Mental Non-motor Imagery Tasks Classifications of Brain Computer Interface for Wheelchair Commands Using Genetic Algorithm-Based Neural Network

被引：16

作者：

Chai, Rifai ^{[1
]}

Ling, Sai Ho ^{[1
]}

Hunter, Gregory P. ^{[1
]}

Nguyen, Hung T. ^{[1
]}

机构：

[1] Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Hlth Technol, Sydney, NSW 2007, Australia

来源：

2012 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2012年

关键词：

genetic algorithm (GA); artificial neural network (ANN); brain computer interface (BCI); electroencephalography (EEG); evolutionary algorithm (EA); MOTOR IMAGERY; SIGNAL; SYSTEM; HEAD; ALS;

D O I：

10.1109/IJCNN.2012.6252499

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A genetic algorithm (GA)-based neural network classification in the application of brain computer interface (BCI) for controlling a wheelchair is presented in this paper. This study uses an electroencephalography (EEG) as a non-invasive BCI approach to discriminate three non-motor imagery mental tasks for disabled individuals who may have difficulty in using BCI based motor imagery tasks. The three tasks classification is mapped into three wheelchair movements: left, right and forward and the relevant combination mental tasks used in this study are mental arithmetic, letter composing, Rubik's cube rolling, visual counting, ringtone imagery and spatial navigation. The results show the proposed system provides good classification performance after selecting the most effective of three discriminative tasks across combination of the different non-motor imagery mental tasks for the five subjects tested. The average classification accuracy is between 76% and 85%, with information transfer rates varies from 0.5 to 0.8 bits per trial.

引用

页数：7

共 29 条

[1] Towards an independent brain-computer interface using steady state visual evoked potentials [J].

Allison, Brendan. Z. ;

McFarland, Dennis J. ;

Schalk, Gerwin ;

Zheng, Shi Dong ;

Jackson, Melody Moore ;

Wolpaw, Jonathan R. .

CLINICAL NEUROPHYSIOLOGY, 2008, 119 (02) :399-408

[2]

Anderson C. W., 1995, Neural Networks for Signal Processing V. Proceedings of the 1995 IEEE Workshop (Cat. No.95TH8094), P475, DOI 10.1109/NNSP.1995.514922

[3]

[Anonymous], EMBC 2009 ANN INT C

[4] A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals [J].

Bashashati, Ali ;

Fatourechi, Mehrdad ;

Ward, Rabab K. ;

Birch, Gary E. .

JOURNAL OF NEURAL ENGINEERING, 2007, 4 (02) :R32-R57

[5] The thought-translation device (TTD):: Neurobehavioral mechanisms and clinical outcome [J].

Birbaumer, N ;

Hinterberger, T ;

Kübler, A ;

Neumann, N .

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2003, 11 (02) :120-123

[6] Initial oh-line evaluations of the LF-ASD brain-computer interface with able-bodied and spinal-cord subjects using imagined voluntary motor potentials [J].

Birch, GE ;

Bozorgzadeh, Z ;

Mason, SG .

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2002, 10 (04) :219-224

[7] Auditory and spatial navigation imagery in Brain-Computer Interface using optimized wavelets [J].

Cabrera, Alvaro Fuentes ;

Dremstrup, Kim .

JOURNAL OF NEUROSCIENCE METHODS, 2008, 174 (01) :135-146

[8] Adaptive EEG thought pattern classifier for advanced wheelchair control [J].

Craig, D. A. ;

Nguyen, H. T. .

2007 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-16, 2007, :2544-2547

[9]

Craig D A, 2006, Conf Proc IEEE Eng Med Biol Soc, V2006, P1291

[10] Cognitive tasks for driving a brain-computer interfacing system: A pilot study [J].

Curran, E ;

Sykacek, P ;

Stokes, M ;

Roberts, SJ ;

Penny, W ;

Johnsrude, I ;

Owen, AM .

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2004, 12 (01) :48-54

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