Decoding subject-driven cognitive states from eeg signals for cognitive brain–computer interface

HIGHLIGHTS

• What: The authors investigated the feasibility of using electroencephalogram (EEG) signals to differentiate between four distinct cognitive states: resting state narrative memory music and subtraction tasks. The aim of this step is to effectively display the characteristics of brain signals in both the time and frequency domains, thereby better revealing patterns and properties of brainwave activities. The authors propose a custom convolutional neural_network model named TFand 30-45 Hz. The model intecies. grates aeach channel and frequency attention (CFA) map module at enhancing the network`s After compressing single-channel time-frequency to aaimed size of 60 . . .
  

   

  

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