ORIGINAL RESEARCH
published: 09 March 2018
doi: 10.3389/fpsyg.2018.00300

EEG Correlates of the Flow State: A
Combination of Increased Frontal
Theta and Moderate Frontocentral
Alpha Rhythm in the Mental
Arithmetic Task
Kenji Katahira 1,2*, Yoichi Yamazaki 1,2 , Chiaki Yamaoka 1 , Hiroaki Ozaki 1 ,Sayaka Nakagawa 1 and Noriko Nagata 1,2
1

School of Science and Technology, Kwansei Gakuin University, Sanda, Japan, 2 Research Center for Kansei Value Creation,
Kwansei Gakuin University, Sanda, Japan

Edited by:Michele Biasutti,
Università degli Studi di Padova, Italy
Reviewed by:
Mari Tervaniemi,
University of Helsinki, Finland
Anna Rita Addessi,
Università di Bologna, Italy*Correspondence:
Kenji Katahirak.katahira@kwansei.ac.jp
Specialty section:
This article was submitted to
Performance Science,a section of the journal
Frontiers in Psychology
Received: 13 September 2017
Accepted: 22 February 2018
Published: 09 March 2018
Citation:
Katahira K, Yamazaki Y, Yamaoka C,
Ozaki H, Nakagawa S and Nagata N(2018) EEG Correlates of the Flow
State: A Combination of Increased
Frontal Theta and Moderate
Frontocentral Alpha Rhythm in the
Mental Arithmetic Task.
Front. Psychol. 9:300.
doi: 10.3389/fpsyg.2018.00300

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Flow experience is a subjective state experienced during holistic involvement in a certain
activity, which has been reported to function as a factor promoting motivation, skill
development, and better performance in the activity. To verify the positive effects of flow
and develop a method to utilize it, the establishment of a reliable measurement of the flow
state is essential. The present study utilized an electroencephalogram (EEG) during anexperimentally evoked flow state and examined the possibility of objective measurementof immediate flow. A total of 16 participants (10 males, 6 females) participated in theexperiment that employed a mental arithmetic task developed in a previous study.
Post-trial self-report of the flow state and EEG during task execution were measured
and compared among three conditions (Boredom, Flow, and Overload) that had different
levels of task difficulty. Furthermore, the correlations between subjective flow items andEEG activity were examined. As expected, the ratings on the subjective evaluation
items representing the flow state were the highest in the Flow condition. Regarding
the EEG data, theta activities in the frontal areas were higher in the Flow and theOverload conditions than in the Boredom condition, and alpha activity in the frontal areas
and the right central area gradually increased depending on the task difficulty. TheseEEG activities correlated with self-reported flow experience, especially items related
to the concentration on the task and task difficulty. From the results, the flow state
was characterized by increased theta activities in the frontal areas and moderate alphaactivities in the frontal and central areas. The former may be related to a high level ofcognitive control and immersion in task, and the latter suggests that the load on theworking memory was not excessive. The findings of this study suggest the possibility of
distinguishing the flow state from other states using multiple EEG activities and indicate
the need for other physiological indicators corresponding to the other aspects of flow
experience.
Keywords: Flow experience, Objective measurement, EEG, mental arithmetic task, FM theta

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EEG Correlates of the Flow StateINTRODUCTIONTABLE 1 | The components of flow experience.

In any task, for better performance, performing at one’s fullpotential in individual situations and obtaining the required skills
in advance are important. In general, the development of anindividual’s ability through training or performance, specific toeach activity, enables better performance. On the other hand, an
optimal psychological state that contributes to these goals can be
a common promotional factor across various types of activity.
The most promising optimal psychological state is the flow
experience proposed by Csikszentmihalyi (2000). According toCsikszentmihalyi (2000), who originally proposed this concept,
flow is the “holistic sensation that people feel when they act withtotal involvement.” The state of flow is characterized by a sense of
intrinsic reward experienced during immersive engagement in an
activity. Flow was investigated in a broad spectrum of activities;
for example, chess playing (Abuhamdeh and Csikszentmihalyi,2009) and rock climbing (Fave et al., 2003). The common
characteristics of flow (Nakamura and Csikszentmihalyi, 2009)are summarized as two conditions of flow and six subjective statesof flow as shown in Table 1.
Flow was originally found in studies that examined thephenomenological aspects of autotelic or intrinsically motivatedactivities (Nakamura and Csikszentmihalyi, 2009). Therefore,
flow is regarded as the optimal experience that promotes intense
commitment or an individual’s development in a certain activity.
Previous research has revealed that flow seems to enhance several
aspects of activities; for example, motivation (Csikszentmihalyi
et al., 1993; Jackson et al., 1998), development (Carli et al.,
1988; Nakamura, 1988), and performance (Jackson et al., 2001;
MacDonald et al., 2006).
By definition, flow is considered to trigger a feeling of intrinsic
reward. Therefore, it is assumed that those who experience thestate of flow in a specific activity experience an increase in theirmotivation to engage in that activity. Prolonged influences offlow on motivation for specific activities were found in a study
conducted by Csikszentmihalyi et al. (1993), which longitudinallyinvestigated the activities of teenagers who were talented in
specific areas. Students who experienced more flow in theirtalented areas tended to be involved in activities in these areas
4 years later, which suggested the relationship between flow andcontinuous commitment to activities. In the field of sports, astudy on elderly athletes revealed a positive relationship between
the state of flow and intrinsic motivation (Jackson et al., 1998).
Experiencing flow makes activities intrinsically rewarding,which enables development of skills related to an activity
by encouraging increased commitment. Moreover, the balance
between skills and challenges required for experiencing flow
provides an optimal level of challenge that develops existingskills (Vygotsky, 1978). Therefore, flow is considered to foster thedevelopment of skills in the long term. Several studies have shown
the relationship between flow and achievement in the high school
context (Carli et al., 1988; Nakamura, 1988).
Furthermore, flow is associated with the quality of
performance of specific tasks. Jackson et al. (2001) reported
that the state of flow during competitive sports correlatedwith quality of performance measured both subjectively

