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.
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Montri PHOTHISONOTHAI, Masahiro NAKAGAWA, "EEG-Based Classification of Motor Imagery Tasks Using Fractal Dimension and Neural Network for Brain-Computer Interface" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 1, pp. 44-53, January 2008, doi: 10.1093/ietisy/e91-d.1.44.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.1.44/_p
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@ARTICLE{e91-d_1_44,
author={Montri PHOTHISONOTHAI, Masahiro NAKAGAWA, },
journal={IEICE TRANSACTIONS on Information},
title={EEG-Based Classification of Motor Imagery Tasks Using Fractal Dimension and Neural Network for Brain-Computer Interface},
year={2008},
volume={E91-D},
number={1},
pages={44-53},
abstract={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.},
keywords={},
doi={10.1093/ietisy/e91-d.1.44},
ISSN={1745-1361},
month={January},}
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TY - JOUR
TI - EEG-Based Classification of Motor Imagery Tasks Using Fractal Dimension and Neural Network for Brain-Computer Interface
T2 - IEICE TRANSACTIONS on Information
SP - 44
EP - 53
AU - Montri PHOTHISONOTHAI
AU - Masahiro NAKAGAWA
PY - 2008
DO - 10.1093/ietisy/e91-d.1.44
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2008
AB - 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.
ER -