Expectation-Maximization Method for EEG-Based Continuous Cursor Control. (19th October 2006)

• Record Type: Journal Article
• Authors: Zhu Zhu, Xiaoyuan Xiaoyuan; Guan Guan, Cuntai Cuntai; Wu Wu, Jiankang Jiankang; Cheng Cheng, Yimin Yimin; Wang Wang, Yixiao Yixiao
• Other Names: Sandham Sandham William Allan William Allan Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2007(2007)
• Publication Date: 2006-10-19
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2007/49037
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : To develop effective learning algorithms for continuous prediction of cursor movement using EEG signals is a challenging research issue in brain-computer interface (BCI). In this paper, we propose a novel statistical approach based on expectation-maximization (EM) method to learn the parameters of a classifier for EEG-based cursor control. To train a classifier for continuous prediction, trials in training data-set are first divided into segments. The difficulty is that the actual intention (label) at each time interval (segment) is unknown. To handle the uncertainty of the segment label, we treat the unknown labels as the hidden variables in the lower bound on the log posterior and maximize this lower bound via an EM-like algorithm. Experimental results have shown that the averaged accuracy of the proposed method is among the best.