The conditions of flow

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Balance between challenge and skill
Clear goals and feedback
The subjective states of flow
Intense concentration
Merging of action and awareness
Loss of reflective self-consciousness
A sense of control
Distortion of temporal experience
Experience of intrinsic reward

and objectively. MacDonald et al. (2006) revealed that flow
experienced by students when working on a group composition
task was related to specialists’ assessment of the quality of
produced musical compositions. This may be due to reduced
self-consciousness when experiencing flow, which may decrease
performance anxiety (Fullagar et al., 2013) and encourage
performers to focus on their task.
Given the positive influences of flow described above, it
is expected that increasing the experience of flow promotes
individuals’ development and enhances motivation and qualityof performance in each specific situation. Recently, researchhas been conducted aiming to utilize these outcomes of flow.
In the field of web design, it is assumed that experiential
flow determines user behavior in the context of the computermediated environment. For example, the relationship between
flow and online consumer behavior was investigated in a previous
study (Smith and Sivakumar, 2004). The attributes of the website
influencing the experience of flow were also examined (Huang,
2003). In the context of computer game research, enjoymentof video games is considered to be explainable in terms of theflow experience (Weber et al., 2009). Researchers have not only
employed game playing as an appropriate task to investigate flow
(Keller and Bless, 2008), but also explored the factors of gameplaying that impact the evocation of flow (Johnson and Wiles,
2003). In the context of music education, the flow experience isdrawing attention as a factor that promotes musical learning. In
addition to the development of applications to enhance the flow
experience of adults (Nijs et al., 2012) and children (Addessi et al.,2015) in musical activities, individual resources that contributeto evoking flow including social factors have been investigated(Bakker, 2005). Furthermore, in the field of human-computerinteraction, it has been pointed out that flow is a promisingindicator for dealing with the state of mind of users who actively
engage in certain activities (Leman et al., 2010).
Although the utility of flow has been found in various fields,
further research is necessary to establish how beneficial outcomescan be obtained by evoking the flow experience. For such efforts,
various types of measures would be required to assess whether thedeveloped methods increase flow. Several psychological measures
have been developed to evaluate the flow experience subjectively.
However, the self-report approach requires interrupting anactivity and taking time to collect responses, which makes it
difficult to measure an immediate state of flow during ongoing

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EEG Correlates of the Flow State

flow state. There are no comparable data because no study
has yet measured EEG data when flow was evoked using thisexperimental paradigm. Therefore, in this research, no specifichypotheses were formulated, and exploratory investigation
was conducted across multiple electrode positions and EEG
frequency ranges.

activities. To make this possible, it would be effective to establish
a method to measure the state of flow based on an objective index.
With respect to examples of indicators based on observationof behavior, Custodero (2005) proposed indices to measure theflow of infants and school-age children based on observablenonverbal behavior, while Addessi et al. (2015) developed an
observation-based coding system to measure the occurrence offlow.
Several previous studies have attempted to measure the flow
state objectively using physiological and neurological measures.
Klasen et al. (2011) conducted an fMRI study to assess brain
activity during the first person’s game task. They analyzed
the contents of the game play to identify events that werelikely to evoke the state of flow and revealed the activities
of multiple brain regions during those events. However, theinfluence of the movements generated for game play wassuggested for some of those activities. Ulrich et al. (2014)
examined brain activity when flow was experienced using fMRI
in a mental arithmetic task, which minimized the influenceof bodily movements. The brain activities were compared inthree conditions, which were different in terms of task difficulty:
Boredom, Flow, and Overload conditions. The relationshipbetween brain activity and self-reported flow experience wasalso examined. They concluded that increased activity in theinferior frontal gyrus and putamen reflected a deeper sense ofcognitive control and coding of reward-related information, anddecreased activity in the medial prefrontal cortex and amygdala
represented a loss of self-referential processing and negative
arousal.
Research using fMRI described above has clarified neuralactivity as consistent with the theory of flow; however, given
our purpose of assessing the method used to evoke the stateof flow, low temporal resolution of imaging techniques isproblematic. To measure the immediate flow state and its
time course during the experimental task, other indicatorswith a higher temporal resolution are considered to be more
effective. de Manzano et al. (2010) measured heart rate,
respiration, and electromyogram from professional pianists
during a piano performance, and identified physiologicalactivities related to self-reported flow experience. Among thephysiological measures, electroencephalogram (EEG) has beenreported to be related to various brain functions; therefore,
EEG is considered suitable for exploring indices related to flow,which is a combination of a variety of subjective states. However,EEG activity when experiencing flow state has not been fully
investigated.
The purpose of this study was to clarify EEG activities
corresponding to an experimentally evoked flow state. Using theexperimental paradigm of the previous study, which adopted themental arithmetic task (Ulrich et al., 2014), several subjective
states—including flow—were experimentally produced, and EEG
data were compared between these states. Furthermore, therelationship between the self-reported flow experience measured
by the psychological scale and the EEG data was examined. These
investigations were expected to reveal clues about the patternof EEG activity characterizing the flow state, which contributes
to developing a real-time objective measurement method of

