Brain Activation and Cognitive Load during EEG Measured

Creativity Tasks Accompanied by Relaxation Music

Dalia Papuc

, Oana Bălan

, Maria-Iuliana Dascălu

, Alin Moldoveanu

and Anca Morar

University Politehnica of Bucharest, Faculty of Engineering in Foreign Languages,

Splaiul Independentei, 313, Bucharest, Romania

University Politehnica of Bucharest, Faculty of Automatic Control and Computer Science,

Splaiul Independentei, 313, Bucharest, Romania

Keywords: EEG, Creative Task, Alpha Power, Cognitive Load.

Abstract: Creativity tasks require specific imagery, memory and semantic processes, as it has been revealed by

various neuroscientific studies. There is significant evidence that an increase of spectral power in the EEG

alpha band is inter-related with creative ideation as a form of top-down activity. However, any creational

task demands a certain level of cognitive load or workload for memory retrieval, mental schemes design,

semantic processing, image formation and concept construct. This paper aims to measure cognitive load

during a series of divergent thinking creative tasks accompanied by relaxation music, to examine event-

related brain activation and raw power measures in the baseline relaxation, alternate uses creative and

verbalization stages of the proposed experimental procedure, as well as to study task-related

synchronization/desynchronization between these phases. Also, its purpose is to verify whether slow

relaxing music influences creativity, with effects in the brainwaves amplitude variations, especially in the

alpha (8-12 Hz) frequency band. We concluded that relaxing music induces creativity and causes an

increase in the alpha brain waves for innovative ideas generation and verbalization with a diminished level

of cognitive load.

1 INTRODUCTION

Along with mental constructs such as intelligence,

creativity is an important asset of the human

psychological profile, with applicability for

producing original and innovative work in a wide

range of scientific domains, for social

communication and interaction or for achieving

various lifetime proposed goals (Fink & Benedek,

2012, Smith 1995). Creativity has been studied

within many experiments requiring completion of

different types of originality tasks, among which a

notable position is reserved to the divergent

production alternate uses tasks, where participants

are asked to bring original and unconventional

solutions or explications to common notions or

concepts (Arden 2010). This type of task involves

retrieval of existing knowledge from the memory

and (re)combination of it into singular ideas (Paulus

and Brown, 2007), generating diversified approaches

in manifold ways, as opposed to convergent thinking

that brings about only one straight solution. There

are many neuroscientific studies that have analysed

brain activity during creativity tasks, considering

spectral power activation within the EEG bands,

event-related potentials (ERP), event-related

synchronization/desynchronization (ERS/D) or

functional connectivity between different cortical

areas – coherence and phase lag. The alpha band is

the most sensitive band in relation with creativity

demands – there is higher alpha power in the

posterior regions of the brain during activation

periods as compared to baseline resting stages

(Jausovec, 1997), in the central and parietal areas

(Molle, 1999), posterior regions of the right

hemisphere during divergent, free-associative

thinking tasks than in the convergent ones

(Shemyakina, 2007) and in the posterior cortical

areas (parietal and parietoocipital) in the alternate

uses procedure (Fink, 2009). Also, more original

ideas conduct to higher right hemisphere alpha

synchronization (Grabner, 2007, Martindale, 1984).

Increases in the parietal and occipital areas is

associated with higher cognitive load, memory

Papuc D., BÄ

Clan O., DascÄ

Clu M., Moldoveanu A. and Morar A.

Brain Activation and Cognitive Load during EEG Measured Creativity Tasks Accompanied by Relaxation Music.

