A Hybrid Brain-Computer Interface System using SS-VEP and EEG Related to Motor Imagery
نویسندگان
چکیده
منابع مشابه
A Study of Various Feature Extraction Methods on a Motor Imagery Based Brain Computer Interface System
Introduction: Brain Computer Interface (BCI) systems based on Movement Imagination (MI) are widely used in recent decades. Separate feature extraction methods are employed in the MI data sets and classified in Virtual Reality (VR) environments for real-time applications. Methods: This study applied wide variety of features on the recorded data using Linear Discriminant Analysis (LDA) classifie...
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BACKGROUND For a self-paced motor imagery based brain-computer interface (BCI), the system should be able to recognize the occurrence of a motor imagery, as well as the type of the motor imagery. However, because of the difficulty of detecting the occurrence of a motor imagery, general motor imagery based BCI studies have been focusing on the cued motor imagery paradigm. NEW METHOD In this pa...
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Background Most investigators of brain-computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)-based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subj...
متن کاملa study of various feature extraction methods on a motor imagery based brain computer interface system
introduction: brain computer interface (bci) systems based on movement imagination (mi) are widely used in recent decades. separate feature extraction methods are employed in the mi data sets and classified in virtual reality (vr) environments for real-time applications. methods: this study applied wide variety of features on the recorded data using linear discriminant analysis (lda) classifier...
متن کاملIntegrating Eeg and Meg Signals to Improve Motor Imagery Classification in Brain-computer Interface
We propose a fusion approach that combines features from simultaneously recorded electroencephalographic (EEG) and magnetoencephalographic (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard sin...
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ژورنال
عنوان ژورنال: Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications
سال: 2015
ISSN: 2188-4730,2188-4749
DOI: 10.5687/sss.2015.130