Copyright © 2008 The Institute of Electronics, Information and Communication Engineers
Regular Section -- Papers -- Rehabilitation Engineering and Assistive Technology |
EEG-Based Classification of Motor Imagery Tasks Using Fractal Dimension and Neural Network for Brain-Computer Interface
1 The authors are with the Chaos and Fractals Informatics Laboratory (NLAB), Department of Electrical Engineering, Faculty of Engineering, Nagaoka University of Technology, Nagaoka-shi, 940–2188 Japan. E-mail: montri{at}pelican.nagaokaut.ac.jp
In this study, we propose a method of classifying a spontaneous electroencephalogram (EEG) approach to a brain-computer interface. Ten subjects, aged 21–32 years, volunteered to imagine left- and right-hand movements. An independent component analysis based on a fixed-point algorithm is used to eliminate the activities found in the EEG signals. We use a fractal dimension value to reveal the embedded potential responses in the human brain. The different fractal dimension values between the relaxing and imaging periods are computed. Featured data is classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Two conventional methods, namely, the use of the autoregressive (AR) model and the band power estimation (BPE) as features, and the linear discriminant analysis (LDA) as a classifier, are selected for comparison in this study. Experimental results show that the proposed method is more effective than the conventional methods.
Key Words: brain-computer interface (BCI), electroencephalogram (EEG), motor imagery, fractal dimension (FD), neural network, independent component analysis
Manuscript received November 15, 2006. Manuscript revised July 12, 2007.
Reference
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