An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA Methods
Abstract
In the modern world of automation, biological signals, especially Electroencephalogram (EEG) is gaining wide attention as a source of biometric information. Eye-blinks and movement of the eyeballs produce electrical signals (contaminate the EEG signals) that are collectively known as ocular artifacts. These noise signals are required to be separated from the EEG signals to obtain the accurate results. This paper reports an experiment of ocular artifacts elimination from EEG signal using blind source separation algorithm based on independent component analysis and principal component analysis. EEG signals are recorded on three conditions, which are normal conditions, closed eyes, and blinked eyes. After processing, the dominant frequency of EEG signals in the range of 12-14 Hz either on normal, closed, and blinked eyes conditions is obtained.
Keywords
Full Text:
PDFReferences
R. R. Vázquez, H. V. Pérez, R. Ranta, V. D. Louis, D. Maquin, and L. Maillard, “Blind source separation, wavelet denoising and discriminant analysis for EEGartefacts and noise cancelling,� Biomedical Signal Processing and Control, vol. 7, pp. 389–400, 2012. crossref
L. Bi, X.-A. Fan, and Y. Liu, “EEG-Based Brain-Controlled Mobile Robots: A Survey,� IEEE Transactions On Human-Machine Systems, vol. 43, no. 2, pp. 161-176, 2013. crossref
P. F. Dieza, S. M. T. Müller, V. A. Mut, E. Laciar, E. Avila, T. F. B. Filho, and M. S. Filho, “Commanding a robotic wheelchair with a high-frequency steady-state visualevoked potential based brain–computer interface,� Medical Engineering & Physics,vol. 35, pp. 1155–1164, 2013. crossref
A. C. Lopes, G. Pires, and U. Nunes, “Assisted navigation for a brain-actuated intelligent wheelchair,�Robotics and Autonomous Systems,vol. 61, pp. 245–258, 2013. crossref
Turnip, D. Soetraprawata, and D. E. Kusumandari, "A Comparison of EEG Processing Methods to Improve the Performance of BCI,�International Journal of Signal Processing Systems, vol. 1, No. 1, 2013. crossref
Turnip, and D. E. Kusumandari, “Improvement of BCI performance through nonlinear independent component analisis extraction,�Journal of Computer, vol. 9, no. 3, pp. 688-695, March 2014. crossref
Z. Allisona, C. Brunner, C. Altstätter, I. C. Wagner, S. Grissmann, and C. Neuper,“A hybrid ERD/SSVEP BCI for continuous simultaneous two dimensional cursor control,� Journal of Neuroscience Methods,vol. 209, pp. 299– 307, 2012. crossref
Y. Li, J. Long, T. Yu, Z. Yu, C. Wang, H. Zhang, and C. Guan, “An EEG-Based BCI System for 2-D Cursor Control by Combining Mu/Beta Rhythm and P300 Potential,� IEEE Transactions On Biomedical Engineering, vol. 57, no. 10, pp. 2495-2504, 2010. crossref
L. Mayaud, S. Filipe, L. Pétégnief, O. Rochecouste, and M. Congedo,“Robust Brain-computer Interface for virtual Keyboard (RoBIK): Project results� IRBM, vol. 34, pp. 131–138, 2013. crossref
R. B. Ashari, I. A. Al-Bidewi, and M. I. Kamel,“Design and simulation of virtual telephone keypad control based on brain computer interface (BCI) with very high transfer rates,� Alexandria Engineering Journal, vol. 50, pp. 49–56, 2011. crossref
A. S. Royer, A. J. Doud, M. L. Rose, and B. He, “EEG Control of a Virtual Helicopter in 3-Dimensional Space Using Intelligent Control Strategies,� IEEE Transactions On Neural Systems And Rehabilitation Engineering, vol. 18, no. 6, pp. 581-589, 2010. crossref
