https://www.selleckchem.com/
Motor imagery (MI) is a mental representation of motor behavior and a widely used pattern in electroencephalogram (EEG) based brain-computer interface (BCI) systems. EEG is known for its non-stationary, non-linear features and sensitivity to artifacts from various sources. This study aimed to design a powerful classifier with a strong generalization capability for MI based BCIs. In this study, we proposed a cluster decomposing based ensemble learning framework (CDECL) for EEG classification of MI based BCIs. The EEG data was decomposed into sub-data sets wi
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