Before revision:  Electroencephalogram (EEG) is the most frequently used input signal in BCIs . However, EEG signals are weak, easily contaminated by interferences and noise, non-stationary for the same subject, and varying among different subjects . So , it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, in different sessions, for different devices and tasks.
After revision:  Electroencephalogram (EEG) is the most frequently used input signal in BCIs . However, EEG signals are weak, easily contaminated by interferences and noise, non-stationary for the same subject, and varying across different subjects and sessions. Therefore , it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, in different sessions, for different devices and tasks.