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Distinguishing Violinists and Pianists Based on Their Brain Signals

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Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation (ICANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11727))

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Abstract

Many studies in neuropsychology have highlighted that expert musicians, who started learning music in childhood, present structural differences in their brains with respect to non-musicians. This indicates that early music learning affects the development of the brain. Also, musicians’ neuronal activity is different depending on the played instrument and on the expertise. This difference can be analysed by processing electroencephalographic (EEG) signals through Artificial Intelligence models. This paper explores the feasibility to build an automatic model that distinguishes violinists from pianists based only on their brain signals. To this aim, EEG signals of violinists and pianists are recorded while they play classical music pieces and an Artificial Neural Network is trained through a cloud computing platform to build a binary classifier of segments of these signals. Our model has the best classification performance on 20 seconds EEG segments, but this performance depends on the involved musicians’ expertise. Also, the brain signals of a cellist are demonstrated to be more similar to violinists’ signals than to pianists’ signals. In summary, this paper demonstrates that distinctive information is present in the two types of musicians’ brain signals, and that this information can be detected even by an automatic model working with a basic EEG equipment.

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Acknowledgements

The authors wish to thank the Auditorium della Compagnia association for hosting the experiment, in particular Alessandro Lipari for the technical help, and the musicians for accepting their involvement in the experiment.

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Authors and Affiliations

  1. Istituto di Scienza e Tecnologie dell’Informazione A. Faedo, Pisa, Italy

    Gianpaolo Coro & Giulio Masetti

  2. Auditorium della Compagnia Montecastelli, Castelnuovo di Val di Cecina, Pisa, Italy

    Philipp Bonhoeffer & Michael Betcher

Authors
  1. Gianpaolo Coro
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  2. Giulio Masetti
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  3. Philipp Bonhoeffer
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  4. Michael Betcher
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Corresponding author

Correspondence to Gianpaolo Coro .

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Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Fabian Theis

Ethics declarations

The authors declare no conflict of interest. This research has been conducted in compliance with the Helsinki Declaration for Ethical Principles for Medical Research Involving Human Subjects, under the responsibility of the authors and of the Auditorium della Compagnia Montecastelli association. Consent was provided by the participants and, in the case of minors, by their parents who were present during the experiments. Since this is not a medical research and it is not an invasive experiment, we did not ask an official ethics committee to formally approve the experiment.

Supplementary Material

A sample of the collected corpus is downloadable through the D4Science e-Infrastructure at https://data.d4science.org/shub/E_dE9JVUw4Z1dFeWtIUG9xMnk0R09PVlNzU28rdnlvYTBEMDlnNkczNWlxdXRtNjA4YWl3b2RPZHNxdTlVN3BxZg==

The services used for this research are freely usable after registration on the D4Science cloud computing platform at the following links:

https://services.d4science.org/group/scalabledatamining/data-miner?OperatorId=org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.transducerers.FEED_FORWARD_NEURAL_NETWORK_REGRESSOR

https://services.d4science.org/group/scalabledatamining/data-miner?OperatorId=org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.transducerers.FEED_FORWARD_NEURAL_NETWORK_TRAINER

The source code is available at http://svn.research-infrastructures.eu/public/d4science/gcube/trunk/data-analysis/EcologicalEngine/src/main/java/org/gcube/dataanalysis/ecoengine/models/

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Coro, G., Masetti, G., Bonhoeffer, P., Betcher, M. (2019). Distinguishing Violinists and Pianists Based on Their Brain Signals. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. ICANN 2019. Lecture Notes in Computer Science(), vol 11727. Springer, Cham. https://doi.org/10.1007/978-3-030-30487-4_11

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  • DOI: https://doi.org/10.1007/978-3-030-30487-4_11

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