Wavelet-Based Minimized Feature Selection for Motor Imagery 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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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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BACKGROUND Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and sp...
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ژورنال
عنوان ژورنال: The Journal of the Korea Contents Association
سال: 2010
ISSN: 1598-4877
DOI: 10.5392/jkca.2010.10.6.027