Expectation-Maximization Method for EEG-Based Continuous Cursor Control
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Research Article Expectation-Maximization Method for EEG-Based Continuous Cursor Control Xiaoyuan Zhu,1 Cuntai Guan,2 Jiankang Wu,2 Yimin Cheng,1 and Yixiao Wang1 1 Department 2 Institute
of Electronic Science and Technology, University of Science and Technology of China, Anhui, Hefei 230027, China for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613
Received 21 October 2005; Revised 12 May 2006; Accepted 22 June 2006 Recommended by William Allan Sandham 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. Copyright © 2007 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
Brain-computer interface (BCI) is a communication system in which the information sent to the external world does not pass through the brain’s normal output pathways. It provides a radically new communication option to people with neuromuscular impairments. In the past decade or so, researchers have made impressive progress in BCI [1]. In this paper our discussions focus on the Electroencephalogram (EEG) driven BCI. Different types of EEG signals have been used as the input of BCI system, such as slow cortical potentials (SCPs) [2], motor imagery signal [3], P300 [4, 5], and steady-state visual-evoked response (SSVER) [6]. In the recent years, EEG controlled cursor movement has attracted many research interests. In this kind of BCI, first, EEG is recorded from the scalp and digitalized in both temporal and spatial space by using acquisition system. Then the digitalized signals are subjected to one or more of feature extraction procedures, such as spectral analysis or spatial filtering. Afterwards, translation algorithm converts the EEG feature into command vector whose elements control different dimensions of cursor movement independently. Finally, the outputs of cursor control part are displayed on the screen. The subjects can learn from these feedbacks to improve their control performance. In Figure 1 we depict one-dimensional (1D) four-targets cursor-control system as an example. In the
scenario of 1D four targets cursor control, there are four targets on the right side of the screen. Targets 1 to 4 are from top to bottom. The original position of the cursor is on the middle of the left side. During each trial, the cursor moves across
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