Akce, M. Johnson, O. Dantsker, and T. Bretl, “A Brain–Machine Interface to Navigate a Mobile Robot in a Planar Workspace: Enabling Humans to Fly Simulated Aircraft With EEG,� IEEE Transactions On Neural Systems And Rehabilitation Engineering, vol. 21, no. 2, pp. 306-318, 2013. crossref
G. G. Gentiletti, J. G. Gebhart, R. C. Acevedo, O. Yá˜nez-Suárez, and V. Medina-Ba˜nuelos,“Command of a simulated wheelchair on a virtual environment using a brain-computer interface,� IRBM, vol. 30, pp. 218–225, 2009. crossref
H.-J. Hwang, K. Kwon, and C.-H. Im, “Neurofeedback-based motor imagery training for brain–computer interface (BCI),� Journal of Neuroscience Methods, vol. 179, pp. 150–156, 2009. crossref
G. Geetha, and S. N. Geethalakshmi, “Artifact Removal from EEG using Spatially Constrained Independent Component Analysis and Wavelet Denoising with Otsu’s Thresholding Technique,� Procedia Engineering, vol. 30, pp. 1064 –1071, 2012. crossref
S. O’Regan, S. Faul, and W. Marnane, “Automatic detection of EEG artefacts arising from head movements using EEG and gyroscope signals,� Medical Engineering & Physics, vol. 35, pp. 867– 874, 2013. crossref
S. O’Regan, and W. Marnane, “Multimodal detection of head-movement artefacts in EEG,� Journal of Neuroscience Methods, vol. 218, pp. 110–120, 2013. crossref
T. Liu, and D. Yao, “Removal of the ocular artifacts from EEG data using a cascaded spatio-temporal processing,� computer methods and programs in biomedicine, vol. 83, pp. 95–103, 2006. crossref
V. Lawhern, W. D. Hairston, K. McDowell, M. Westerfield, and K. Robbins, “Detection and classification of subject-generated artifacts in EEG signals using autoregressive models,� Journal of Neuroscience Methods, vol. 208, pp. 181–189, 2012. crossref
Soetraprawata, and A. Turnip, "Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification," Journal of Mechatronics, Electrical Power and Vehicular Technology, vol. 4, no. 1, pp. 1-8, 2013. crossref
H.-A. T. Nguyen, J. Musson, F. Li, W. Wang, G. Zhang, R. Xu, C. Richey, T. Schnell, F. D. McKenzie, and J. Li, “EOG artifact removal using a wavelet neural network,� Neurocomputing, vol. 97, pp. 374–389, 2012. crossref
A. Turnip, and D. Soetraprawata, "Performance of EEG-P300 Classification Using Backpropagation Neural Networks," Journal of Mechatronics, Electrical Power and Vehicular Technology, vol. 4, no. 2, pp. 81-88, 2013. crossref
Turnip, S. S. Hutagalung, J. Pardede, and, D. Soetraprawata, "P300 detection using multilayer neural networks based adaptive feature extraction method,"International Journal of Brain and Cognitive Sciences, vol. 2, no. 5, pp. 63-75, 2013. crossref
Turnip, and K.-S. Hong, "Classifying mental activities from EEG-P300 signals using adaptive neural network," International Journal of Innovative Computing, Information and Control (IJICIC), vol. 8, no. 7, 2012.
Turnip, K.-S. Hong, and M.-Y. Jeong, "Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis," Journal of BioMedical Engineering OnLine, 10:83, 2011. crossref
Article Metrics
Metrics powered by PLOS ALM
Refbacks
- There are currently no refbacks.
Copyright (c)
Cited-By
Abstracted/Indexed by:
About MEV | Journal Policies | Author | Information |
1.png){kind=small}
MEV is published by National Research and Innovation Agency (BRIN) - formerly LIPI
Copyright of BRIN (e-ISSN:2088-6985, p-ISSN:2087-3379). View MEV Stats
Email this article 
