Music Improvisation Is Characterized by Increase EEG Spectral Power in Prefrontal and Perceptual Motor Cortical Sources and Can be Reliably Classified From Non-improvisatory Performance

Abstract

This study expores neural activity underlying creative processes through the investigation of music improvisation. Fourteen guitar players with a high level of improvisation skill participated in this experiment. The experimental task involved playing 32-s alternating blocks of improvisation and scales on guitar. electroencephalography (EEG) data was measured continuously throughout the experiment. In order to remove potential artifacts and extract brain-related activity the following signal processing techniques were employed: bandpass filtering, Artifact Subspace Reconstruction, and Independent Component Analysis (ICA). For each participant, artifact related independent components (ICs) were removed from the EEG data and only ICs found to be from brain activity were retained. Source localization using this brain-related activity was carried out using sLORETA. Greater activity for improvisation over scale was found in multiple frequency bands (theta, alpha, and beta) localized primarily in the medial frontal cortex (MFC), Middle frontal gyrus (MFG), anterior cingulate, polar medial prefrontal cortex (MPFC), premotor cortex (PMC), pre and postcentral gyrus (PreCG and PostCG), superior temporal gyrus (STG), inferior parietal lobule (IPL), and the temporalparietal junction. Together this collection of brain regions suggests that improvisation was mediated by processes involved in coordinating planned sequences of movement that are modulated in response to ongoing environmental context through monitoring and feedback of sensory states in relation to internal plans and goals. Machine-learning using Common Spatial Patterns (CSP) for EEG feature extraction attained a mean of over 75% classification performance for improvisation vs. scale conditions across participants. These machine-learning results are a step towards the development of a brain-computer interface that could be used for neurofeedback training to improve creativity.

Beta and theta oscillations correlate with subjective time during musical improvisation in ecological and controlled settings: a single subject study

“…In this experiment, the Free condition corresponded to an unconstrained, more natural situation with respect to experiment 1, in contrast with the Slow and Fast conditions that instructed CR to perform mental imagery of a specific STR state. Therefore, the power increase observed in the Free condition may be interpreted in light of previous findings that showed EEG activity increases when comparing improvisation with "non-improvisation" [7,43]. Note however that the observed power increase might also be interpreted in a more general framework of creativity and flow state.…”

“…Many other studies have used electroencephalography (EEG) and magnetoencephalography, in order to get a finer temporal understanding of neuronal activity during improvisation. Studies have found improvisation related activity in the alpha (8)(9)(10)(11)(12) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) frequency range [7,16,45] located in prefrontal and medial frontal areas, while other studies have examined brain connectivity [32,49] or power changes at the sensor level [14,42,43].…”

Spatiospectral brain networks reflective of improvisational experience

“…Musicians with higher improvisation experience were further found to show lower BOLD activation in the right motor area (inferior frontal gyrus or IFG, anterior insula), regions associated with the default mode network or DMN (angular gyrus), the dorsolateral prefrontal cortex or DLPFC (Pinho et al, 2014) and higher upper-alpha power frontally during improvisation relative to control conditions (Lopata et al, 2017). These findings are supported by studies which contrasted brain activity during musical improvisation relative to control tasks within individuals in fMRI (Limb & Braun, 2008;Bengtsson et al, 2007;de Manzano & Ullén, 2012;Liu et al, 2012;Kouneiher et al, 2009), and complemented by electro-and magnetoencephalography-based studies which, in slightly different tasks, reported increased theta, alpha and beta power (Sasaki et al, 2019), decreased theta, alpha and beta power (Adhikari et al, 2016), or increased alpha and theta, but decreased beta power (Boasen et al, 2018).…”

“…given that there is ample evidence implicating the alpha oscillation in musical improvisation with reports of both increased (Sasaki et al, 2019;Boasen et al, 2018) or decreased (Adhikari et al, 2016) alpha power while musicians improvise in slightly different experiments. Beyond musical improvisation, amplitude changes in the alpha oscillation have been robustly linked to domain general creativity as measured for example by divergent thinking tasks (Zabelina & Ganis, 2018;Fink et al, 2007;Jauk et al, 2012;Schwab et al, 2014) or compound remote associates tasks (Rothmaler et al, 2017), with a relatively high heterogeneity in the direction of effects (Dietrich & Kanso, 2010;Arden et al, 2010) ascribed to the diversity in tasks and methods (Fink et al, 2014), but with findings overall leaning toward increased frontal and parietal alpha power for greater creativity (Dietrich & Kanso, 2010), where one interpretation pointed toward a hypothetical function of alpha in attenuating top-down control (Lustenberger et al, 2015).…”

Comparison of Smoothing Filters’ Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain–Computer Interface Headset during Audio Stimulation

“…The most popular method for the EEG frequency ranges classification is the use of Greek letters: Alpha ( —8–12 Hz), Beta ( —13–30 Hz), Delta ( — Hz), Gamma ( — Hz), Theta ( —4–7 Hz), and Mu—similar to alpha ( —8–12 Hz, which strictly depend on the activity the brain performance [ 3 , 8 , 9 , 11 , 24 , 25 , 26 ].…”