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METHODS
Participants
A total of 16 right-handed university students (10 males, 6
females; average age: 21.9 ± 1.1 years) participated in this study,
and were paid 1,000 yen per hour. The duration of the experimentvaried from individual to individual (3–4.5 h). The study was
conducted in accordance with the local ethics committee ofKwansei Gakuin University. Under normal circumstances, thelocal ethics committee does not request a full review and any
approval procedure if researchers are following the committee
guidelines. Since this study adhered to the guidelines of thecommittee, no specific approval procedure was carried out. The
instructions for the experimental procedure were provided to theparticipants both orally and in writing, and written informed
consent was obtained prior to the experiment.

Experimental Design
The participants performed mental arithmetic tasks in several
task conditions that differed in terms of calculation difficulty. The
task and experimental manipulation in the present experimentalmost replicated the approach used in a previous study by Ulrich
et al. (2014).
In the employed arithmetic ask, participants mentallysummed the numbers and provided the result, which was
designed to always have three digits. To minimize body
movement, the participants entered the result on the keyboard
displayed on the screen (Figure 1) using a trackball. The trial
was started by presenting the mathematical expression on thescreen; the participants then entered the result digit-by-digit andsubmitted it by pressing the “E” button. There was no feedback
regarding the accuracy of the calculation in order not to cause
differences in motivation due to the ratio of positive to negative
feedback.
Three conditions with different task difficulty were used:
Boredom, Flow, and Overload. The Boredom condition wascharacterized by a low-difficulty task and provided the lesschallenging situation to participants. The Flow condition
employed dynamic adjustment so that the task difficulty was
always adjusted to participants’ level of skill, thus providing asituation with an appropriate level of challenge. The Overload
condition also employed a similar adjustment, while an overhigh-difficulty task was generated. All participants performed thearithmetic task in all three conditions. The task difficulty in eachcondition was determined or adjusted as follows.
The Boredom condition always included two numbers to besummed. The first number was randomly selected from 100 to
109, and the second number was from 1 to 9. To make the taskdifficulty constantly low, the summation of these two numbers
was always between 101 and 110.

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EEG Correlates of the Flow State

were employed, and these sequences were counterbalanced
across participants.
Prior to the experiment, the participants performed two
preliminary tasks to familiarize them with using a trackball and tomeasure their level of skill for the arithmetic task. In the former
task, the participants performed the arithmetic task with the taskdifficulty used in Boredom condition for 5 min. In the latter
task, the participants performed the arithmetic task used in Flow
condition for 5 min, and the level of calculation of the individual
participants were identified as an average task difficulty derived
from the last 25% calculations.
Since it is desirable to maintain constant time intervals
between the onset of arithmetic tasks when analyzing EEG data,a trial protocol was changed from that used in the previous
study. More specifically, the time interval between entering thelast result and the presentation of the current mathematicalexpression was varied to keep the duration of single trials
constant. The preliminary experiment with five participants
investigated the influence of this modification of trial protocol
and confirmed that it had no effect on the subjective ratings. Eachtask block always included eight trials due to this modification.
The first trial in each block was regarded as a preparatory step forthe participants to enter the flow state and was excluded from theanalyses.

Measurement

FIGURE 1 | On-screen keyboard used in the experiment.

As the performance data of the arithmetic task, the calculation
results submitted in each trial were recorded. Furthermore,
the time duration from the presentation of the mathematicalexpression to the entering of the results was measured to examine
the time required by the participants for the calculation.
After each task block, the participants were asked to complete
the questionnaire shown in Table 2 that measured flow state inthat block. This questionnaire consisted of nine items used by
Ulrich et al. (2014) and two items of perceived challenge and goalthat were suggested to be the components of the experience offlow. Ten items were rated on a seven-point Likert-scale ranging
from 1 (“I do not agree at all”) to 7 (“I completely agree”).
Participants responded to one item that assessed their subjective
sense of time on a visual analog scale ranging from 0 (“veryshort”) to 100 (“very long”).
The EEG data were recorded throughout the task using the
BioSemi Active-Two system (BioSemi Inc., The Netherlands). A
whole-scalp, 64-channel active electrode EEG array electrodes
according to the International 10/20 EEG system and extra 6channel set on the face collected EEG data at a sampling rate of1024 Hz.

