Keywords
How to Cite
Abstract
Purpose of this research is to extract features associated with human brain signal related to electroencephalographic measurements and classification of extracted EEG signals to the relevant the brain region. EEG brain signals from 14 electrodes placed on the human scalp is recorded non-invasively using Emotiv EPOC / EPOC+:Scientific contextual EEG system with a sampling rate of 128 Hz. EEG data of human brain functions related to evoked motor imagery tasks consisting of two different classes of activities, namely imagination of right arm-movement i.e. arm down (termed here as PUSH) and arm up (termed here as PULL) for three healthy subjects is recorded. After pre-processing for noise and artifacts removal, the EEG signals associated with investigated evoked activities are extracted using Independent Component Analysis (ICA). The results obtained show good contrast plots for the extracted brain signals recorded on F7, FC5 and FC6 electrodes, decomposed on independent components, namely IC1, IC4, IC5, IC6. Classification of extracted features is mapped on to the motor imagery parts of human brain. The algorithm based on independent component analysis gives good results for feature extraction corresponding to evoked signals. Power spectra are also determined for the extracted independent components.
References
Sanei S, Chambers JA. EEG signal processing, John Wiley & Sons Ltd. 2007.
Pfurtschellera G, Lopes FH, Silva Da. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology 1999.
Schneider S, Rice DR. Neurologic manifestations of childhood hysteria. J Pediatr 1979; 94: 153. https://doi.org/10.1016/S0022-3476(79)80385-6
Young BG, Jordan KG, Doig GS. An assessment of non-conclusive seizures in the intensive care unit using continuous EEG monitoring. Neurology 1996; 47: 83-89. https://doi.org/10.1212/WNL.47.1.83
Schalk G, McFarland DJ, Hinterberger T, Birbaumer N, Wolpaw JR. BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Transactions on Biomedical Engineering 2004.
Fabiani G, McFarland DJ, Wolpaw JR, Pfurtscheller G. Conversion of eeg activity into cursor movement by brain-computer interface (bci). IEEE Trans. on Neural Systems and Rehabilitation Eng 2004.
Kiymik KM, Akin M, Subasi A. Automatic recognition of alertness level by using wavelet transform and artificial neural network. Journal of Neuroscience Methods 2004.
Jung TP, Makeig S, Stensmo M, Sejnowski TJ. Estimating Alertness from EEG power spectrum. IEEE Transaction on Biomedical Engineering 1997.
Ting W, Guo-Zheng Y, Bang-Hua Y, Hong S. EEG feature extraction based on wavelet packet decomposition for brain computer interface. Measurement 2008; 41: 618-625. https://doi.org/10.1016/j.measurement.2007.07.007
Aowlad Hossain ABM, Rahman W, Riheen MA. Left and Right Hand Movements EEG Signals Classification Using Wavelet Transform and Probabilistic Neural Network. International Journal of Electrical and Computer Engineering (IJECE) 2015; 5(1): 92-101.
Liu AK, Dale AM, Belliveau JW. Monte Carlo Simulation Studies of EEG and MEG Localization Accuracy. Human Brain Mapping 2002; 16: 47-62. https://doi.org/10.1002/hbm.10024
Subasi A, Erc¸elebi E. Classification of EEG signals using neural network and logistic regression. Computer Methods and Programs in Biomedicine 2005; 78(2): 87-99. https://doi.org/10.1016/j.cmpb.2004.10.009
Subasi A. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications 2007; 32(4): 1084-1093. https://doi.org/10.1016/j.eswa.2006.02.005
Jasper H, Proctor LD. Reticular Formation of the Brain 1958.
Cvetkovic D, Ubeyli ED, Cosic I. Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: a pilot study. Digital Signal Processing 2008; 18(5): 861-874. https://doi.org/10.1016/j.dsp.2007.05.009
Kordylewski H, Graupe D, Liu K. A novel large-memory neural network as an aid in medical diagnosis applications. IEEE Transactions on Information Technology in Biomedicine 2001; 5(3): 202-209. https://doi.org/10.1109/4233.945291
Güler I, Übeyli ED. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. Journal of Neuroscience Methods 2005; 148(2): 113-121. https://doi.org/10.1016/j.jneumeth.2005.04.013
Übeyli ED. Wavelet/mixture of experts network structure for EEG signals classification. Expert Systems with Applications 2008; 34(3): 1954-1962. https://doi.org/10.1016/j.eswa.2007.02.006
Hyvärinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw 1999; 10: 626-634. https://doi.org/10.1109/72.761722
Hyvärinen A, Karhunen J, Oja E. Independent Component Analysis. Wiley 2001. https://doi.org/10.1002/0471221317
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2017 Journal of Basic & Applied Sciences