An online semi-supervised P300 speller based on extreme learning machine

作者：Wang, Junjie; Gu, Zhenghui^*; Yu, Zhuliang; Li, Yuanqing

来源：Neurocomputing, 2017, 269: 148-151.

DOI：10.1016/j.neucom.2016.12.098

• Brain-computer interface
• Semi-supervised learning
• Extreme learning machine

Semi-supervised learning has been applied in brain-computer interfaces (BCIs) to reduce calibration time for user. For example, a sequential updated self-training least squares support vector machine (SUST-LSSVM) was devised for online semi-supervised P300 speller. Despite its good performance, the computational complexity becomes too high after several updates, which hinders its practical online application. In this paper, we present a self-training regularized weighted online sequential extreme learning machine (ST-RWOS-ELM) for P300 speller. It achieves much lower complexity compared to SUST-LSSVM without affecting the spelling accuracy performance. The experimental results validate its effectiveness in the P300 system.

• 出版日期2017-12-20
• 单位华南理工大学