In the Flow condition, the task difficulty was automaticallyand continuously adjusted in each trial to fit participants’ skills.
Each trial started with a task difficulty that suited participants’levels of skill identified in advance. The adjustment of taskdifficulty was made based on the results of the last two tasks.
When the last two results were correct, the task difficultyincreased in two ways as described below. If the last summandof the latest mathematical expression had one digit, the lastsummand in the next calculation changed to a two-digit number
so that task difficulty increased. In the case of the last summandwith two digits, the next calculation was added by an additional
one-digit summand. For every single adjustment, either of thesetwo changes was applied. Thus, these two types of adjustment
occurred alternatively. On the other hand, when two successive
results were wrong, the task difficulty decreased in the reverse
manner of the above-mentioned method.
In the Overload condition, the initial task difficulty was threelevels higher than that in the Flow condition. The task difficultyincreased when at least three out of the last five trials had correct
results, and decreased if at least four out of the last five results
were incorrect. The task difficulty did not fall below the initiallevel.
The experiment included three task blocks for each task
condition as well as three Rest blocks. The trial was repeated foreach task block that lasted 184 s. The Rest blocks were introduced
to measure the resting state where no mathematical problem waspresented and the participants maintained a gaze fixation. To
eliminate the effect of block order, two types of block sequence

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Analysis
As for performance data, the percentage of correct answers andresponse time were averaged for each task condition for eachparticipant. The effect of the task condition on these two typesof performance data was examined using analysis of variance.
Regarding subjective experience, rating scores on each itemof the questionnaire measuring the state of flow were averaged
across the three blocks of each task condition. Differences
between each pair of task conditions were examined for each item

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EEG Correlates of the Flow StateTABLE 2 | Items for measuring subjective flow experience.

RESULTS

Item 1

I would love to solve math calculations of that kind again

Behavioral Data

Item 2

I was strongly involved in the task

Item 3

I was thrilled

Item 4

The task was boring

Item 5

I had the necessary skill to solve the calculations successfully

Item 6

Task demands were well matched to my ability

Item 7

During the task, I was concerned with other task-irrelevant issues

Item 8During the task, my consciousness was completely focused on solving
the math calculations

Item 9The task was challenging

Item 10

I was heading toward the goal

Item 11

Subjective experience of time

The performance of mental arithmetic task was assessed in termsof the percentage of correct answers. The average percentage
of correct answer was 99.7% in the Boredom condition, 54.4%
in the Flow condition, and 16.9% in the Overload condition.
A significant effect of task condition was seen [F (2, 30) = 476.2,
p < 0.001], and the results of the multiple comparisons using
Bonferroni method ensured significant differences between all
the conditions (p < 0.001 for all pairs). The averages of the
response time were 5.5 s in the Boredom condition, 14.8 s in the
Flow condition, and 17.2 s in the Overload condition. Significant
differences between the conditions were also revealed for the
response time [F (2, 30) = 613.7, p < 0.001], and pair-wisedifferences were significant (p < 0.001 for all pairs).
The influence of task conditions on participants’ subjective
rating was investigated. Individuals’ rating data were averaged
across the three blocks of each task condition with respect toeach item. Analyses of variance including task condition as afactor were conducted separately on 11 items. The results areshown in Figure 2. The significant effects of task condition were
found for all the items [minimum F (2, 30) = 4.076, p < 0.05].
Multiple comparisons using Bonferroni method revealed similar
trends for items 2, 8, 9, and 10. That is, the ratings for these
items in the Boredom condition were significantly lower thanthose in the other two conditions. Items 4, 5, 7, and 11 showed
an opposite trend, in which the ratings were significantly higher
in the Boredom condition than in the other two conditions.
Additionally, for item 5, the rating in the Overload condition
was significantly lower than that in the Flow condition. For items
1, 3, and 6, the ratings were the highest in the Flow condition;
however, significant differences between task conditions wereobserved only for item 1 (Boredom vs. Flow) and item 6 (Flowvs. Overload).
A factor analysis using the maximum-likelihood method andpromax rotation was performed on 10 items excluding item
11 that asked about the subjective sense of time. The analysis
extracted two factors based on the Kaiser-Guttman criterion,
which explained 70.0% of the total variance. The first factor
includes the items concerning task demand (items 4, 5, 9) andthe items describing immersion into the task (items 2, 7, and
8). In particular, item 5 (“I had the necessary skill to solve thecalculations successfully”) showed a strong relation solely to thisfactor, suggesting that it can be interpreted as a factor of the levelof challenge. The second factor includes the antecedent conditionof flow (item 6), the positive experience during task execution(items 3 and 10), and the consequent motivation (item 1). In
particular, since item 6 (“Task demands were well matched to
my ability”) had a moderate association with this factor alone,it seemed adequate to interpret this factor as a factor of balance
between challenge and skill (Table 3).

