ko, Kwang-Enu (중앙대학교 전자전기공학부)
Sim, Kwee-Bo (중앙대학교 전자전기공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.22, no.1, 2012 , pp. 62-68 More about this Journal
Abstract
Steady-State Visually Evoked Potentials (SSVEP) are natural response signal associated with the visual stimuli with specific frequency. By using SSVEP, occipital lobe region is electrically activated as frequency form equivalent to stimuli frequency with bandwidth from 3.5Hz to 75Hz. In this paper, we propose an experimental paradigm for analyzing EEGs based on the properties of SSVEP. At first, an experiment is performed to extract frequency feature of EEGs that is measured from the image-based visual stimuli associated with specific objective with affordance and object-related affordance is measured by using mirror neuron system based on the frequency feature. And then, linear discriminant analysis (LDA) method is applied to perform the online classification of the objective pattern associated with the EEG-based affordance data. By using the SSVEP measurement experiment, we propose a Brain-Computer Interface (BCI) system for recognizing user's inherent intentions. The existing SSVEP application system, such as speller, is able to classify the EEG pattern based on grid image patterns and their variations. However, our proposed SSVEP-based BCI system performs object pattern classification based on the matters with a variety of shapes in input images and has higher generality than existing system.
Keywords
Steady-State Visually Evoked Potentials; Mirror Neuron System; Linear Discriminant Analysis; EEG; Brain-Computer Interface;
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Times Cited By KSCI : 1  (Citation Analysis)
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