DOI: 10.5220/0006511201560162

In Proceedings of the International Conference on Computer-Human Interaction Research and Applications (CHIRA 2017), pages 156-162

ISBN: 978-989-758-267-7

search, retrieval and semantic associations, increased

focus on goal-directed tasks, diverting irrelevant

stimuli, internal mnemonic representations or

memory retrieval according to the purposes of the

user – top-down activation in the absence of bottom-

up stimulation (dorsal parietal) (Cabeza, 2008,

Jensen, 2002). The right hemisphere is dedicated to

imagery and association of unrelated semantic

concepts (Bowden, 2003). Alpha synchronization

appears also in the prefrontal cortex, especially in

the right hemisphere (Hietanen, 1998), reflecting

high processing demands (Benedek, 2011) and

increased attention (Knyazev, 2007). The results

from (Punsawad et al, 2014) showed increased

levels of alpha and theta during creational tasks in

the right frontal areas (channel F8).

Cognitive load theory states that working

memory is limited in regard with the quantity of

information that can be kept in mind at a moment of

time and to the ability of processing and combining

novel knowledge (Antonenko et al, 2010, Peterson,

1965). EEG measurements revealed that alpha

desynchronization is associated with increased

attention and cognitive load in the parietal areas

(Gevins, 1997). Also, it is related to searching and

retrieving information from the long-term memory.

Theta synchronization on the other hand is related to

task difficulty and emotional factors, especially in

the frontal midline locations (Klimesch, 2005,

Gevins, 2000) and is associated with episodic and

working memory processes. Delta power increases

also during complex mental tasks in the temporal

regions (channels T7 and T8), demonstrating the

amount of attention given by the user to internal

information processing (Dolce, 1974), while low

beta bands have synchronized powers in the frontal

midline areas (Klimesch, 1999)

Gifted individuals exhibit higher alpha power

changes (high level of relaxation) and activation in

the frontal cortical area, whereas hard working

subjects present low level alpha synchrony,

particularly in the temporal regions (O’Boyle 1995).

In (Antonenko et al, 2010), video and picture

presentation activated the occipital and temporal

lobes (responsible for visualization), while text

presentation, the frontal one (assigned to verbal

processing).

2 PROPOSED METHOD

Our study proposes an alternate uses divergent

thinking set of tasks, in which the subject is required

to think about uncommon uses for 5 different words

- pencil, brick, key, paper clip and shoe, while

listening to slow relaxing music. The first step is

represented by the fixation cross to which the

subjects have to look for a time period of 10

seconds. This step will be referred to as reference

(or calibration, to record baseline brain activity)

because this time interval aims to be reference

interval for the EEG activity. Secondly, a stimulus

word was shown for 5 seconds. The subjects were

given instructions to be as innovative as possible and

to come up with different usages than the common

one for the displayed object. The next step is

represented by the uncoloured bulb image, 30

seconds of creative thinking time for the

participants. During this step, the subjects were prior

instructed not to verbalize their answer yet, but only

to think of original uses of the previously seen

stimulus. Simultaneously, the subject was provided

relaxation music via headphones. The final phase is

represented by the coloured bulb image, which lasts

for 10 seconds, time in which the participants had to

verbalize their creative ideas (Figure 1).

Verbalization has been chosen over listing because it

takes less time and is simpler for participants, the

emphasis being on the idea generation process. The

task requires 55 seconds to be completed. Each of

the subjects performed the task five times, once per

stimulus word.

Figure 1: The experimental procedure.

3 THE EXPERIMENT

3.1 Subjects

The study was performed on six subjects (four

females and two males) aged 21-29, five

undergraduate students and a teacher. The subjects

were orally informed about the purpose of the

experiment, were provided instructions and gave

their approval consent before the start of the tests.

3.2 Data Acquisition

The hardware devices used to capture EEG signals

through the non-invasive method were the Brain

Products actiCAP Xpress Bundle set together with

V-Amp 16ch amplifier (Acticap website). The

software used for EEG acquisition was OpenVibe

for Brain Products (Open Vibe website). All the

signals were sampled at a frequency of 512 Hz, as it

is the optimal sampling rate for analysing resting

EEG (Jing & Takigawa, 2000). The used electrodes

positions were: fronto-parietal left (FP1) and right

(FP2), fronto-central left (FC1) and right (FC2),

central left (C3) and right (C4), temporal left (T7)

and right (T8), parietal left (P3) and right (P4) and

occipital left (O1) and right (O2). Furthermore, 4

midline electrodes were used (Fz, Cz, Pz, Oz).