using analysis of variance. Furthermore, to investigate the aspectsof flow measured by the employed questionnaire, a factor analysis
was performed on the rating data.
EEG data were analyzed using EEGLAB (Version 13.4.4b)
and FieldTrip (Build 20140522) working on MATLAB (Version
R2013b, the MathWorks, Inc., Massachusetts, USA). The EEG
data were down-sampled to 256 Hz, filtered with 1 and100 Hz bandpass. An independent component analysis was
performed on the EEG data to identify independent components
corresponding to noises that were to be manually rejectedusing visual inspection. Additionally, the Laplacian filter wasapplied. Each trial was extracted from the preprocessed data, andtrials including eye blinks and noisy channels where potentials
exceeded ±100 µV were discarded. The averaged rejection rate
was 2.2% (SD = 4.2) for all the participants.
A time-frequency analysis was applied to the data from eachtask block. The analysis was performed using Morlet wavelet
analysis available on the FieldTrip toolbox. A total of 7 cycles
determining the width of wavelets was used. In this analysis, thewavelet’s center frequencies range from 1 to 100 Hz in steps of
1 Hz, and the time window was moved from 10 s before to 25 safter the trial onset in steps of 5 ms. The analyzed data wereaveraged for each task condition for each participant.
Based on the averaged data, four frequency ranges wereextracted (delta: 1–3 Hz, theta: 4–7 Hz, alpha: 10–13 Hz, beta: 14–
30 Hz). Separate ROI analyses were conducted focusing sevenareas (Frontocentral: AFz, Fz, FCz; Left frontal: AF3, F3, F7,
FC3; Right frontal: AF4, F4, F8, FC4; Left central: C3, C5,
CP3, CP5; Right central: C4, C6, CP4, CP6; Left occipital: P3,
P5, PO3, PO7; Right occipital: P4, P6, PO4, PO8) for eachfrequency range. The data of each trial included the EEG data
after answering the mathematical problem. To focus on EEG
activity while solving the computational problem, amplitudes
during the interval from 1 s after the trial onset to average
response time in each condition (Boredom condition: 5.5 s, Flow
condition: 14.8 s, Overload condition: 17.2 s after the trial onset)
were averaged for each area for each frequency range. The
amplitudes in each task condition were assessed in terms of theamount of changes based on the data from the Rest blocks. To test
the effect of task condition on amplitudes, analysis of variance
was performed.

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EEG Data
As a result of examining the effect of the task condition on
the four EEG rhythms in the seven areas, all the EEG rhythms
showed a significant effect of the task condition in one or

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EEG Correlates of the Flow StateFIGURE 2 | Comparison of the ratings of subjective flow items in the Boredom, Flow. and Overload conditions. Error bars indicate standard error. *p < 0.05.

alpha in the right center, and beta in the left occipital—correlations between the amplitude of the EEG activities
and subjective rating data of flow were examined (Table 4).
When calculating correlation coefficients, data were pooled
over conditions and individuals. The amplitudes of the thetaand alpha activities in the frontal area showed a similar
pattern of significant correlation (including correlation withtrend toward significance) as a whole (except for the alphaactivity at the left frontal). In particular, items 2, 8, 9, anda factor of the level of challenge showed positive correlations
with many EEG rhythms. On the other hand, items 4, 5,
7, and 11 showed negative correlations with many rhythms.
Item 10 showed a positive correlation only with the theta
activities in frontal areas and item 1 and a factor of balance
between challenge and skill showed a positive correlation
only with the theta activity in the left frontal area. Items
3 and 6 had no correlation with any of the EEG rhythms.
The left occipital beta activity tended to be different fromother rhythms and showed a negative correlation only withitem 9.

TABLE 3 | Result of the factor analysis of subjective flow items.
FactorLevel of challenge

Balance between challenge and skill

Item 8

0.970

0.525

Item 7−0.899−0.502Item 9

0.868

0.667

Item 2

0.853

0.588

Item 4−0.797−0.517Item 5−0.671−0.114Item 3

0.472

0.883

Item 1

0.533

0.815

Item 10

0.670

0.795

Item 6

0.088

0.448

more areas [minimum F (2, 30) = 4.017, p < 0.05]. The resultsof the multiple comparisons using Bonferroni method revealed
several significant differences between the conditions, which
are shown in Figure 3. In both theta and alpha activities,
significant differences were observed between boredom and allother conditions in the frontal areas (p < 0.05 for all pairs),
and the amplitude of the Boredom condition was smaller thanthe other two conditions. In addition, alpha activity at theright central area showed significant differences in all pairs ofconditions (p < 0.05 for all pairs), the Boredom condition showed
the smallest amplitude and the Overload condition showed thelargest amplitude. Furthermore, for the left occipital beta activity,
the amplitude of the Boredom condition was significantly larger
than that of the Flow condition (p <0.05).

