Otwarty dostęp

Towards Effective Music Therapy for Mental Health Care Using Machine Learning Tools: Human Affective Reasoning and Music Genres


Zacytuj

[1] A. Bardekar and A. A. Gurjar, Study of Indian Classical Ragas Structure and its Influence on Human Body for Music Therapy, in 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), 2016, pp. 119-123: IEEE.10.1109/ICATCCT.2016.7911976Search in Google Scholar

[2] C. L. Baldwin and B. A. Lewis, Positive valence music restores executive control over sustained attention, PLOS ONE, vol. 12, no. 11, p. e0186231, 2017.10.1371/journal.pone.0186231569065629145395Search in Google Scholar

[3] L. Harmat, J. Takács, and R. Bodizs, Music improves sleep quality in students, Journal of advanced nursing, vol. 62, no. 3, pp. 327-335, 2008.10.1111/j.1365-2648.2008.04602.x18426457Search in Google Scholar

[4] G. Coppola et al., Mozart’s music in children with drug-refractory epileptic encephalopathies, Epilepsy & Behavior, vol. 50, pp. 18-22, 2015.10.1016/j.yebeh.2015.05.03826093514Search in Google Scholar

[5] M. Z. Hossain, Observer’s galvanic skin response for discriminating real from fake smiles, 2016.10.1145/3152771.3156179Search in Google Scholar

[6] L. Chen, T. Gedeon, M. Z. Hossain, and S. Caldwell, Are you really angry?: detecting emotion veracity as a proposed tool for interaction, presented at the Proceedings of the 29th Australian Conference on Computer-Human Interaction, Brisbane, Queensland, Australia, 2017.10.1145/3152771.3156147Search in Google Scholar

[7] J. A. Healey and R. W. Picard, Detecting stress during real-world driving tasks using physiological sensors, IEEE Transactions on intelligent transportation systems, vol. 6, no. 2, pp. 156-166, 2005.10.1109/TITS.2005.848368Search in Google Scholar

[8] Y. Nagai, L. H. Goldstein, P. B. Fenwick, and M. R. Trimble, Clinical efficacy of galvanic skin response biofeedback training in reducing seizures in adult epilepsy: a preliminary randomized controlled study, Epilepsy & Behavior, vol. 5, no. 2, pp. 216-223, 2004.10.1016/j.yebeh.2003.12.00315123023Search in Google Scholar

[9] L. Harrison and P. Loui, Thrills, chills, frissons, and skin orgasms: toward an integrative model of transcendent psychophysiological experiences in music, Frontiers in psychology, vol. 5, p. 790, 2014.10.3389/fpsyg.2014.00790410793725101043Search in Google Scholar

[10] D. Huron and E. Margulis, Musical Expectancy and Thrills, Handbook of Music and Emotion: Theory, Research, Applications, pp. 575-604, 07/29 2011.10.1093/acprof:oso/9780199230143.003.0021Search in Google Scholar

[11] M. Guhn, A. Hamm, and M. Zentner, Physiological and musico-acoustic correlates of the chill response, Music Perception: An Interdisciplinary Journal, vol. 24, no. 5, pp. 473-484, 2007.10.1525/mp.2007.24.5.473Search in Google Scholar

[12] D. G. Craig, An exploratory study of physiological changes during “chills” induced by music, Musicae scientiae, vol. 9, no. 2, pp. 273-287, 2005.10.1177/102986490500900207Search in Google Scholar

[13] K. H. Kim, S. W. Bang, and S. R. Kim, Emotion recognition system using short-term monitoring of physiological signals, Medical and biological engineering and computing, vol. 42, no. 3, pp. 419-427, 2004.10.1007/BF02344719Search in Google Scholar

[14] M. Z. Hossain, T. Gedeon, and R. Sankaranarayana, Using temporal features of observers’ physiological measures to distinguish between genuine and fake smiles, IEEE Transactions on Affective Computing, pp. 1-1, 2018.Search in Google Scholar

[15] A. Haag, S. Goronzy, P. Schaich, and J. Williams, Emotion recognition using bio-sensors: First steps towards an automatic system, in Tutorial and research workshop on affective dialogue systems, 2004, pp. 36-48: Springer.10.1007/978-3-540-24842-2_4Search in Google Scholar

