A multivariate statistical analysis of EEG signals for differentiation of musicians and non-musicians
It is possible to reveal whether a subject received musical training through the neural activation patterns induced in response to music listening. We are particularly interested in analyzing the brain data on a global level, considering its activity registered in electroencephalogram electrodes signals. Our experiments results, with 13 musicians and 12 non-musicians who listened the song Hungarian Dance No 5 from Johannes Brahms, have shown that is possible to differentiate musicians and non-musicians with high classification accuracy (88%). Given this multivariate statistical framework, it has also been possible to highlight the most expressive and discriminant changes in the participants brain according to the acoustic features extracted from the audio.
Abrams, D. A., Ryali, S., Chen, T., Chordia, P., Khouzam, A., Levitin, D. J., and Menon, V. (2013). Inter-subject synchronization of brain response during natural music listening. European Journal of Neuroscience, 37:1458–1469.
Alluri, V., Toiviainen, P., Jaaskelainen, I. P., Glerean, E., Sams, M., and Brattico, E. (2012). Large-scale brain networks emerge from dynamics processing of musical timbre, key and rhythm. NeuroImage, 59:3677–3689.
Alluri, V., Toiviainen, P., Lund, T. E., Wallentin, M., Vuust, P., Nandi, A. K., Ristaniemi, T., and Brattico, E. (2013). From vivaldi to beatles and back: Predicting lateralized brain responses to music. NeuroImage, 83:627–636.
Baird, A. and Samson, S. (2015). Music and dementia. Progress in brain research, 217:207–235.
Bennet, A. and Bennet, D. (2008). The human knowledge system: music and brain coherence. Information and knowledge management systems, 38(3).
Fukunaga, K. (1994). Introduction to statistical pattern recognition. Morgan Kaufmann, 2nd edition.
Gregori, I. R. S., Sanches, I., and Thomaz, C. E. (2017). Clutch judder classification and prediction: A multivariate statistical analysis based on torque signals. IEEE transactions on industrial electronics, 64:4287–4295.
Lartillot, O. (2014). MIRtoolbox 1.6.1 Users Manual. Department of Architecture, Design and Media Technology, 1st edition.
Lerch, A. (2012). An Introduction to audio content analysis. Applications in signal processing and music informatics. IEEE Press, 1st edition.
Marcuse, L. V., Fields, M. C., and Yoo, J. (2016). Rowan’s Primer of EEG. Elsevier, 2nd edition.
Markovic, A., Kuhnis, J., and Janche, L. (2017). Task context influences brain activation during music listening. Frontiers in Human Neuroscience, 1.
Peretz, I. and Zatorre, R. J. (2004). Brain organization for music processing. Annual Reviews Psychology, 56:89 –114.
Poikonen, H., Alluri, V., Brattico, E., Lartillot, O., Tervaniemi, M., and Huotilainen, M. (2016a). Event-related brain responses while listening to entire pieces of music. Neuroscience, pages 58–73.
Poikonen, H., Toiviainen, P., and Tervaniemi, M. (2016b). Early auditory processing in musicians and dancers during a contemporary dance piece. Scientific Reports - Nature.
Rigoulot, S., Pell, M. D., and Armony, J. L. (2015). Time course of the influence of musical expertise on the processing of vocal and musical sounds. Neurocience, 290.
Saari, P., Burunat, I., Brattico, E., and Toiviainen, P. (2018). Decoding musical training from dynamic processing of musical features in the brain. Scientific Report, 708:1–12.
Sato, J. R., Thomaz, C. E., Cardoso, E. F., Fujita, A., Martin, M. d. G. M., and Amaro, E. J. (2008). Hyperplane navigation: A method to set individual scores in fmri group datasets. NeuroImage, 42:1472–1480.
Sheng-Fu, L., Tsung-Hao, H., Wei-Hong, C., and Kuei-Ju, L. (2011). Classification of eeg signals from musicians and non-musicians by neural networks. World Congress on Intelligent Control and Automation, IEEE, 8:865–869.
Virtala, P., Huotilainen, M., Partanen, E., and Tervaniemi, M. (2014). Musicianship facilitates the processing of western music chords - an erp and behavioral study. Neuropsychologia, 61:247–258.
Vusst, P., Brattico, E., Sppanen, M., Naatanen, R., and Tervaniemi, M. (2014). The sound of music: Differentiating musicians using a fast, musical multi-feature mismatch negativity paradigm. Neuropsychologia, 61:1432–1443.
Xavier, I., Pereira, M., Giraldi, G., Gibson, S., Solomon, C., Rueckert, D., Gillies, D., and Thomaz, C. (2015). A photo-realistic generator of most expressive and discriminant changes in 2d face images. International conference on emerging security technologies, pages 80–85.