DISCUSSION
Behavioral Data
In this research, the experimental paradigm of Ulrich et al. (2014)
was used to experimentally evoke flow and other conditions.
The results of the mental calculation tasks were 99.7% for the
Boredom condition, 54.4% for the Flow condition, and 16.9%
for the Overload condition. Compared to previous research, therate of correct answers in the Overload condition was lower;
however, those of the other two conditions were at similar levels(98.4, 63.2, 31.0%, respectively, in Ulrich et al., 2014). In terms ofsubjective evaluation data, many items showed similar trends toprevious research; for example, the reverse U-shape tendency initems 1, 3, and 6 was also observed in the results of Ulrich et al.
(2014). Based on these facts, the experimental paradigm in thisstudy created a similar task situation as the previous research,

Correlations Between Subjective Flow and
EEG Data
Focusing on the EEG rhythms with significant differences
between the conditions—theta and alpha in the frontal area,

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EEG Correlates of the Flow StateFIGURE 3 | Comparison of the amplitude of EEG activity between the Boredom, Flow, and Overload conditions. Error bars indicate standard error. *p < 0.05.

previous research were included in the factor of balance between
challenge and skill; this also confirmed the agreement between
this study and the previous one regarding the measurement ofsubjective states.

and the subjective state experienced in each condition can also
be regarded to resemble those in the previous research.
To investigate which aspects of flow were measured by thescale used in this research, factor analysis was performed on
subjective evaluation data. The results showed a two-factor
structure, and these factors can be interpreted as the level ofchallenge and the balance between challenge and skill. Eachfactor was a combination of several flow components. The
factor of the level of challenge included aspects related to theperception of task difficulty level and concentration on the task
and is considered to represent a high level of challenge thatinduces holistic participation in the task. The factor of balance
between challenge and skill included different aspects, such as
appropriate task difficulty as an antecedent condition of flow,
positive subjective state during flow, and motivation for activities
in the future, and is considered the factor that expresses the stateof flow comprehensively. Items 1, 3, and 6 that were used to
calculate a composite variable as the representatives of flow in the

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EEG Activity Corresponding to the FlowState
To clarify the EEG activity that is specific to the flow state, thedifferences between the conditions of the amplitude of the EEG
activities were examined using analysis of variance. As a result,significant differences between the conditions were observed in
the theta and alpha activities at the frontal area, the alpha activity
at the right central area, and the left occipital beta activity. Amongthem, the left occipital beta activity might be an artifact caused by
the effects of bodily movement, which could not be completely
eliminated; therefore, only the theta and alpha activities are
discussed below.

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EEG Correlates of the Flow StateTABLE 4 | Correlations between EEG rhythms and subjective flow items (bolded = p < 0.05, bolded italic = p < 0.10, corrected with false discovery rate method).
Theta
Fronto-central

Left frontal

Alpha
Right frontal

Fronto-central

Left frontal

Beta

Right frontal

Right central

Left occipital

Item 1

0.281

0.339

0.263

0.112

−0.098

0.018

0.142

−0.221

Item 2

0.251

0.436

0.249

0.347

0.222

0.463

0.339

−0.169Item 3

0.101

0.210

0.085

0.108

0.048

0.089

0.221

−0.089Item 4–0.375–0.430–0.325–0.309−0.086−0.273−0.2420.278

Item 5–0.320–0.409–0.329–0.495−0.248–0.520–0.3770.192

Item 6

0.173

0.117

0.162

−0.023−0.119−0.088−0.143−0.040Item 7−0.207–0.321−0.182–0.386−0.267–0.389–0.2950.092

Item 8

0.304

0.391

0.278

0.377

0.171

0.364

0.210

−0.233Item 9

0.402

0.475

0.347

0.365

0.117

0.339

0.285

–0.320Item 10

0.422

0.353

0.346

0.269

0.023

0.155

0.184

−0.229Item 11–0.325–0.327−0.253–0.314−0.126–0.324–0.306

0.214

Level of challenge

0.344

0.435

0.304

0.407

0.192

0.402

0.278

−0.238

Balance between challenge and skill

0.269

0.325

0.230

0.156

−0.005

0.094

0.192

−0.189less cognitive control, and a lower frontal and right central alphaactivity suggesting a lower load on the working memory. Second,
the results suggested that the alpha activity may clarify thedifference between the Flow and Overload conditions. The Flow
condition was characterized by lower alpha activity compared to
the Overload condition. Putting together the data on the alphaand theta activity, as the EEG indicators that are specific to theflow state, the combination of a high theta activity suggestinga high level of cognitive control and immersion in a task witha moderate alpha activity suggesting a relatively low load on
working memory may be effective.

