Abstract

The activity and the ability of brain to maintain the state of calmness in individuals practicing meditation has been a subject of research from long time. The aim of the study here is to prove that the meditation aids in retaining the state of calmness of brain. A MATLAB based multifaceted framework is developed for analyzing the dataset of brain EEG of people practicing meditation. The proposed method performs the processing of 32 electrode EEG data and denoises the signal in time series. The plotting of data followed by PSD analysis and FFT transform of the signal to analyze the data in frequency domain for examining each frequency band. The comparison is done using the L2 norm. The ICWT is later found to analyze the data and calculate for Modulus and angle of the EEG signal. The statistical analysis in time and frequency domain is use to study the effect of meditation on focused attention and retaining of same in meditating and non-meditating brains.

Keywords:

Electroencephalograph (EEG), Inverse Coefficient Wavelet Transform (ICWT), L2 norm, meditation, Power Spectral Density (PSD),

References

1. Cahn, B.R. and J. Polich, 2006. Meditation states and traits: EEG, ERP and neuroimaging studies. Psycol. Bull., 132(2): 180-211.
   CrossRef    PMid:16536641    
2. Carsten, S., M. Morten, W. Ole and K.H. Lars, 2010. SOFOMORE: Combined EEG Source and Forward Model Reconstruction. A White Paper by Technical University of Denmark. Department of Informatics and Mathematical Modeling. Richard Petersens Plads, 2800 Lyngby, Denmark, 1: 1-4.
   
3. Dongwei, C., W. Fang, W. Zhen and H. Li, 2013. EEG based emotion recognition with brain network using independent components analysis and grangers causality. Proceeding of IEEE International Conference on Computer Medical Analysis (ICCMA), 1: 1-6.
   
4. Faber, P.L., D. Lehman, P. Milz, S. Tei and K. Kochi, 2011. Dimensionality of multichannel EEG (omega complexity) during meditation in five traditions. Eur. Psychiat., 26(Suppl. 1): 944-947.
   CrossRef    
5. Guo, H. and C.A. Sunnyvale, 1998. Mapped inverse discrete wavelet transform for data compression. Proceeding of IEEE International Conference on Acoustics, Speech and Signal Processing, 3: 1385-1388.
   
6. Hasenkamp, W., D. Christine, W. Mendelhall and E. Duncan, 2012. Mind wandering and attention during focused meditation: A fine-grained temporal analysis of fluctuating cognitive states. NeuroImage, 59(1): 750-760.
   CrossRef    PMid:21782031    
7. Lehman, D., P.L. Faber, P. Achermann, D. Jeanmonod, L.R. Gianotti and D. Pizzagalli, 2001. Brain sources of EEG gamma frequency during volitionally meditation-induced, altered states of consciousness and experience of the self. Psychiat. Res., 108(2): 111-121.
   CrossRef    
8. Murat, Z.H., M.N. Taib, S. Lias and R.S.S.A. Kadir, 2010. EEG analysis for Brainwave Balancing Index (BBI). Proceeding of 2nd International Conference on Computational Intelligence, Communication System and Networks (CICSYN), 1: 389-393.
   CrossRef    
9. Napoli, A., K. Darvish and I. Obied, 2012. Assessing traumatic brain injuries using EEG power spectral analysis and instantaneous phase. Proceeding of 38th IEEE Annual Northeast Bioengineering Conference, 1: 3-4.
   CrossRef    
10. Nikvand, N., B.H. Rafsanjani and M.A. Khalilzadeh, 2010. ECG signal denoising using noise invalidation. Proceeding of 17th Iranian IEEE Conference on Biomedical Engineering (ICBME), 1: 1-4.
    CrossRef    
11. Oveisi, F., S. Oveisi, A. Efranian and P. Ioannis, 2007. A white paper on nonlinear independent component analysis for EEG based brain computer interface system. Iran Univ., Sci. Technol., 8: 165-180.
    
12. Shijina, P. and K.A. Narayanankutty, 2012. Mathematical modeling for EEG source localization using subspace method. Int. J. Adv. Comput. Eng. Commun. Technol., 1(1): 87-92.
    
13. Tan, L.F., Z. Dienes, A. Jansarai and S.Y. Goh, 2014. Effect of mindfulness meditation on brain computer interface performance. J. Consciousness Cogn., 23: 12-21.
    CrossRef    PMid:24275085    
14. Wang, D., T. Kochiyama, S. Lu and J.W. Long, 2005. Measurement and analysis of electroen cephalograph using directional visual stimuli for brain computer interface. Proceeding of International Conference on Active Media Technology (AMT, 2005), 1: 34-39.
    

Competing interests

The authors have no competing interests.

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