[16] J. S. Rahman, T. Gedeon, S. Caldwell, R. Jones, M. Z. Hossain, and X. Zhu, Melodious Micro-frissons: Detecting Music Genres from Skin Response, in International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 2019: IEEE.10.1109/IJCNN.2019.8852318Search in Google Scholar

[17] J. R. Hughes and J. J. Fino, The Mozart effect: distinctive aspects of the music—a clue to brain coding?, Clinical Electroencephalography, vol. 31, no. 2, pp. 94-103, 2000.10.1177/155005940003100208Search in Google Scholar

[18] L. C. Lin et al., Parasympathetic activation is involved in reducing epileptiform discharges when listening to Mozart music, Clin Neurophysiol, vol. 124, no. 8, pp. 1528-35, Aug 2013.Search in Google Scholar

[19] R. McCraty, The effects of different types of music on mood, tension, and mental clarity.”Search in Google Scholar

[20] Youtube. (2016). Gamma Brain Energizer - 40 Hz - Clean Mental Energy - Focus Music - Binaural Beats. Avail able: https://www.youtube.com/watch?v=9wrFk5vuOskSearch in Google Scholar

[21] Youtube. (2017). Serotonin Release Music with Alpha Waves - Binaural Beats Relaxing Music, Happiness Frequency. Available: https://www.youtube.com/watch?v=9TPSs16DwbASearch in Google Scholar

[22] N. Hurless, A. Mekic, S. Pena, E. Humphries, H. Gentry, and D. Nichols, Music genre preference and tempo alter alpha and beta waves in human non-musicians.Search in Google Scholar

[23] Billboard Year End Chart. Available: https://www.billboard.com/charts/year-endSearch in Google Scholar

[24] D. J. Thurman et al., Standards for epidemiologic studies and surveillance of epilepsy, Epilepsia, vol. 52, pp. 2-26, 2011.10.1111/j.1528-1167.2011.03121.xSearch in Google Scholar

[25] Y. Shi, N. Ruiz, R. Taib, E. Choi, and F. Chen, Galvanic skin response (GSR) as an index of cognitive load, in CHI’07 extended abstracts on Human factors in computing systems, 2007, pp. 2651-2656: ACM.10.1145/1240866.1241057Search in Google Scholar

[26] T. Lin, M. Omata, W. Hu, and A. Imamiya, Do physiological data relate to traditional usability indexes?, in Proceedings of the 17th Australia conference on Computer-Human Interaction: Citizens Online: Considerations for Today and the Future, 2005, pp. 1-10: Computer-Human Interaction Special Interest Group (CHISIG) of Australia.Search in Google Scholar

[27] S. Reisman, Measurement of physiological stress, in Bioengineering Conference, 1997., Proceedings of the IEEE 1997 23rd Northeast, 1997, pp. 21-23: IEEE.Search in Google Scholar

[28] R. A. McFarland, Relationship of skin temperature changes to the emotions accompanying music, Biofeedback and Self-regulation, vol. 10, no. 3, pp. 255-267, 1985.10.1007/BF00999346Search in Google Scholar

[29] T. Partala and V. Surakka, Pupil size variation as an indication of affective processing, International journal of human-computer studies, vol. 59, no. 1-2, pp. 185-198, 2003.10.1016/S1071-5819(03)00017-XSearch in Google Scholar

[30] R. S. Larsen and J. Waters, Neuromodulatory correlates of pupil dilation, Frontiers in neural circuits, vol. 12, p. 21, 2018.10.3389/fncir.2018.00021Search in Google Scholar

[31] J. Zhai and A. Barreto, Stress Recognition Using Non-invasive Technology, in FLAIRS Conference, pp. 395-401, 2006.Search in Google Scholar

[32] M. W. Weiss, S. E. Trehub, E. G. Schellenberg, and P. Habashi, Pupils dilate for vocal or familiar music, Journal of Experimental Psychology: Human Perception and Performance, vol. 42, no. 8, p. 1061, 2016.10.1037/xhp0000226Search in Google Scholar

[33] E4 wristband from empatica. Available: https://www.empatica.com/research/e4/Search in Google Scholar

[34] The Eye Tribe. Available: http://theeyetribe.com/about/index.htmlSearch in Google Scholar

[35] J. L. Walker, Subjective reactions to music and brainwave rhythms, Physiological Psychology, vol. 5, no. 4, pp. 483-489, 1977.10.3758/BF03337859Search in Google Scholar

