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Dalla Bella S, Janaqi S, Benoit CE, Farrugia N, Bégel V, Verga L, Harding EE, Kotz SA. Unravelling individual rhythmic abilities using machine learning. Sci Rep 2024; 14:1135. [PMID: 38212632 PMCID: PMC10784578 DOI: 10.1038/s41598-024-51257-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 01/02/2024] [Indexed: 01/13/2024] Open
Abstract
Humans can easily extract the rhythm of a complex sound, like music, and move to its regular beat, like in dance. These abilities are modulated by musical training and vary significantly in untrained individuals. The causes of this variability are multidimensional and typically hard to grasp in single tasks. To date we lack a comprehensive model capturing the rhythmic fingerprints of both musicians and non-musicians. Here we harnessed machine learning to extract a parsimonious model of rhythmic abilities, based on behavioral testing (with perceptual and motor tasks) of individuals with and without formal musical training (n = 79). We demonstrate that variability in rhythmic abilities and their link with formal and informal music experience can be successfully captured by profiles including a minimal set of behavioral measures. These findings highlight that machine learning techniques can be employed successfully to distill profiles of rhythmic abilities, and ultimately shed light on individual variability and its relationship with both formal musical training and informal musical experiences.
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Affiliation(s)
- Simone Dalla Bella
- International Laboratory for Brain, Music, and Sound Research (BRAMS), Montreal, Canada.
- Department of Psychology, University of Montreal, Pavillon Marie-Victorin, CP 6128 Succursale Centre-Ville, Montréal, QC, H3C 3J7, Canada.
- Centre for Research on Brain, Language and Music (CRBLM), Montreal, Canada.
- University of Economics and Human Sciences in Warsaw, Warsaw, Poland.
| | - Stefan Janaqi
- EuroMov Digital Health in Motion, IMT Mines Ales and University of Montpellier, Ales and Montpellier, France
| | - Charles-Etienne Benoit
- Inter-University Laboratory of Human Movement Biology, EA 7424, University Claude Bernard Lyon 1, 69 622, Villeurbanne, France
| | | | | | - Laura Verga
- Comparative Bioacoustics Group, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Department of Neuropsychology & Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. 616, Maastricht, 6200 MD, The Netherlands
| | - Eleanor E Harding
- Department of Otorhinolaryngology/Head and Neck Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Sonja A Kotz
- Department of Neuropsychology & Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. 616, Maastricht, 6200 MD, The Netherlands.
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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Alameda C, Sanabria D, Ciria LF. The brain in flow: A systematic review on the neural basis of the flow state. Cortex 2022; 154:348-364. [DOI: 10.1016/j.cortex.2022.06.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 04/20/2022] [Accepted: 06/13/2022] [Indexed: 11/03/2022]
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Yaghmour M, Sarada P, Roach S, Kadar I, Pesheva Z, Chaari A, Bendriss G. EEG Correlates of Middle Eastern Music Improvisations on the Ney Instrument. Front Psychol 2021; 12:701761. [PMID: 34671287 PMCID: PMC8520950 DOI: 10.3389/fpsyg.2021.701761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 09/14/2021] [Indexed: 11/27/2022] Open
Abstract
The cognitive sciences have witnessed a growing interest in cognitive and neural basis of human creativity. Music improvisations constitute an ideal paradigm to study creativity, but the underlying cognitive processes remain poorly understood. In addition, studies on music improvisations using scales other than the major and minor chords are scarce. Middle Eastern Music is characterized by the additional use of microtones, resulting in a tonal–spatial system called Maqam. No EEG correlates have been proposed yet for the eight most commonly used maqams. The Ney, an end-blown flute that is popular and widely used in the Middle East was used by a professional musician to perform 24 improvisations at low, medium, and high tempos. Using the EMOTIV EPOC+, a 14-channel wireless EEG headset, brainwaves were recorded and quantified before and during improvisations. Pairwise comparisons were calculated using IBM-SPSS and a principal component analysis was used to evaluate the variability between the maqams. A significant increase of low frequency bands theta power and alpha power were observed at the frontal left and temporal left area as well as a significant increase in higher frequency bands beta-high bands and gamma at the right temporal and left parietal area. This study reveals the first EEG observations of the eight most commonly used maqam and is proposing EEG signatures for various maqams.
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Affiliation(s)
| | | | - Sarah Roach
- Premedical Division, Weill Cornell Medicine Qatar, Doha, Qatar
| | | | | | - Ali Chaari
- Premedical Division, Weill Cornell Medicine Qatar, Doha, Qatar
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