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For the BCI research to classify the different imagined movements of both left and right hands, a method using wavelet packet decomposition for feature extraction and using SVM for pattern classification was adopted. Firstly discusses the wavelet packet transform in depth and brings out an idea of taking wavelet packet coefficients' variance as feature into account, then extracts the feature serials...
Brain-computer interface (BCI) system uses brain activity to control external devices such as computers and electronic devices. It is a novel kind of human computer interaction. BCI system can be regard as pattern recognition system, and the key point is classification of Electroencephalogram (EEG) signals under different mental tasks. Classification algorithms of BCI system include Fisher linear...
Electrocorticograms (ECoG) signals have many potential advantages and gained much attention for use with brain-computer interface (BCI). In this study, feature extraction using band powers was applied to ECoG signals from one subject performing imagined movements of either the left small-finger or the tongue. Probabilistic neural network (PNN) which was very suitable for classification problems was...
Brain-computer interface (BCI) uses brain activity for communication and control of objects in their environment without the participation of peripheral nerves and muscles. BCI technology can help improve the quality of life and restore functions for people with severe motor disabilities. We used combinations of wavelet entropy (WE) and band powers (BP) for feature extraction in BCI system which was...
In this study, a brain-computer interface (BCI) using electrocorticograms (ECoG) is proposed. Feature extraction is an important task that significantly affects the classification results. First, the discrete wavelet transform was applied to ECoG signals from one subject performing imagined movements of either the left small-finger or the tongue. After preprocessing, relative wavelet energy of selected...
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