The frontal theta activity showed significantly smaller
amplitude in the Boredom condition compared to the otherconditions, and showed the same level of activity in the Flow andOverload conditions. The frontal theta activities are called the FM
theta, and have been shown to be related to cognitive control(Cavanagh and Frank, 2014) and concentration (Lagopoulos
et al., 2009). Since flow occurs in a situation in which the
maximum ability of an individual is required, it is considered thatmaximum cognitive control is manifested in the state of flow.
In addition, immersion in a task is an important component offlow. Therefore, it is considered that the high theta activity in the
Flow condition reflected these subjective states accompanying theflow experience. However, a high level of cognitive control anddeep commitment to the task are also applicable to the Overload
condition; this may be the reason there was no difference between
the Flow and Overload conditions.
On the other hand, the alpha activity at the frontal and rightcentral areas showed a tendency to increase gradually based on
the degree of task difficulty. Especially, at the right central area,
there were significant differences in the alpha activity not only
among the Boredom condition and the other two conditions,but also between the Flow and Overload conditions. It has been
pointed out that the increase in alpha activity in the parietooccipital area is related to the load on working memory (Tuladhar
et al., 2007), and it is possible that the observed alpha activity
in this study may reflect the difference in the load on working
memory that arose from the different task difficulty levels. Unlike
the result of the theta activity, which suggested that the similarlevel of cognitive control to the Overload condition occurredin the Flow condition, the alpha activity seemed to reflect thetask difficulty per se that brought about a high level of cognitive
control, which made it possible to distinguish the Flow andOverload conditions.
In summary, first, the differences in EEG activity between
the Boredom condition and other conditions were clearly
demonstrated. Specifically, the Boredom condition wascharacterized by a lower frontal theta activity, suggesting

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Correlation Between EEG and Subjective
Rating Data
As a result of the correlation analysis of the subjective evaluationof flow and EEG data, all EEG rhythms showed a similarcorrelation with the subjective rating data, except for the left
occipital beta activity. The factor of the level of challenge
was correlated with both the theta rhythm and the alpharhythm, which can be interpreted in terms of the factor being
representative of evaluation of task difficulty (“The task wasboring,” “I had the necessary skill to solve the calculations
successfully,” and “The task was challenging”) and immersion
state (“I was strongly involved in the task,” “During the task, I
was concerned with other task-irrelevant issues,” and “During thetask, my consciousness was completely focused on solving themath calculations”). It is thought that the degree of task difficulty
indirectly brought about cognitive control and immersion in
the task indicated by the theta activity, and directly affected theload on the working memory indicated by the alpha activity.
Therefore, these tendencies are consistent with the interpretation
obtained from the comparison of the differences in conditions.
On the other hand, only theta activity correlated with thefactor of balance between challenge and skill that included item 1,“I would love to solve math calculations of that kind again,” anditem 10, “I was heading toward the goal.” It has been reported
that the power of the frontal theta activity increases when

8

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EEG Correlates of the Flow Statemeasurements may yield fruitful results as an attempt to clarify
the relationship between the behavioral aspects and the state ofinternal processing of flow.

the working memory is manipulated successfully (Itthipuripat
et al., 2013), and it is considered that this EEG rhythm isrelated to not only cognitive control itself, but also its smooth
performance. Given that the factor of balance between challenge
and skill and its representative items (1 and 10) are related tothe positive experiences in the appropriate condition of the levelof challenge, it would be reasonable that this factor showed apositive correlation with the theta activity.

Problems and Implications for Future
Research
When interpreting the results of this research, it is possible topoint out the differences in the trial time course among thetask conditions. In the experimental paradigm of this study,
several different phases can be identified in the trial, which are(a) from the time the calculation problem is presented untila participant completes the mental calculation, (b) from thetime a participant starts the input of the mentally calculated
answer until the “E” button is pressed and the answer issubmitted, and (c) from the time the answer to a problem issubmitted until the next computational problem is presented.
Such a task structure is inevitable in the experimental paradigm
using this mental arithmetic task; however, this may haveinfluenced the difference in physiological responses among theconditions. For example, the time until a participant completesa mental calculation task differs depending on the condition,
and as a result, the proportions of each phase in the time
length of one trial run also varied depending on the task
condition. Due to the high temporal resolution of EEG, thedifferences between the conditions may have been reflected
strongly and may have caused unexpected fluctuations in thedata.
By addressing these issues in future research, it would bepossible to clarify the indicators of EEG activities that are specificto the Flow condition. For example, it will be possible to identify
the transition between the phases during mental calculation
tasks and accurately measure the EEG activity of each phase. It
will also be effective to use tasks that do not have qualitatively
different phase transitions during trials. In addition to the mental
calculation tasks used in this research, several experimental
paradigms for evoking flow in experimental environment havebeen proposed. Among these paradigms, tasks that use a gaming
framework seem to be promising because they do not necessarily
call for a change in the task structure during a single trial.
However, in the case of using video games as a task, there isa risk that bodily movement is produced when playing a gameaffects EEG data; therefore, it is necessary to develop tasks with aminimal amount of bodily movement. Furthermore, in previous
studies using video games, task difficulty was not automaticallyand continuously changed based on the performance of aparticipant. It is, therefore, necessary to develop a method thatenables such adjustments.
In terms of physiological measurements, this research used
only EEG. However, the other physiological indicators are
considered to be effective tools for establishing objective
indicators of the state of flow. de Manzano et al. (2010) reported
a positive correlation between the activity of zygomatic major
muscle and flow. They regarded this muscle activity as a reflectionof positive emotional experiences accompanying the state of flow.
On the other hand, although not shown to have any relationshipwith flow in their results, negative emotional experiences of

