Motor imagery eeg classification based on riemannian sparse optimization and dempster-shafer fusion of multi-time-frequency patterns

HIGHLIGHTS

• who: Jing Jin. and colleagues from the AData Descriptions Dataset, : We choose Dataset IIa from BCI Competition IV [50] as the first dataset to evaluate the effectiveness of the proposed methods. It contains, channel EEG data recorded from , subjects performing , kinds of MI tasks, i.e [, ]. Left-Hand, Right-Hand, Feet, and Tongue mental tasks. Each participant completed two sessions, one for training and the other for evaluation [50]. We combine two sessions and choose Left and Right-Hand tasks to evaluate the binary classification performance, which contains , trials of each MI task. The training set and . . .
  

   

  

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