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The electrocorticogram(ECoG) is proved to have high signal-to-noise ratio(SNR), which makes it better fitting for BCIs. And this paper represents a kind of classification method of ECoG signals for motor imagery tasks(left finger and tongue). Band power(BP) with the frequency band of [8 30] was extracted as the feature, and the linear discriminant analysis(LDA), k-nearest neighbor(kNN) rules and linear...
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...
Over the last few years various P300 classification algorithms have been assessed using the P300 data provided by the Wadsworth center for brain-computer interface (BCI) competitions II and III. In this paper a novel method of P300 classification is presented and compared to the state of the art results obtained for BCI competition II data set lib and BCI competition III data set II. The novel classification...
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