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Motor imagery based brain computer interface (BCI) has drawback of long subject dependent calibration session times. This can be a very exhausting and a time consuming process. In order to alleviate it, transfer learning and active learning approaches can be utilised. Informative instances are selected by applying active learning concept from other subjects under similar circumstances. Then, they...
Non-invasive EEG signal based brain computer interface (BCI) for motor imagery task - classification requires large number of subject specific training samples for each user session that reduces the user feasibility of BCI. A generalized classifier using few subject specific sample will ease the real world implementation of motor imagery based BCI. At first, this paper applies an improved active transfer...
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