Classification of EEG Signal for Body Earthing Application
M. Ghaly and D. Teplitz, “The biologic effects of grounding the human body during sleep as measured by cortisol levels and subjective reporting of sleep, pain, and stress,” J. Altern. Complement. Med. vol. 10, pp. 767–776, 2004.
G. Chevalier, K. Mori, and J. L. Oschman, “The effect of earthing (grounding) on human physiology,” Eur. Biol. Bioelectromagn. vol.3, pp. 600–621, 2006.
P. M. Sovilj, S. S. Milovancev, and V. Vujicic, “Digital Stochastic Measurement of a Nonstationary Signal With an Example of EEG Signal Measurement” IEEE Trans. On Instrumentation and Measurement, vol. 60, pp. 3230–3232, 2011.
M. Mustafa, S. K. Huong, N. Sulaiman, R. Samad, N. R. H. Abdullah, N. S. Pakheri and M. N Taib, “Initial result of body earthing on student stress performance,” in Int. Conf. BioSignal Anal. Process. Syst. ICBAPS 2015, pp.129–133.
D. S. Benitez, S. Toscano, and A. Silva, “On the use of the Emotiv EPOC neuroheadset as a low cost alternative for EEG signal acquisition,” in 2016 IEEE Colomb. Conf. Commun. Comput. COLCOM, pp. 1-6.
A. Zabidi, W. Mansor, Y. K. Lee, and C. W. N. F. C. W. Fadzal, “Shorttime Fourier Transform analysis of EEG signal generated during imagined writing,” in Syst. Eng. Technol. (ICSET) Int. Conf. no. 2. pp. 1–4.
M. Snajdarova, B. Babusiak, and I. Cap, “Interactive Tool for Analysis of EEG Signal,” ELEKTRO. pp.583-588, 2016.
A. T. Tzallas, M. G. Tsipouras, and D. I. Fotiadis, “Epileptic seizure detection in EEGs using time-frequency analysis,” IEEE Trans. Inf. Technol. Biomed. vol. 13, pp. 703–710, 2009.
M. Akin,”Comparison of wavelet transform and FFT methods in the analysis of EEG signals,” J. Med. Syst. vol. 26, pp. 241–247, 2002.
M. K. Kiymik, I. Güler, A. Dizibüyük, and M. Akin, “ Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application,” Comput. Biol. Med. vol. 35, pp. 603–616, 2005.
A. Subasi and E. Erçelebi , “Classification of EEG signals using neural network and logistic regression,” Comput. Methods Programs Biomed. vol. 78, pp. 87–99, 2005.
A. T. Tzallas, M. G. Tsipouras, and D. I. Fotiadis, “Automatic seizure detection based on time-frequency analysis and artificial neural networks,” Comput. Intell. Neurosci. PMC2246039, 2007.
R. F. Navea and E. Dadios, “Classification of wavelet-denoised musical tone stimulated EEG signals using artificial neural networks,” in 2017 IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, pp. 1503–1508.
I. Belakhdar, W. Kaaniche, R. Djmel, and B. Ouni, “A comparison between ANN and SVM classifier for drowsiness detection based on single EEG channel,” in 2nd Int. Conf. Adv. Technol. Signal Image Process. ATSIP 2016, pp. 443–446.
D. Garrett, D. A. Peterson, C. W. Anderson, and M. H. Thaut. 2003, “Comparison of linear, nonlinear, and feature selection methods for EEG signal classification,” IEEE Trans. Neural Syst. Rehabil. Eng. vol. 11, pp. 141–144, 2003.
I. Yesilyurt, “The application of the conditional moments analysis to gearbox fault detection - A comparative study using the spectrogram and scalogram,” NDT E Int. vol. 37, pp. 309–320, 2004.
M. Mustafa, M. N. Taib, Z. H. Murat, N. Sulaiman, and S. A. M. Aris, “Classification of EEG spectrogram image with ANN approach for brainwave balancing application,” Int. J. Simul. Syst. Sci. Technol. vol. 12, pp. 29–36, 2011.
N. Hazarika, J. Z. Chen, A. C. Tsoi and A. Sergejew, “Classification of EEG signals using the wavelet transform,” Signal Processing. vol. 59, pp. 61-72.
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