Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(942.73KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Moving One Dimensional Cursor Using Extracted Parameter
Siti Zuraimi BT Salleh, Norlaili BT Mat Safri, Siti Hajar Aminah Ali, Norlaili Mat Safri
Pages - 110 - 119     |    Revised - 30-10-2009     |    Published - 30-11-2009
Volume - 3   Issue - 5    |    Publication Date - November 2009  Table of Contents
MORE INFORMATION
KEYWORDS
brain-computer interface, electroencephalogram (EEG), extracted parameter, Fast Fourier Transform (FFT)
ABSTRACT
This study focuses on developing a method to determine parameters to control cursor movement using noninvasive brain signals, or electroencephalogram (EEG) for brain-computer interface (BCI). There were two conditions applied i.e. Control condition where subjects relax (resting state); and Task condition where subjects imagine a movement. During both conditions, EEG signals were recorded from 19 scalp locations. In Task condition, subjects were asked to imagine a movement to move the cursor on the screen towards target position. Fast Fourier Transform (FFT) was used to analyse the recorded EEG signals. To obtain maximum speed and accuracy, EEG data were divided into various interval and difference in power values between Task and Control conditions were calculated. As conclusion, the present study suggests that difference in delta frequency band between resting and active imagination may be use to control one dimensional cursor movement and the region that gives optimum output is at the parietal region.
CITED BY (1)  
1 Azmy, H., & Safri, N. M. (2013). EEG Based BCI Using Visual Imagery Task for Robot Control. Jurnal Teknologi, 61(2).
1 Google Scholar 
2 ScientificCommons 
3 Academic Index 
4 CiteSeerX 
5 refSeek 
6 iSEEK 
7 Socol@r  
8 ResearchGATE 
9 Bielefeld Academic Search Engine (BASE) 
10 Scribd 
11 WorldCat 
12 SlideShare 
13 PDFCAST 
14 PdfSR 
1 J.R. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, T.M. Vaughan. “Brain-computer interfaces for communication and control”. Clinical Neurophysiology, 113: 767-791, 2002
2 D.J. McFarland and J.R. Wolpaw. “Sensorimotor rhythm-based brain-computer interface (BCI): feature selection by regression improves performance”. IEEE Transaction on neural Systems and Rehab., 13(3): 372-379, 2005
3 J.R. Millan, F. Renkens, J. Mourino and W. Gerstner. “Brain-actuated interaction”. Artificial Intelligence, 159: 241-259, 2004
4 J.M. Carmena, M.A. Lebedev, R.E. Crist, J.E.O’Doherty, D.M. Santucci, D.F. Dimitrov, P.G. Patil, C. S. Henriquez and M.A.L. Nicolelis. “Learning to control a brain-machine interface for reaching and grasping by primates”. PLOS Biology, 1(2): 193-208, 2003
5 J.R. Wolpaw, D.J. McFarland, T.M. Vaughan and G. Schalk. “Control of a two dimensional movement signal by a noninvasive brain-computer interface in humans”. PNAS, 101(51):17849-17854, 2004
6 M. M. Ahmed and D. Mohammad. “Segmentation of brain MR images for tumor extraction by combining kmeans clustering and Perona-Malik anistropic diffusion model”. International Journal of Image Processing, 2(1), 27-34, 2008
7 “Biomedical Signals Amplifier”, ElettronicaVeneta, pp. 27 (2006)
8 R. S. Manzoor, R. Gani, V. Jeoti, N. Kamel and M. Asif. “Dwpt based FFT and its application to SNR estimation in OFDM Systems”. Signal Processing: An International Journal, 3(2), 22-33, 2009
9 E.C. Leuthardt, G. Schalk and J.R. Wolpaw. “A brain-computer interface using electrocorticographic signals in human”.J. Neural Eng., 1: 63-71, 2004
10 J.R. Wolpaw, D.J. McFarland, T.M. Vaughan. “Brain-computer interface research at the Wadsworth Center”. IEEE Transaction on Neural Systems and Rehab.,8(2): 222-226, 2003
11 G. N. Martin. “Human Neuropsychology”, Prentice Hall, pp. 90, (1998)
12 J. Kandel, J. Schwartz and T. Jessel. “Principles of Neural Science”, Elsevier, (1991)
13 Mccrae, r. R., & costa, p. T., jr. “Toward a new generation of personality theories: theoretical contexts for the five-factor model. In J. S. Wiggins, the five-factor model”. The Guilford Press; 1 edition, pp 140-185 (1996).
Mr. Siti Zuraimi BT Salleh
UNIVERSITI TEKNOLOGI MALAYSIA - Malaysia
missxeetea_z@yahoo.com
Dr. Norlaili BT Mat Safri
UNIVERSITI TEKNOLOGI MALAYSIA - Malaysia
Miss Siti Hajar Aminah Ali
UNIVERSITI TEKNOLOGI TUN HUSSIEN ONN - Malaysia
Dr. Norlaili Mat Safri
- Malaysia