Research Repository

The Study of Generic Model Set for Reducing Calibration Time in P300-Based Brain-Computer Interface.

Jin, Jing and Li, Shurui and Daly, Ian and Miao, Yangyang and Liu, Chang and Wang, Xingyu and Cichocki, Andrzej (2020) 'The Study of Generic Model Set for Reducing Calibration Time in P300-Based Brain-Computer Interface.' IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28 (1). pp. 3-12. ISSN 1534-4320

The_Study_of_Generic_Model_Set_for_Reducing_Calibration_Time_in_P300-Based_BrainComputer_Interface.pdf - Accepted Version

Download (3MB) | Preview


P300-based brain-computer interfaces (BCIs) provide an additional communication channel for individuals with communication disabilities. In general, P300-based BCIs need to be trained, offline, for a considerable period of time, which causes users to become fatigued. This reduces the efficiency and performance of the system. In order to shorten calibration time and improve system performance, we introduce the concept of a generic model set. We used ERP data from 116 participants to train the generic model set. The resulting set consists of ten models, which are trained by weighted linear discriminant analysis (WLDA). Twelve new participants were then invited to test the validity of the generic model set. The results demonstrated that all new participants matched the best generic model. The resulting mean classification accuracy equaled 80% after online training, an accuracy that was broadly equivalent to the typical training model method. Moreover, the calibration time was shortened by 70.7% of the calibration time of the typical model method. In other words, the best matching model method only took 81s to calibrate, while the typical model method took 276s. There were also significant differences in both accuracy and raw bit rate between the best and the worst matching model methods. We conclude that the strategy of combining the generic models with online training is easily accepted and achieves higher levels of user satisfaction (as measured by subjective reports). Thus, we provide a valuable new strategy for improving the performance of P300-based BCI.

Item Type: Article
Uncontrolled Keywords: P300 speller; brain computer interface; WLDA; generic model set; matching method; online training strategy
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 14 Jul 2021 09:57
Last Modified: 15 Jan 2022 01:31

Actions (login required)

View Item View Item