Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification
نویسندگان
چکیده
منابع مشابه
Common Spatial Patterns Feature Extraction and Support Vector Machine Classification for Motor Imagery with the SecondBrain
Recently, a large set of electroencephalography (EEG) data is being generated by several high-quality labs worldwide and is free to be used by all researchers in the world. On the other hand, many neuroscience researchers need these data to study different neural disorders for better diagnosis and evaluating the treatment. However, some format adaptation and pre-processing are necessary before ...
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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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In the present study, we propose a neurofeedback-based motor imagery training system for EEG-based brain-computer interface (BCI). The proposed system can help individuals get the feel of motor imagery by presenting them with real-time brain activation maps on their cortex. Ten healthy participants took part in our experiment, half of whom were trained by the suggested training system and the o...
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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...
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ژورنال
عنوان ژورنال: Computational and Mathematical Methods in Medicine
سال: 2018
ISSN: 1748-670X,1748-6718
DOI: 10.1155/2018/9871603