Flow and Optimal Difficulty in the Portable EEG: On the Potentiality
of using Personalized Frequency Ranges for State Detection
Michael T. Knierim, Mario Nadj and Christof Weinhardt
Institute of Information Systems and Marketing, Karlsruhe Institute of Technology, Fritz-Erler-Str. 23, Karlsruhe, Germany
Keywords: Flow, Optimal Difficulty, Workload, Attention, Frequency Separation, Portable EEG.
Abstract: The experience of flow has been centrally linked to peak task performances and heightened well-being. To
more effectively elicit these outcomes, flow is increasingly studied using neurophysiological measures. For
example, portable EEG is employed to enable automatic state detection required for adaptive system design.
However, so far, there is a lack of highly diagnostic findings, and moderately diagnostic ones relate more
strongly to a central flow pre-condition namely optimal task difficulties. Unfortunately, even these metrics
might be infeasible in real-world scenarios and for portable EEG systems without midline electrodes. In this
work, we discuss how frequency band personalization and separation could provide options to overcome these
problems. Results from an experiment with a task manipulated in difficulty highlight that upper Alpha and
Beta ranges show differentiating patterns to their lower frequency counterparts (i.e. within bands). These sub-
bands could be used to detect instances of higher flow and optimized difficulty using portable EEG.
1 INTRODUCTION
Flow, the experience of effortless attention, peak task
performance and heightened well-being is deemed an
individually beneficial experience and also a
desirable state from an organizational perspective
(Ceja and Navarro, 2012). As the requirements for
flow are complex, flow facilitation at work is still a
central challenge (Ceja and Navarro, 2012). One
promising avenue to study flow and to advance the
development of flow-adaptive support systems is to
use electroencephalographical (EEG) measures
(Cheron, 2016; Harris et al., 2017). EEG provides
comparatively low cost, high portability (e.g. wireless
EEG headsets) and high temporal resolution
(Blankertz et al., 2016). Nonetheless, empiric EEG
results regarding flow have so far been short and have
shown conflicting results. For example, opposing
patterns of frontal Alpha activity with increased flow
have been reported (e.g. Berta et al., 2013; Léger et
al., 2014; Ewing et al., 2016; Katahira et al., 2018).
Overall, so far, no robust neural marker of flow has
been identified, despite shared conceptions that a
distinctive experience like flow ought to have some
representative underlying neural configuration
(Cheron, 2016; Harris et al., 2017). One of the closest,
yet not sufficient approaches to identify intensified
flow comes from the detection of situations with
moderate task demands. This is derived from research
on mental workload and the reverse inference that a
task with too low or too high demands is unlikely to
elicit flow (Csikszentmihalyi, 1996). The diagnostic
potential is given by observations of frontal midline
Theta increases with task demands, or by posterior
Alpha reductions with increasing task demands
(Borghini et al., 2014). Despite these advances, the
transferability of laboratory findings particularly for
flow is still limited, in particular because some of
the aforementioned features show limitations through
confounds with prolonged task exposure (e.g. Theta
power increases with task duration), natural behavior
(e.g. posterior Alpha blocking through visual
stimulation), and topographical localization (e.g.
Theta changes are strongest over midline positions).
The latter is important, as portable EEG is often using
few electrodes in non-uniformly distributed positions.
To extend the work on unobtrusive, automated
flow detection through portable EEG devices, we
propose that through a refined frequency separation
approach, (1) refined empiric contributions can be
made to the research on flow neurophysiology, (2)
new avenues to observe the concept of neural
efficiency during flow are opened, and (3)
alternatives to the prominent Theta and Alpha
markers for mental workload can be derived.
Knierim, M., Nadj, M. and Weinhardt, C.
Flow and Optimal Difficulty in the Portable EEG: On the Potentiality of using Personalized Frequency Ranges for State Detection.
DOI: 10.5220/0008362601830190
In Proceedings of the 3rd International Conference on Computer-Human Interaction Research and Applications (CHIRA 2019), pages 183-190
ISBN: 978-989-758-376-6
Copyright
c
2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
183
2 THEORETIC BACKGROUND
Flow research has found the state to occur remarkably
similar across numerous contexts like arts, gaming,
work, or sports (Csikszentmihalyi, 1996; Moller et
al., 2010). Flow theory describes the experience along
nine dimensions, that are classified in order of
occurrence (cf. Csikszentmihalyi, 1996 Figure 1).
Figure 1: Flow Theory.
Among the antecedents, the optimal balance
between perceived difficulty and an individuals’
skills has played a major role in explaining how
experiences might range from boredom in very easy
tasks to anxiety in very hard tasks (Csikszentmihalyi,
1996). Due to its central place in flow theory, the
manipulation of a task’s difficulty, has been primarily
employed for the experimental flow elicitation
(Moller et al., 2010), as it has been in workload
contexts, albeit not with a focus on optimal
difficulties. Markers from workload research have
already been employed in flow research and have for
example led to conclusions of increased frontal Theta
levels in flow (Katahira et al., 2018), potentially even
with a maximum in higher flow (Ewing et al., 2016).
