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Hangman BCI: An unsupervised adaptive self-paced Brain–Computer Interface for playing games

Hasan, Bashar Awwad Shiekh and Gan, John Q (2012) 'Hangman BCI: An unsupervised adaptive self-paced Brain–Computer Interface for playing games.' Computers in Biology and Medicine, 42 (5). pp. 598-606. ISSN 0010-4825

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This paper presents a novel user interface suitable for adaptive Brain Computer Interface (BCI) system. A customized self-paced BCI architecture is introduced where the system combines onset detection system along with an adaptive classifier working in parallel. An unsupervised adaptive method based on sequential expectation maximization for Gaussian mixture model is employed with new timing scheme and an additional averaging step to avoid over-fitting. Sigmoid function based post-processing approach is proposed to enhance the classifiers' output. The adaptive system is compared to a non-adaptive one and tested on five subjects who used the BCI to play the hangman game. The results show significant improvement of the True-False difference for all the classes and a reduction in the number of steps required to solve the problem. © 2012 Elsevier Ltd.

Item Type: Article
Uncontrolled Keywords: Self-paced Brain-Computer Interfaces; Adaptive BCI; Gaussian mixture models; Discrete control; Post-processing; Human-machine interaction
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
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: 23 Apr 2013 09:04
Last Modified: 15 Jan 2022 00:26

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