An inter-subject model to reduce the calibration time for motion imagination-based brain-computer interface
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A major factor blocking the practical application of brain-computer interfaces (BCI) is the long calibration time. To obtain enough training trials, participants must spend a long time in the calibration stage. In this paper, we propose a new framework to reduce the calibration time through knowledge transferred from the electroencephalogram (EEG) of other subjects. We trained the motor recognition model for the target subject using both the target’s EEG signal and the EEG signals of other subjects. To reduce the individual variation of different datasets, we proposed two data mapping methods. These two methods separately diminished the variation caused by dissimilarities in the brain activation region and the strength of the brain activation in different subjects. After these data mapping stages, we adopted an ensemble method to aggregate the EEG signals from all subjects into a final model. We compared our method with other methods that reduce the calibration time. The results showed that our method achieves a satisfactory recognition accuracy using very few training trials (32 samples). Compared with existing methods using few training trials, our method achieved much greater accuracy.
KeywordsBrain-computer interface (BCI) Electroencephalogram (EEG) Machine learning Movement imagination Common spatial pattern Inter-subject model
This work was supported in part by National High Technology Research and Development Program of China (863 Program) under Grant 2015AA042301 and the National Natural Science Foundation of China under Grant 61773369, 61573340, 61503374.
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