Electrodes impedances were kept below 10 kΩ in

order to minimize the noise and artefacts in the data.

The recorded data was bandpass filtered between

0.5-100 Hz.

3.3 Data Acquisition

The term EEG artefact refers to recorded electrical

activity whose origin is non-cerebral. Depending on

its origin, these artefacts are split into physiological

and non-physiological artefacts. The first category is

generated from the body (other than the brain), while

the second one from outside the body. Blinking, eye

movements, muscle activity, body or head

movement, cardiac, pulse waves, they all represent

examples of the physiological artefacts. Some of

these can be recognized. Non-physiological artefacts

are caused by either electrical interference with other

power sources or the abnormal functioning of the

recording equipment. A typical electrical

interference artefact present in EEG recordings has

as source power lines and equipment. This artefact

has a 50/60 Hz frequency. Our signals were DC-

offset corrected in order to prevent the influence of

voltage imbalance issues and powerline

contamination was removed using the notch filter.

Blinking, cardiac and muscle activity artefacts,

together with the bad channels were automatically

removed using the Brainstorm software (Tadel et al,

2011). Independent Component Analysis has been

performed in EEGLAB (EEG LAB website) in order

to separate independent sources, reject artefacts and

identify brain related activity in coloured

topographic maps.

3.4 Power Spectrum Analysis in

Frequency Bands

The information was extracted for the last step

which is statistical analysis. Firstly, the data that is

to be statistically analysed requires having the same

length. Currently, most of the recordings have

around 55 seconds, -4/+15 seconds, according to the

number of inputs the subjects were having in the

verbalization phase. Given the interest in studying

how the brain waves change in the divergent

thinking phase, as stated in the hypothesis, the signal

was split such as the reference phase may be

compared with the other phases. Therefore, the

signal was split as follows - from a time window

perspective: [0 10] for the reference phase, [15 25],

[25 35] & [35 45] for the creative thinking phase and

[45 55] for the verbalization phase. Extracting all

these intervals was done manually in EEGLAB.

After being split into intervals, the EEG recordings

were imported in Brainstorm where further

processing steps were taken.

Figure 2: Power spectral analysis for Resting period -

averaged response across all subjects.

Figure 3: Power spectral analysis for Creative Thinking

period - averaged response across all subjects.

Considering the averaged responses for all the

subjects, alpha activity was prominent as follows: in

the resting phase - in the left occipital region (Figure

2), in the creative thinking phase – in the right

parietal area (Figure 3), while in the verbalization

stage – in the right parietal and left occipital regions

(Figure 4). Similar patterns are observed in the theta

band, with the only difference that in the

verbalization phase, activation is prominent only in

the right parietal cortical area and to a lower extent

in the left occipital region, demonstrating the slight

influence of visualization in verbal tasks. Alpha

synchronization occurs in the right parietal area and

desynchronization is evident in the occipital region

in the thinking stage, as compared to resting

baseline.

Figure 4: Power spectral analysis for Verbalization period

- averaged response across all subjects.

3.5 Statistical Analysis

Even though fluctuations over time in the alpha

waves can be noticed by examining the time-series

for a signal time-frequency decomposition, in order

to formalize the results and to be able to actually

draw conclusions, a statistical analysis needs to be

performed. Another reason for performing this

analysis is represented by the number of data that

would require a thorough individual analysis to

reach a global study result.

Taking into consideration the hypothesis: “There

is a significant change in alpha activity during a

creative activity as compared to a non-creative one”,

the study aims to evaluate whether creativity causes

an increase in the alpha brain waves through a given

divergent thinking task and a statistical hypothesis

test is applied to check whether there is a significant

difference between the two phases. The data was

analysed in a parametric Student t-test and 2D

topography of the results was plotted.

