A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain–Computer Interface - Huang, Xin; Xu, Yilu; Hua, Jing; Yi, Wenlong; Yin, Hua; Hu, Ronghua; Wang, Shiyi (Frontiers in Neuroscience, 2021)
In an electroencephalogram- (EEG-) based brain-computer interface (BCI), a subject can directly communicate with an electronic device using his EEG signals in a safe and convenient way. However, the sensitivity to noise/artifact and the non-stationarity of EEG signals result in high inter-subject/session variability. Therefore, each subject usually spends long and tedious calibration time in building a subject-specific classifier. To solve this problem, we review existing signal processing approaches, including transfer learning (TL), semi-supervised learning (SSL), and a combination of TL and SSL. TL transfers the labeled samples from different source subjects for the target subject. SSL simultaneously utilizes the labeled and unlabeled samples from the target subject. Both TL and SSL can reduce calibration time for the target subject by minimizing the need of labeled samples. The combination of TL and SSL can take advantage of each other. For each kind of signal processing approaches, we introduce their concepts and representative methods. In the end, we draw a conclusion and point to research directions in the future.