33% Classification Accuracy Improvement in a Motor Imagery Brain Computer Interface
نویسندگان
چکیده
منابع مشابه
33% Classification Accuracy Improvement in a Motor Imagery Brain Computer Interface
A right-hand motor imagery based brain-computer interface is proposed in this work. Such a system requires the identification of different brain states and their classification. Brain signals recorded by electroencephalography are naturally contaminated by various noises and interferences. Ocular artifact removal is performed by implementing an automatic method “Kmeans-ICA” which does not requi...
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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...
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...
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Motor imagery classification in electroencephalography (EEG)-based brain–computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the stateof-the-art approaches. To ...
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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: Journal of Biomedical Science and Engineering
سال: 2017
ISSN: 1937-6871,1937-688X
DOI: 10.4236/jbise.2017.106025