PMID- 31920594
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20200930
IS  - 1662-5161 (Print)
IS  - 1662-5161 (Electronic)
IS  - 1662-5161 (Linking)
VI  - 13
DP  - 2019
TI  - 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.
PG  - 435
LID - 10.3389/fnhum.2019.00435 [doi]
LID - 435
AB  - 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 temporal-parietal 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.
CI  - Copyright (c) 2019 Sasaki, Iversen and Callan.
FAU - Sasaki, Masaru
AU  - Sasaki M
AD  - Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan.
FAU - Iversen, John
AU  - Iversen J
AD  - Swartz Center for Computational Neuroscience, University of California, San 
      Diego, San Diego, CA, United States.
FAU - Callan, Daniel E
AU  - Callan DE
AD  - Center for Information and Neural Networks (CiNet), National Institute of 
      Information and Communications Technology (NICT), Osaka University, Osaka, Japan.
LA  - eng
PT  - Journal Article
DEP - 20191210
PL  - Switzerland
TA  - Front Hum Neurosci
JT  - Frontiers in human neuroscience
JID - 101477954
PMC - PMC6915035
OTO - NOTNLM
OT  - EEG
OT  - ICA
OT  - guitar
OT  - improvisation
OT  - loreta
OT  - machine learning
OT  - medial frontal cortex
OT  - music
EDAT- 2020/01/11 06:00
MHDA- 2020/01/11 06:01
PMCR- 2019/01/01
CRDT- 2020/01/11 06:00
PHST- 2019/03/31 00:00 [received]
PHST- 2019/11/27 00:00 [accepted]
PHST- 2020/01/11 06:00 [entrez]
PHST- 2020/01/11 06:00 [pubmed]
PHST- 2020/01/11 06:01 [medline]
PHST- 2019/01/01 00:00 [pmc-release]
AID - 10.3389/fnhum.2019.00435 [doi]
PST - epublish
SO  - Front Hum Neurosci. 2019 Dec 10;13:435. doi: 10.3389/fnhum.2019.00435. 
      eCollection 2019.