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Electroencephalogram (EEG) signal classification plays an important role to facilitate physically impaired patients by providing brain-computer interface (BCI)-controlled devices. However, practical applications of BCI make it difficult to decode motor imagery-based brain signals for multiclass classification due to their non-stationary nature. In this study, we aim to improve multiclass classification...