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Due to the non-stationarity of EEG signals, online training and adaptation are essential to EEG based brain–computer interface (BCI) systems. Self-paced BCIs offer more natural human–machine interaction than synchronous BCIs, but it is a great challenge to train and adapt a self-paced BCI online because the user’s control intention and timing are usually unknown. This paper proposes a novel motor...
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