EEG-Based Classification of Motor Imagery Tasks Using Fractal Dimension and Neural Network for Brain-Computer Interface


IEICE TRANSACTIONS on Information and Systems   Vol.E91-D   No.1   pp.44-53
Publication Date: 2008/01/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e91-d.1.44
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Rehabilitation Engineering and Assistive Technology
brain-computer interface (BCI),  electroencephalogram (EEG),  motor imagery,  fractal dimension (FD),  neural network,  independent component analysis,  

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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.