Real Time Acquisition and Psychoacoustic Analysis of Brain Wave
Psychoacoustics has become a potential area of research due to the growing interest of both laypersons and medical and mental health professionals. Non invasive brain computer interface like Electroencephalography (EEG) is widely being used in this field. An attempt has been made in this paper to examine the response of EEG signals to acoustic stimuli further analyzing the brain electrical activity. The real time EEG is acquired for 6 participants using a cost effective and portable EMOTIV EEG neuro headset. EEG data analysis is further done using EMOTIV test bench, EDF browser and EEGLAB (MATLAB Tool) application software platforms. Spectral analysis of acquired neural signals (AF3 channel) using these software platforms are clearly indicative of increased brain activity in various bands. The inferences drawn from such an analysis have significant correlation with subject’s subjective reporting of the experiences. The results suggest that the methodology adopted can further be used to assist patients with sleeping and depressive disorders.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1091192Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
 Wiebke Trost; Thomas Ethofer; Marcel Zentner; Patrik Vuilleumier. Mapping Aesthetic Musical Emotions in the brain. Cerebral Cortex Advance Access. Laboratory of Behavioral Neurology and Imaging of Cognition, Department of Neuroscience, Medical School, University of Geneva. Switzerland 15 December, 2011
 Seiji Nishifuji; Masahiro Sato; Daisuke Maino; Shogo Tanaka. Effect of Acoustic Stimuli and Mental Task on Alpha, Beta and Gamma Rhythms in Brain Wave. In Proceedings of SICE Annual Conference. Taipei. 18-21 August, 2010.
 Anibal Cotrina Atencio; Teodiano Freire Bastos Filho; Andr´e Ferreira; Alessandro Botti Benevides. Evaluation of ERD/ERS Caused by Unpleasant Sounds to be applied in BCIs. PPGEE. Federal University of Espirito Santo. 2011.
 Stefan Koelsch. Toward a Neural Basis of Music Perception- A Review and Updated Model. Frontiers in Psychology. Berlin, Germany. 9 June 2011.
 Ajay Anil Gurjar; Siddharth A. Ladhake; Ajay P. Thakare. Analysis Of Acoustic of ‘OM’ Chant to Study It’s Effect on Nervous System. IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.1.India.January 2009.
 Stigsby B; J. C. Rodenburg; H. B Moth. Electroencephalographic Findings during Mantra Meditation (Transcendental Meditation): A Controlled Quantitative Study of Experienced Mediators.
 Zentner M. Homer’s Prophecy: An Essay on Music’s Primary Emotions. 2010 Music Anal. 29:102—125.
 Scherer K, Zentner M. Emotional Effects of Music: Production Rules. In: Juslin P. N, Sloboda J, editors. Music and Emotion: Theory and Research. 2001 Oxford: Oxford University Press. p. 361--392.
 Winkler, I; Kushnerenko, E; Horváth, J; Ceponiene, R;,Fellman, V., Huotilainen, M., et al. Newborn Infants Can Organize the Auditory World. Proceedings of the National Academy of Sciences, U.S.A.2003. 100, 11812–11815.2262 Journal
 Trehub, S. E., Bull, D., & Thorpe, L. A. Infants’ perception of melodies: The role of Melodic Contour. Child Development. 1984. 55, 821–830.
 L. I. Aftanas and S. V. Pavlov. Trait Anxiety Impact on Posterior Activation, Asymmetries at Rest and During Evoked Negative Emotions: EEG investigation. International J. Psychophysiology. 2005. vol. 55, pp. 85- 94, 2005.
 Oya, H. Kawasaki; M. A. Howard III; Ralf Adolphs. Electrophysiological Responses in the Human Amygdale Discriminate Emotion Categories of Complex Visual Stimuli. J. Neuroscience. 2002 vol. 22, pp.9502 – 9512.
 S. Kumar; H. M. Forster; P. Bailey; T. D. Griffiths. Mapping Unpleasantness of Sounds to Their Auditory Representation.. J Acoust Soc Am. 2008 vol. 124, no. 6, pp. 3810–7.
 Jinghai, Y.; Derong, J.; Jianfeng, H. Design and Application of Brain-Computer Interface Web Browser Based on VEP. In Proceedings of the International Conference on Future BioMedical Information Engineering (FBIE’09), Sanya, China, 13–14 December 2009; pp. 77–80.
 Zhu, D.; Bieger, J.; Garcia Molina, G.; Aarts, R.M. A Survey of Stimulation Methods Used in SSVEP-Based BCIs. Comput. Intell. Neurosci. 2010, doi: 10.1155/2010/702357.
 Guangyu, B.; Xiaorong, G.; Yijun, W.; Bo, H.; Shangkai, G. VEP-Based Brain-Computer Interfaces: Time, Frequency, and Code Modulations
[Research Frontier]. IEEE Comput. Intell. Mag. 2009, 4, 22–26.
 Lee, P.; Hsieh, J.; Wu, C.; Shyu, K.; Wu, Y. Brain Computer Interface Using Flash Onset and Offset Visual Evoked Potentials. Clin. Neurophysiol. 2008, 119, 605–616.
 Allison, B. Z.; McFarland, D. J.; Schalk, G.; Zheng, S. D.; Jackson, M.M.; Wolpaw, J. R. Towards an Independent Brain-Computer Interface Using Steady State Visual Evoked Potentials. Clin. Neurophysiol. 2008, 119, 399–408.
 Dan, Z.; Alexander, M.; Xiaorong, G.; Bo, H.; Andreas, K.E.; Shangkai, G. An Independent Brain-Computer Interface Using Covert Non-Spatial Visual Selective Attention. J. Neural Eng. 2010, 7, 016010.
 Wu, Z.; Lai, Y.; Xia, Y.; Wu, D.; Yao, D. Stimulator Selection in SSVEP-Based BCI. Med. Eng. Phys. 2008, 30, 1079–1088.