NEUROIMAGING ELECTROENCEPHAHLOGRAPHY (EEG) APPLICATION ON HUMAN ELECTRICAL BRAIN ACTIVITIES DURING MEDITATION AND MUSIC LISTENING
Meditation has a positive impact on the life of human beings. Researchers have scientifically measured and reported the positive impact of meditation from various neuroscience and neuroimaging technology such as encephalogram, fMRI, ECG, etc. Therefore, the neurophysiological EEG was used to identify the brain activities after listening to Zikr and compared it to music listening. Five healthy students as subjects were instructed to listen to the Zikr meditation from Asma Ul-Husna and slow rock music. A low-cost 16 electrodes of Emotiv Epoc was used to record the brain waves activities and determined its location in the brain lobes. Statistical analysis by using FFT from the MATLAB EEGLAB Toolbox software was performed to obtain and analyzed the data. The analysis result showed that the right frontal F8 give out high alpha and beta value thus proving that it involves focus and attention. 85% of the lobes involved give out low beta band during listening to Zikr meditation which indicates the person to focus more during Zikr listening session. Hence, Zikr meditation can lead a person into a calmer state when compared to music listening.
 A. M. Bhatti, M. Majid, S. M. Anwar, and B. Khan, “Human emotion recognition and analysis in response to audio music using brain signals,” Computers in Human Behavior, vol. 65, pp. 267–275, 2016.
 M. Teplan, “Fundamentals of EEG measurement,” Measurement Science Review, vol. 2, no. 2, pp. 1–11, 2002.
 I. Wulandari, A. Huriyati and M. Kep, “Anxiety’s Level of Bantenes Patient’s: The Effect of Dhikr Therapy Before Surgical Procedure,” International Journal of Research in Medical Sciences, vol. 3, no. 1, pp. 36–40, 2015.
 N. I. C. Marzuki, N. H. Mahmood, and N. M. Safri, “Type of music associated with relaxation based on EEG signal analysis,” Jurnal Teknologi , vol. 61, no. 2 , pp. 65–70, 2013.
 B. R. Cahn and J. Polich, “Meditation states and traits: EEG, ERP, and neuroimaging studies,” Psychological Bulletin, vol. 132, no. 2, pp. 180–211, 2006.
 S. M. Alarcao and M. J. Fonseca, “Emotions Recognition Using EEG Signals: A Survey,” IEEE Transactions on Affective Computing, vol. 10, no. 3, pp. 374-393, 2017.
 M. S. Shekha, A. O. Hassan, and S. A. Othman, “Effects of Quran Listening and Music on Electroencephalogram Brain Waves,” The Egyption Journal of Experimental Biology (Zoology), vol. 9, no. 1, pp. 119–121, 2013.
 EMOTIV EPOC. (2014). Brain Computer Interface & Scientific Contextual EEG [Online]. Availabel: https://www.emotiv.com/files/Emotiv-EPOC-Product-Sheet-2014.pdf
 J. D. Jacoby, M. Tory, and J. Tanaka, “Evoked response potential training on a consumer EEG headset,” in IEEE Pacific RIM Conference on Communications,Computers and Signal Processing, Hong Kong, 2015, pp. 485-490.
 Q. Zhang and N. Yoshimine, "A Study on Human Brain Activity during Music Listening using EEG Measurement,” Tama University School of Global Studies Bulletin, vol. 8, pp. 149–157, 2015.
 N. Behboodian and M. Kamal, K. Natsume and T. Kitajima, “Frequency analysis of Brain signals for Biometric Application ”, International Journal of Pure and Applied Mathematics, vol. 118, no. 24, pp. 1–14, 2018.
 A. Delorme and S. Makeig, “EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis,” Journal of Neuroscience Methods, vol. 134, no. 1, pp. 9–21, 2004.
 D. J. Lee, E. Kulubya, P. Goldin, A. Goodarzi, and F. Girgis, “Review of the neural oscillations underlying meditation,” Frontiers in Neuroscience, vol. 12, pp. 1–7, 2018.
 J. Tee and W. Leong, “EEG Extraction for Meditation,” Journal of Engineering Science and Technology, vol. 13, no. 7, pp. 2125–2135, 2018.
 R. Bhoria, P. Singal and D. Verma, “Analysis of Effect of Sound Levels on EEG,” International Journal of Advanced Engineering Research and Technology , vol. 2, no. 2, pp. 121–124, 2012.
 C. W. Quaedflieg, F. T. Smulders, T. Meyer, F. P. M. L. Peeters, H. L. G. J. Merckelbach and T. Smeets, “The validity of individual frontal alpha asymmetry EEG neurofeedback,” Social Cognitive and Affective Neuroscience, vol. 11, no. 1, pp. 33–43, 2015.
 B. Farnsworth. (2019). EEG (Electroencephalography): The Complete Pocket Guide [Online]. Available: https://imotions.com/blog/eeg/
 E. A. Larsen, “Classification of EEG Signals in a Brain- Computer Interface System,” M.S. thesis, Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway, 2011.
Authors who publish with this journal agree to the following terms:
- Authors transfer copyright to the publisher as part of a journal publishing agreement with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after the manuscript is accepted, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).