Beta and Theta Oscillations Correlate With Subjective Time During Musical Improvisation in Ecological and Controlled Settings: A Single Subject Study

Abstract

In this paper, we describe the results of a single subject study attempting at a better understanding of the subjective mental state during musical improvisation. In a first experiment, we setup an ecological paradigm measuring EEG on a musician in free improvised concerts with an audience, followed by retrospective rating of the mental state of the improviser. We introduce Subjective Temporal Resolution (STR), a retrospective rating assessing the instantaneous quantization of subjective timing of the improviser. We identified high and low STR states using Hidden Markov Models in two performances, and were able to decode those states using supervised learning on instantaneous EEG power spectrum, showing increases in theta and alpha power with high STR values. In a second experiment, we found an increase of theta and beta power when experimentally manipulating STR in a musical improvisation imagery experiment. These results are interpreted with respect to previous research on flow state in creativity, as well as with the temporal processing literature. We suggest that a component of the subjective state of musical improvisation may be reflected in an underlying mechanism related to the subjective quantization of time. We also demonstrate the feasibility of single case studies of musical improvisation using brain activity measurements and retrospective reports, by obtaining consistent results across multiple sessions.

Unravelling individual rhythmic abilities using machine learning

“…Machine learning is progressively becoming an important element of the toolbox for research in cognitive science and neuroscience (Aglinskas et al, 2022;Richards et al, 2019;Shen et al, 2022;Yarkoni & Westfall, 2017) with applications in music research (for a review, Agres et al, 2021) such as computational music analysis (e.g., music information retrieval and automatic music classification; (Lau & Ajoodha, 2022;Mueller et al, 2019;Stober et al, 2014), and more recently in music cognition (e.g., for emotion detection, Vempala & Russo, 2017). A similar approach based on machine learning and graph theory as used to model individual differences in rhythmic abilities could be purposefully extended to other music abilities such as pitch perception and production, or improvisation (e.g., Farrugia et al, 2021, for a single-case study).…”

EEG Correlates of Middle Eastern Music Improvisations on the Ney Instrument

“…Single case studies are being used by many across multiple sessions to obtain consistent results using brain activity measurements ( Farrugia et al, 2021 ).…”

The brain in flow: A systematic review on the neural basis of the flow state

“…We identified 11 studies addressing the neural correlates of flow with EEG (4 were case studies) while trying to induce flow state through different experimental protocols (Farrugia et al, 2021;Katahira et al, 2018;Knierim et al, 2018Knierim et al, , 2021Leroy & Cheron, 2020;Moreno et al, 2020;Núñez Castellar et al, 2019;Shehata et al, 2021;Wolf et al, 2015;Wollseiffen et al, 2016;Yun et al, 2017).…”

“…Furthermore, case studies provide an opportunity to collect (neural) data in ecological contexts and a fine-grained characterization of the flow experience. We identified 3 relevant case studies using this approach (Farrugia et al, 2021;Leroy & Cheron, 2020;Moreno et al, 2020). In general, their results point to brain rhythms, especially alpha, beta and gamma, as potential markers of flow state arising in tasks requiring highly specialization such as playing a musical instrument (Farrugia et al, 2021), writing a scientific manuscript (Moreno et al, 2020) or crossing a 15-metre-high tightrope (Leroy & Cheron, 2020).…”

“…We identified 3 relevant case studies using this approach (Farrugia et al, 2021;Leroy & Cheron, 2020;Moreno et al, 2020). In general, their results point to brain rhythms, especially alpha, beta and gamma, as potential markers of flow state arising in tasks requiring highly specialization such as playing a musical instrument (Farrugia et al, 2021), writing a scientific manuscript (Moreno et al, 2020) or crossing a 15-metre-high tightrope (Leroy & Cheron, 2020). Interestingly, these studies suggest the use of peripheral psychophysiological measures (e.g., electrocardiogram or galvanic skin conductance) as complementary measures that may help identify and characterize flow state (see Peifer and Tan, 2021, for a review).…”