30 files (10 seconds duration) resulting from

resting baseline recording were compared to 90 files

(10 seconds duration each) from the creativity task

recording. The results show significant differences

in the left fronto-central cortical area at p<0.13

where the mean of the alpha power in the creative

thinking condition is higher than in the resting

phase. Taking the first test scenario, reference (A)

vs. thinking (B) phase and the previous explanations,

the results are statistically significant in the direction

of condition B (blue) represented by the thinking

phase in this scenario. The confidence level for these

tests is close to 90%. The results of this study prove

that indeed creative ideas generation is associated

with the growth of the alpha activity in comparison

with the reference period (Figure 5).

Figure 5: Student t-test. Reference (A) vs Creative

Thinking (B), p < 0.13.

Figure 6: Student t-test. Verbalization (A) vs Creative

Thinking (B), p < 0.01.

30 files (10 seconds duration) resulting from

verbal recordings were compared to 90 files (10

seconds duration each) from the creativity task

recording. The results show significant differences

in the left fronto-central and right parietal and

temporal cortical areas at p<0.01 where the mean of

the alpha power in the verbal condition is higher

than in the creative thinking phase. Given the fact

that the critical level for the verbalization vs.

reference test is lower (p < 0.01) than the one of

thinking (B) vs. reference (A) phases (p < 0.13), and

that the regions where alpha activity presents

significant changes are identical, we would expect

that in the verbalization phase the alpha activity is

increased as compared to thinking phase. Figure 6

proves a remarkable increase in the alpha activity in

the frontal and temporal lobes as well as in the

central region, to be more specific, in the frontal left

hemisphere - especially frontopolar (FP1) and in the

temporal (T8) and central (C4) right hemisphere

(Figure 6).

The values displayed in the 2D test result views

(Figure 5 & 6) represent the significant t-values, the

sensors that have the value of p > α being set to 0.

We may state that for those ‘white’ brain regions the

hypothesis is likely to occur → the null hypothesis

(H0) is not rejected, thus no significant changes

occur in the alpha waves. The two-tailed tests

establish whether the difference between the two

groups, A and B, is statistically significant in either

the positive or negative direction (Frost, 2016) In the

figures, the red values represent a higher amplitude

for condition A, while the blue values for the

condition B.

Quantitative EEG (QEEG), as the measurement

of the electrical brain activity and connectivity

between different areas, offers substantial and

valuable information about the dynamic changes of

brain activation, interrelation, engagement and

overload of various areas. Brain connectivity refers

to coherence analysis (correlation between different

brain areas) and phase lag (speed of information

transfer on the cortex and rate of data transfer).

The results showed high correlation in the frontal

area (FP1 x FP2), parietal and temporal regions (P3

x P4, P3 x T7, Pz x T7, Pz x P3). High rates of data

transfer (high values of phase lag) occur in the

frontal regions for the theta band (FP1 x FP2) and in

the left frontal, parietal and central areas for the

alpha band (FP1 x C3, C3 x Pz, T7 x C3, FC1 x C3,

FC1 x FP2, P3 x P4, P3 x T7).

4 DISCUSSION

In a creativity related brain activity study, the

answers of subjects that were more innovative

positively correlated with lower alpha (8 - 10 Hz)

amplitudes mainly in the left hemisphere. The same

study also shows desynchronization in the central

and posterior regions, but not in the frontal one

(Razumnikova, 2007). Taking into account the

results of the aforementioned study which also state

that highly original responses generated increased

event-related synchronization in the left hemisphere

for the low alpha band (Haarmann et al, 2013), we

may make the assumption that the increased alpha

activity in the left hemisphere is correlated with

higher originality in the subjects’ answers.

Beta synchronization in the frontal and midline

regions are indicators of cognitive load. In our study,

alpha synchronization occurs for all the frequency

bands and beta desynchronization is visible in the

frontal and midline areas. Also, there is no

significant difference between the resting and

creative task for the theta and delta bands. Thus, we

conclude that the level of cognitive workload is

reduced during alternate uses divergent thinking

tasks as far as it concerns the delta, theta and beta

bands and that a sign of activation is visible only for

the alpha band, indicating memory retrieval and

attentional demands. However, as far as it concerns

the averaged responses across all subjects and trials

(Figure 3), alpha synchronization is visible in the

creative thinking phase in the parietal area and less

visible in occipital area.

