Incorporation of Inter-Subject Information to Improve the Accuracy of Subject-Specific P300 Classifiers

• Published in: International journal of neural systems Vol. 26; no. 3; p. 1650010
• Main Authors: Xu, Minpeng, Liu, Jing, Chen, Long, Qi, Hongzhi, He, Feng, Zhou, Peng, Wan, Baikun, Ming, Dong
• Format: Journal Article
• Language: English
• Published: Singapore 01.05.2016
• Subjects: Brain - physiology; Brain-Computer Interfaces; Calibration; Datasets as Topic; Electroencephalography - methods; Event-Related Potentials, P300 - physiology; Female; Humans; Machine Learning; Male; Young Adult; classifier calibration; P300-speller; event-related potential; inter-subject information; Brain–computer interface
• ISSN: 0129-0657
• DOI: 10.1142/S0129065716500106

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.