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Classification of EEG signals is core issues on EEG-based brain computer interface (BCI). Typically, such classification has been performed using signals from a set of selected EEG sensors. Because EEG sensor signals are mixtures of effective signals and noise, which has low signal-to-noise ratio, motor imagery EEG signals can be difficult to classification. In this paper, the energy entropy was used...
Human thinking tasks evoke Electroencephalogram (EEG) signal changes, so EEG analysis can help to design communication systems of Brain computer interface (BCI). Second-order blind identification (SOBI), a blind source separation (BSS) algorithm was applied to preprocess EEG data. Subsequently, Fisher distance was used to select the features. Finally, classification of Motor Imagery EEG evoked by...
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