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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...
Feature extraction and 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. Energy entropy was...
In this paper, two methods for the classification of motor imagery electroencephalogram (EEG) recordings in a brain computer interface (BCI) task was described. The first one is based on a time-frequency analysis of EEG signals. The original EEG signals are converted to time-frequency signals by a function of short time Fourier transforms (STFTs). In another method, we applied second-order blind identification...
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