One aspect to discuss is the value of the p-value

threshold. To increase the accuracy of the statistical

tests, the p-value threshold could have been

decreased, mainly for the reference vs thinking

groups test, however, the results would not have

shown this significant information if done so.

Moreover, a larger sample size will normally lead to

a better estimate too. The eventual presence of

physiological artefacts, the possibility of not having

the phases according to such strict time intervals as

defined in the experimental task, and not having the

ability to check precisely whether the subjects are

following the instructions for the first 45 seconds

(especially thinking creatively for the whole 30

seconds interval) of the EEG recordings, they all

contribute to eventual discussions on test’s accuracy.

5 CONCLUSIONS

The aforementioned findings allow us to validate the

study hypothesis which states that there is a

significant change in alpha activity during a creative

activity as compared to a non-creative one. Through

giving the subjects an alternate uses task which aims

to measure divergent thinking while listening to

relaxing music and recording their brain activity, it

was evaluated and concluded that creativity causes

an increase in the alpha brain waves in the

innovative ideas generation as well as in their verbal

expression phases with a diminished level of

cognitive load.

Further research concerns encompass increasing

the number of subjects and trials per experiment in

order to obtain higher statistical significance and

designing an experiment where the subjects will be

required to listen to different music genres or

semantically significant ambient sounds connected

to the meaning of the word they have to creatively

think about.

ACKNOWLEDGEMENTS

This project has received funding from the European

Union’s Horizon 2020 research and innovation

programme under grant agreement No 643636

“Sound of Vision. This work has been funded by

University Politehnica of Bucharest, through the

“Excellence Research Grants” Program, UPB –

GEX 2017. Identifier: UPB- GEX2017, 18 from

8.09.2017/2017 SAFE-VR.

REFERENCES

Fink, A., Benedek, M., 2012. EEG alpha power and

creative ideation. Neurosci. Biobehav.

Smith, S.M., Ward, T.B., Finke, R.A., 1995. The Creative

Cognition Approach. MIT Press,Cambridge.

Arden, R., Chavez, R.S., Grazioplene, R., Jung, R.E.,

2010. Neuroimaging creativity: a psychometric

review. Behavioural Brain Research 214, 143–156.

Paulus, P.B., Brown, V.R., 2007. Toward more creative

and innovative group idea generation: a cognitive-

social-motivational perspective of brainstorming.

Social and Personality Psychology Compass 1, 248–

265.

Jausovec, N., 1997. Differences in EEG activity during the

solution of closed and open problems. Creative

Research Journal 10, 317–324.

Mölle, M., Marshall, L., Wolf, B., Fehm, H.L., Born, J.,

1999. EEG complexity and performance measures of

creative thinking. Psychophysiology 36, 95–104.

Shemyakina, N.V., Danko, S.G., Nagornova, Z.V.,

Starchenko, M.G., Bechtereva, N.P. ,2007. Changes in

the power and coherence spectra of the EEG rhythmic

components during solution of a verbal creative task of

overcoming a stereotype. Human Physiology 33, 524–

530.

Fink, A., Graif, B., Neubauer, A.C., 2009. Brain correlates

underlying creative thinking: EEG alpha activity in

professional vs. novice dancers. NeuroImage 46,854–

862.

Grabner, R.H., Fink, A., Neubauer, A.C., 2007. Brain

correlates of self-rated originality of ideas: Evidence

from event-related power and phase-locking changes

in the EEG. Behavioral Neuroscience 121, 224–230.

Martindale, C., Hines, D., Mitchell, L., Covello, E., 1984.

EEG alpha asymmetry and creativity. Personality and

Individual Differences 5, 77–86.