Objective Measurement of Flow State
Using EEG Activity
Compared to the study by Ulrich et al. (2014) using fMRI,
the present study seems to provide complementary findings.
They found indices of brain activity correlated with a single
psychological measure that is at its peak under the Flow
condition. On the other hand, the present study found several
indices of EEG activity correlating with different aspects of thesubjective rating of the flow, and may collectively discriminate
all of the conditions of the Boredom, Flow, and Overload.
With respect to the association with brain functions, Ulrich
et al. (2014) connected their indices with neural activities thatreflect positive experience in the flow state, that is, deeper
sense of cognitive control and decreased negative emotions.
The EEG indices found in this study do not seem to reflectemotional outcome in the flow state, but rather the state ofcognitive activity (the level of cognitive effort and cognitiveload) during task execution. Taking all of the above into
consideration, Ulrich et al. (2014) and our research may provide
neurological indicators corresponding to different aspects of theflow experience.
Since this study used only a mental arithmetic task, it isunclear whether the identified EEG activity will work as anindex of flow in other types of activities. However, the findingsof this research provide plausible clues about the features ofinternal processing while flow is experienced in a given task,that is, the state in which holistic involvement in the task can beachieved without adverse effects of stress. Specific EEG activities
corresponding to flow among various tasks may differ from eachother; however, the features of internal processing reflected bythose EEG activities may share the properties mentioned above.
On the other hand, it should be noted that the results of thisstudy do not fully explain the contents of flow that will be
experienced in the fields of more complex activities. According
to Biasutti (2017), musicians in the flow state challenge newattempts and their experiences often involve discovery. It is
unlikely that these aspects of the flow state were experienced in
the task used in this research, therefore, it is expected that EEG
studies targeting more creative activities will be conducted in the
future.
Objective measurements of the flow state using EEG activity
can be useful, especially when behavior is not expressed at an
observable level. However, the necessity for an observational
approach remains because it is expected that the activities thatenable EEG measurement without disturbing the occurrence offlow are limited, especially when the participants are children.
Future studies employing both observational methods and EEG

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EEG Correlates of the Flow StateAUTHOR CONTRIBUTIONS

non-flow state were expected to be reflected by the corrugatorsupercilii muscle. If negative or positive emotional experiences
during the task are actually reflected in these muscular activities,
these may provide effective indicators to distinguish Flow andOverload conditions, which have not been clearly distinguishedusing EEG measurement in this research.

Conceptualization: KK and NN. Data curation: YY, HO,
CY, and SN. Formal analysis: KK, YY, HO, CY, and SN.
Funding acquisition: KK and NN. Investigation: YY, HO, CY,
and SN. Methodology: KK, HO, CY, SN, and NN. Project
administration: NN. Resources: KK and NN. Software: YY, HO,
and CY. Supervision: NN. Visualization: KK, HO, and CY.
Writing–original draft: KK. Writing–review and editing: YY
and NN.

CONCLUSION
This research attempted to clarify the EEG activity corresponding
to the Flow condition using mental calculation tasks, inwhich task levels were dynamically adjusted to evoke flow.
In conclusion, theta activities in frontal areas were identified
to be related to a high level of cognitive control and theimmersion aspect of flow. Furthermore, in the flow state, we
observed suppressed alpha activity in the frontal and central
areas, suggesting that the load on the working memory was notexcessive. The revealed EEG activity suggests that a combinationof these activities may distinguish the state of flow from otherstates such as boredom and overload. However, the resultsof this study are insufficient to establish objective indicators
to quantify the strength of flow. Therefore, future research isneeded to examine more diverse data and identify the featuresof physiological activity in the flow state beyond the types of task.

FUNDING
This work was supported by the Japan Society for the Promotionof Science (Grant Number JP15H05347), and partially by MEXTSupported Program for the Strategic Research Foundation atPrivate Universities, 2014–2018 (Grant Number S1411038) andby the Center of Innovation Program from Japan Science andTechnology Agency, JST.

ACKNOWLEDGMENTS
We are grateful to Dr. Kae Nakajima for her contribution to
enabling this research.

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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2018 Katahira, Yamazaki, Yamaoka, Ozaki, Nakagawa andNagata. This is an open-access article distributed under the terms of theCreative Commons Attribution License (CC BY). The use, distribution or
reproduction in other forums is permitted, provided the original author(s)and the copyright owner are credited and that the original publication inthis journal is cited, in accordance with accepted academic practice. No use,
distribution or reproduction is permitted which does not comply with theseterms.

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