[36] D. F. Alwin, Feeling thermometers versus 7-point scales: Which are better?, Sociological Methods & Research, vol. 25, no. 3, pp. 318-340, 1997.10.1177/0049124197025003003Search in Google Scholar

[37] J. A. Russell, A circumplex model of affect, Journal of personality and social psychology, vol. 39, no. 6, p. 1161, 1980.10.1037/h0077714Search in Google Scholar

[38] J. Kim and E. Andre, Emotion recognition based on physiological changes in music listening, IEEE Trans Pattern Anal Mach Intell, vol. 30, no. 12, pp. 2067-83, Dec 2008.Search in Google Scholar

[39] S. Jerritta, M. Murugappan, R. Nagarajan, and K. Wan, Physiological signals based human emotion recognition: a review, in 2011 IEEE 7th International Colloquium on Signal Processing and its Applications, 2011, pp. 410-415: IEEE.10.1109/CSPA.2011.5759912Search in Google Scholar

[40] R. W. Picard, E. Vyzas, and J. Healey, Toward machine emotional intelligence: Analysis of affective physiological state, IEEE transactions on pattern analysis and machine intelligence, vol. 23, no. 10, pp. 1175-1191, 2001.Search in Google Scholar

[41] U. R. Acharya et al., Characterization of focal EEG signals: a review, Future Generation Computer Systems, vol. 91, pp. 290-299, 2019.10.1016/j.future.2018.08.044Search in Google Scholar

[41] R. Chowdhury, M. Reaz, M. Ali, A. Bakar, K. Chellappan, and T. Chang, Surface electromyography signal processing and classification techniques, Sensors, vol. 13, no. 9, pp. 12431-12466, 2013.Search in Google Scholar

[43] C. D. Katsis, N. Katertsidis, G. Ganiatsas, and D. I. Fotiadis, Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol. 38, no. 3, pp. 502-512, 2008.10.1109/TSMCA.2008.918624Search in Google Scholar

[44] T. Triwiyanto, O. Wahyunggoro, H. A. Nugroho, and H. Herianto, An investigation into time domain features of surface electromyography to estimate the elbow joint angle, Advances in Electrical and Electronic Engineering, vol. 15, no. 3, pp. 448-458, 2017.10.15598/aeee.v15i3.2177Search in Google Scholar

[45] R. Kohavi and G. H. John, Wrappers for feature subset selection, Artificial intelligence, vol. 97, no. 1-2, pp. 273-324, 1997.10.1016/S0004-3702(97)00043-XSearch in Google Scholar

[46] J. Pohjalainen, O. Räsänen, and S. Kadioglu, Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits, Computer Speech & Language, vol. 29, no. 1, pp. 145-171, 2015.10.1016/j.csl.2013.11.004Search in Google Scholar

[47] J. Yang and V. Honavar, Feature subset selection using a genetic algorithm, in Feature extraction, construction and selection: Springer, 1998, pp. 117-136.10.1007/978-1-4615-5725-8_8Search in Google Scholar

[48] F. J. Valverde-Albacete and C. Peláez-Moreno, 100% classification accuracy considered harmful: The normalized information transfer factor explains the accuracy paradox, PloS one, vol. 9, no. 1, p. e84217, 2014.10.1371/journal.pone.0084217388839124427282Search in Google Scholar

[49] K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.10.1109/CVPR.2016.90Search in Google Scholar

[50] M. G. N. Bos, P. Jentgens, T. Beckers, and M. Kindt, Psychophysiological response patterns to affective film stimuli, (in eng), PloS one, vol. 8, no. 4, pp. e62661-e62661, 2013.10.1371/journal.pone.0062661363996223646134Search in Google Scholar

[51] S. Jerritta, M. Murugappan, K. Wan, and S. Yaacob, Emotion Detection from QRS Complex of ECG Signals Using Hurst Exponent for Different Age Groups, in 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, 2013, pp. 849-854.10.1109/ACII.2013.159Search in Google Scholar

[52] J. S. Rahman, T. Gedeon, S. Caldwell and R. Jones, Brain Melody Informatics: Analysing Effects of Music on Brainwave Patterns, in International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom, 2020: IEEE.10.1109/IJCNN48605.2020.9207392Search in Google Scholar

eISSN:
2083-2567
Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Computer Sciences, Artificial Intelligence, Databases and Data Mining