Cabeza, R., Ciaramelli, E., Olson, I.R., Moscovitch, M.,

2008. The parietal cortex and episodic memory: an

attentional account. Nature Reviews Neuroscience 9,

613–625.

Jensen, O., Gelfand, J., Kounios, J., Lisman, J.E., 2002.

Oscillations in the alpha band (9-12 Hz) increase with

memory load during retention in a short-term memory

task. Cerebral Cortex 12, 877–882.

Bowden, E.M., Jung-Beeman, M., 2003. Aha! Insight

experience correlates with solution activation in the

right hemisphere. Psychonomic Bulletin & Review

10,730–737.

Hietanen, J.K., Surakka, V., Linnankoski, I., 1998. Facial

electromyographic responses to vocal affect

expressions. Psychophysiology 35, 530–536.

Benedek, M., Bergner, S., Könen, T., Fink, A., Neubauer,

A.C., 2011. EEG alpha synchronization is related to

top-down processing in convergent and divergent

thinking. Neuropsychologia 49, 3505–3511.

Knyazev, G.G., 2007. Motivation, emotion, and their

inhibitory control mirrored inbrain oscillations.

Neuroscience & Biobehavioral Reviews 31, 377–395.

Punsawad, Y., Chathong, W., Wongsawat, Y., 2014. The

use of quantitative EEG in creativity study with simple

task,

APSIPA, Asia-Pacific.

Antonenko, P. D., Paas, F., Grabner, R., & Van Gog, T,

2010. Using electroencephalography to measure of

cognitive load. Educational Psychology Review, 22,

425-438.

Peterson, L., & Peterson, M., 1959. Short-term retention

of individual verbal items. Journal of Experimental

Psychology, 58, 193–198.

Gevins, A., Smith, M. E., McEvoy, L., & Yu, D., 1997.

High-resolution EEG mapping of cortical activation

related to working memory: Effects of task difficulty,

type of processing, and practice. Cerebral Cortex, 7,

374–385.

Klimesch, W., Schack, B., & Sauseng, P., 2005. The

functional significance of theta and upper alpha

oscillations for working memory: A review.

Experimental Psychology, 52, 99–108.

Gevins, A., & Smith, M. E., 2000. Neurophysiological

measures of working memory and individual

differences in cognitive ability and cognitive style.

Cerebral Cortex, 10, 829–839.

Dolce, G., Waldeier, H., 1974. Spectral and multivariate

analysis of EEG change during mental activity in man.

Clinical neurophysiology, 577-584.

Klimesch, W., 1999. EEG alpha and theta oscillations

reflect cognitive and memory performance: a review

and analysis. Brain Research Reviews, 169-195.

O'Boyle, M. W., Benbow, C. P., & Alexander, J. E., 1995.

Sex differences, hemispheric laterality, and associated

brain activity in the intellectually gifted.

Developmental Neuropsychology, 4, 415–443.

Acticap Express Bundle. Available at:

http://www.brainproducts.com/actiCAPXpress.

Open Vibe. Available at: http://openvibe.inria.fr/

Jing, H., & Takigawa, M. 2000. Low sampling rate

induces high correlation dimension on

electroencephalograms from healthy subjects.

Psychiatry and Clinical Neurosciences, 54 (4), 407-

412. doi:10.1046/j.1440-1819.2000.00729.x.

Tadel, F., Baillet, S., Mosher, J.C., Pantazis, D., Leahy,

R.M., 2011. Brainstorm: A User-Friendly Application

for MEG/EEG Analysis. Computational Intelligence

and Neuroscience, vol. 2011, ID 879716.

EEG LAB. Available at: https://sccn.ucsd.edu/

eeglab/index.php.

Frost, J., 2016. Understanding t-Tests: t-values and t-

distributions. Retrieved June 21, 2017, from

http://blog.minitab.com/blog/adventures-instatistics-

2/choosing-between-a-nonparametric-test-and-a-

parametric-test.

Razumnikova, O. M., 2007. Creativity related cortex

activity in the remote associates task. Brain Research

Bulletin, 73 (1-3).