title: Incorporation of Inter-Subject Information to Improve the Accuracy of Subject-Specific P300 Classifiers.
abstract: Although the inter-subject information has been demonstrated to be effective for a rapid calibration of the P300-based brain-computer interface (BCI), it has never been comprehensively tested to find if the incorporation of heterogeneous data could enhance the accuracy. This study aims to improve the subject-specific P300 classifier by adding other subject's data. A classifier calibration strategy, weighted ensemble learning generic information (WELGI), was developed, in which elementary classifiers were constructed by using both the intra- and inter-subject information and then integrated into a strong classifier with a weight assessment. 55 subjects were recruited to spell 20 characters offline using the conventional P300-based BCI, i.e. the P300-speller. Four different metrics, the P300 accuracy and precision, the round accuracy, and the character accuracy, were performed for a comprehensive investigation. The results revealed that the classifier constructed on the training dataset in combination with adding other subject's data was significantly superior to that without the inter-subject information. Therefore, the WELGI is an effective classifier calibration strategy which uses the inter-subject information to improve the accuracy of subject-specific P300 classifiers, and could also be applied to other BCI paradigms.
authors.affiliation: 1 Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin 300072, P. R. China.
authors.forename: Minpeng
authors.initials: M
authors.lastname: Xu
authors.affiliation: 1 Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin 300072, P. R. China.
authors.forename: Jing
authors.initials: J
authors.lastname: Liu
authors.affiliation: 1 Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin 300072, P. R. China.
authors.forename: Long
authors.initials: L
authors.lastname: Chen
authors.affiliation: 1 Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin 300072, P. R. China.
authors.forename: Hongzhi
authors.initials: H
authors.lastname: Qi
authors.affiliation: 1 Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin 300072, P. R. China.
authors.forename: Feng
authors.initials: F
authors.lastname: He
authors.affiliation: 1 Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin 300072, P. R. China.
authors.forename: Peng
authors.initials: P
authors.lastname: Zhou
authors.affiliation: 1 Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin 300072, P. R. China.
authors.forename: Baikun
authors.initials: B
authors.lastname: Wan
authors.affiliation: 1 Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin 300072, P. R. China.
authors.forename: Dong
authors.initials: D
authors.lastname: Ming
mesh_terms: D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D002138:Calibration; D066264:Datasets as Topic; D004569:Electroencephalography / Q000379:methods*; D018913:Event-Related Potentials, P300 / Q000502:physiology; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D055815:Young Adult
keywords: Brain–computer interface; P300-speller; classifier calibration; event-related potential; inter-subject information