Characterization of the Stages of Creative Writing With Mobile EEG Using Generalized Partial Directed Coherence

Two stages of the creative writing process were characterized through mobile scalp electroencephalography (EEG) in a 16-week creative writing workshop. Portable dry EEG systems (four channels: TP09, AF07, AF08, TP10) with synchronized head acceleration, video recordings, and journal entries, recorded mobile brain-body activity of Spanish heritage students. Each student's brain-body activity was recorded as they experienced spaces in Houston, Texas (“Preparation” stage), and while they worked on their creative texts (“Generation” stage). We used Generalized Partial Directed Coherence (gPDC) to compare the functional connectivity among both stages. There was a trend of higher gPDC in the Preparation stage from right temporo-parietal (TP10) to left anterior-frontal (AF07) brain scalp areas within 1–50 Hz, not reaching statistical significance. The opposite directionality was found for the Generation stage, with statistical significant differences (p < 0.05) restricted to the delta band (1–4 Hz). There was statistically higher gPDC observed for the inter-hemispheric connections AF07–AF08 in the delta and theta bands (1–8 Hz), and AF08 to TP09 in the alpha and beta (8–30 Hz) bands. The left anterior-frontal (AF07) recordings showed higher power localized to the gamma band (32–50 Hz) for the Generation stage. An ancillary analysis of Sample Entropy did not show significant difference. The information transfer from anterior-frontal to temporal-parietal areas of the scalp may reflect multisensory interpretation during the Preparation stage, while brain signals originating at temporal-parietal toward frontal locations during the Generation stage may reflect the final decision making process to translate the multisensory experience into a creative text.

[1]  Satterthwaite Fe.  An approximate distribution of estimates of variance components. , 1946 .

[2]  T. Harmony,et al.  Correlation between EEG spectral parameters and an educational evaluation. , 1990, The International journal of neuroscience.

[3]  Karl J. Friston,et al.  Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets , 1993, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[4]  S. Pincus.  Approximate entropy (ApEn) as a complexity measure. , 1995, Chaos.

[5]  T. Fernández,et al.  Electroencephalographic coherences discriminate between children with different pedagogical evaluation. , 1995, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[6]  H. Petsche,et al.  The possible meaning of the upper and lower alpha frequency ranges for cognitive and creative tasks. , 1997, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[7]  N. Jausovec,et al.  EEG activity during the performance of complex mental problems. , 2000, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[8]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[9]  Arnold Neumaier,et al.  Algorithm 808: ARfit—a matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models , 2001, TOMS.

[10]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[11]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[12]  R. Cabeza,et al.  Interaction between the Amygdala and the Medial Temporal Lobe Memory System Predicts Better Memory for Emotional Events , 2004, Neuron.

[13]  N. Jausovec,et al.  Differences in Resting EEG Related to Ability , 2004, Brain Topography.

[14]  R. Cabeza,et al.  Remembering one year later: role of the amygdala and the medial temporal lobe memory system in retrieving emotional memories. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Xinnian Chen,et al.  Comparison of the Use of Approximate Entropy and Sample Entropy: Applications to Neural Respiratory Signal , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[16]  L.A. Baccald,et al.  Generalized Partial Directed Coherence , 2007, 2007 15th International Conference on Digital Signal Processing.

[17]  Jie Cui,et al.  2008 Special Issue: BSMART: A Matlab/C toolbox for analysis of multichannel neural time series , 2008 .

[18]  Paolo Maria Rossini,et al.  Choice of multivariate autoregressive model order affecting real network functional connectivity estimate , 2009, Clinical Neurophysiology.

[19]  E. Bruce,et al.  Sample Entropy Tracks Changes in Electroencephalogram Power Spectrum With Sleep State and Aging , 2009, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[20]  Danilo P Mandic,et al.  Multivariate multiscale entropy: a tool for complexity analysis of multichannel data. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Katarzyna J. Blinowska,et al.  Review of the methods of determination of directed connectivity from multichannel data , 2011, Medical & Biological Engineering & Computing.

[22]  John M. O'Toole,et al.  Analysis of the time-varying cortical neural connectivity in the newborn EEG: A time-frequency approach , 2011, International Workshop on Systems, Signal Processing and their Applications, WOSSPA.

[23]  Tatjana Loncar-Turukalo,et al.  Unbiased entropy estimates in stress: A parameter study , 2012, Comput. Biol. Medicine.

[24]  Nigel H. Lovell,et al.  Characterizing mental load in an arithmetic task using entropy-based features , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[25]  C. Kessler,et al.  Neural correlates of creative writing: An fMRI Study , 2013, Human brain mapping.

[26]  Tzyy-Ping Jung,et al.  Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[27]  B. Suchan,et al.  The medial temporal lobe: Memory and beyond , 2013, Behavioural Brain Research.

[28]  N. Logothetis,et al.  Auditory and Visual Modulation of Temporal Lobe Neurons in Voice-Sensitive and Association Cortices , 2014, The Journal of Neuroscience.

[29]  N. Neumann,et al.  Neural correlates of verbal creativity: differences in resting-state functional connectivity associated with expertise in creative writing , 2014, Front. Hum. Neurosci..

[30]  Martin Lotze,et al.  Professional training in creative writing is associated with enhanced fronto-striatal activity in a literary text continuation task , 2014, NeuroImage.

[31]  M. Firoozabadi,et al.  Analysis of EEG Signals Related to Artists and Nonartists during Visual Perception, Mental Imagery, and Rest Using Approximate Entropy , 2014, BioMed research international.

[32]  Barbara Landau,et al.  The Necessity of the Medial Temporal Lobe for Statistical Learning , 2014, Journal of Cognitive Neuroscience.

[33]  Jing Hu,et al.  Multiscale entropy analysis of biological signals: a fundamental bi-scaling law , 2015, Front. Comput. Neurosci..

[34]  A. Braun,et al.  Brain activity and connectivity during poetry composition: Toward a multidimensional model of the creative process , 2015, Human brain mapping.

[35]  C. Stam,et al.  The effect of epoch length on estimated EEG functional connectivity and brain network organisation , 2016, Journal of neural engineering.

[36]  Elham Barzegaran,et al.  Functional connectivity analysis in EEG source space: The choice of method , 2017, PloS one.

[37]  Jesus G. Cruz-Garza,et al.  Into the Mind of an Artist: Convergent Research at the Nexus of Art, Science, and Technology , 2019, Springer Series on Bio- and Neurosystems.

[38]  Andrew Y. Paek,et al.  Assaying neural activity of children during video game play in public spaces: a deep learning approach , 2019, Journal of neural engineering.