A model combining multi branch spectral-temporal cnn, efficient channel attention, and lightgbm for mi-bci classification

HIGHLIGHTS

• who: LightGBM and collaborators from the convert individual brain cognitive activity (ie., motor intentions) into actual instructions to help people to communicate with external devices [1, ]. For this to happen, several BCIs-based electroencephalography (EEG) signals were widely used, for instance, steady-state visual evoked potentials (SSVEP), evoked potentials, and motor imagery (MI) BCIs [3]. Among these BCI systems, only the Manuscript submitted for review November, . This work was supported in part by the National Natural Science Foundation of China have published the Article: A Model Combining Multi Branch Spectral-Temporal CNN, Efficient Channel Attention, and LightGBM . . .
  

   

  

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