A second stream in neurophysiology research has
focused on elucidating whether or not flow is
represented by neural activity reductions. In one
theoretic instance, it has been proposed that during
flow, neural activity in frontal brain regions might be
downregulated to shift explicit demand processing in
frontal regions to implicit and automated processing
of learned behaviours in posterior regions (Transient
Hypofrontality Dietrich, 2004). While this approach
has been criticized to be overly simplistic (Harris et
al., 2017), extensions of a reductionist understanding
have proposed that still, some brain regions would
show reduced activation during flow, as only areas
crucially necessary to meet the task demands would
be activated. Such an optimization could make a way
to experience highly automated and error-free task
processing (Neural Efficiency Hypothesis (Cheron,
2016; Harris et al., 2017). Following both
propositions, in the EEG Alpha band activity, as the
prototypical inhibitory rhythm during wakefulness,
could be the measure of choice to identify neural
configurations during flow (Cheron, 2016). In
relation to this, while some studies find increased
Alpha power with higher flow self-reports (Léger et
al., 2014), within the difficulty manipulation (DM)
group comparison studies, results point more to
Alpha activity decreases with increasing task
difficulty (Ewing et al., 2016; Katahira et al., 2018
report the inverse relationship, but use amplitudes as
unit of analysis). This highlights that there is still
much to uncover to explain Alpha patterns in flow.
In this article it is argued, that the refined
specification of frequency band ranges might explain
some of the previous differences and could open new
avenues with additional explanatory potential and
higher robustness of findings. Often in EEG research
and in flow EEG research in particular, frequency
band ranges are extracted using generalized, broad
ranges (e.g. Theta 4-7.5 Hz or Alpha 7.5-12.5 Hz),
despite evidence, that such generalized ranges can
mask frequency specific changes (Klimesch, 1999).
Importantly, evidence from laboratory experiments
has highlighted that Alpha band components can
show different and even sometimes opposing patterns
(Klimesch, 1999). For example, by segmentation of
personalized Alpha bands into three 2 Hz wide
subcomponents, lower Alpha bands (Lo1 and Lo2)
have been found to relate to general attentional
demands and alertness (over the whole scalp) and the
upper Alpha has been found to react to changes in
task-specific processes (in topographically restricted
regions) (Klimesch, 1999).
As flow is not only repeatedly associated with
cognitive demands in the form of working memory
recruitment (i.e. Theta range activity), but also often
in relation with attentional processes (Harris et al.,
2017), it would seem of high interest to employ Alpha
band segmentation to not only identify regions of
reduced neural activity, but perhaps even identify
changes in global changes in attentional demands,
and task-specific pattern changes. The need for band
personalization has already been acknowledged in
flow research (Berta et al., 2013; Ewing et al., 2016),
yet no extensive evaluations have been completed, for
example studying personalized, narrow, and multiple
sub-bands. Such approaches would seem however to
cover several conceptually related processes of flow
like workload, attention and neural efficiency.
Difficulty – Skill Balance
Clear Goals
Unambiguous Feedback
Focused Concentration
Merging of Action & Awareness
Loss of Self-Consciousness
Perceived Control
Distortion of Time
Cognition (e.g., Ease of Use)
Affect (e.g., Enjoyment)
Behavior (e.g., Actual Usage)
Consequences
Flow
Experience
Antecedents
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3 METHOD
In the herein presented study, the adaptation of a
mental arithmetic DM task was chosen due to its pre-
validated nature in flow research (Katahira et al.,
2018). In this math task, the DM can be easily
achieved by increasing or decreasing the number of
digits that have to be summed in a fixed time frame
(here 28s per trial with 4s breaks between trials).
The final result is in all cases a three-digit number,
yet the number of digits is adapted based on
performance (cf. Table 1). In the EASY condition,
very simple equations are shown, that could only have
the result 303 or 304. In the HARD condition, very
hard equations are shown that always are at minimum
9 levels higher (i.e. 9 digits more) than the level that
is calibrated to have the optimal difficulty for each
participant. Finally, in the OPTIMAL-Calc.
condition, the equations are of a moderate difficulty
as determined by an early calibration phase. Lastly, in
the OPTIMAL-Chos. condition, participants can
select the optimal task difficulty themselves.
Table 1: Examples of the Math task Difficulties.
EASY
(Level = 0)
OPTIMAL
(Level = 2)
HARD
(Level = 16)
100 + 1 +
100 + 1 +
100 + 2
100 + 13 +
100 + 22 +
100 + 3
100 + 35 + 22 + 16 + 2 +
100 + 64 + 45 + 26 +
100 + 25 + 51 + 31
Study participants were sampled from a public
student pool and received a compensation of 22 Euro.
After a preparation phase (consent, sensor
attachment, 5min eyes open resting and 1min eyes
closed resting), an introduction to the math task was
shown and participants could practice the task using
the EASY condition. Afterwards, the task was shown
in the OPTIMAL condition starting at level 1 in order
to calibrate the optimal difficulty. Next, all four math
task conditions were presented in randomized order
(Williams Design). The preparatory and all following
task conditions lasted for ca. 5 minutes. After each
condition, self-reports were collected for perceived
difficulty (1 item) and flow (10 item flow short scale
FKS both instruments by Engeser and Rheinberg,
2008). EEG data was collected with a saline-based
14-channel Emotiv Epoc+ headset (256Hz sampling).
Data was collected for 41 participants. Data from
two participants who repeatedly failed control
questions in the surveys were removed. For all self-
report variables, outliers were removed (>2 standard
deviations SD from the construct mean). Next,
Cronbach’s Alpha coefficients were inspected for the
flow construct and found acceptable after one item
was removed (0.80). The distribution normality was
assessed (univariate Shapiro-Wilk tests) and
supported for all conditions.
Table 2: EEG Data Pre-processing Pipeline.
Data Preparation (R)
Step
Parameters
Data
Extraction
Baseline & Task
Phases
Channel
Centering
Channel Mean
Subtraction
Signal Processing (Matlab)
Step
Parameters
Line Noise
Removal
50 Hz & 100 Hz
Re-
Referencing
Robust Common
Average Reference
Detrending
1 Hz High-Pass
Trim Outliers
800mV / 250ms
Channel &
Paroxysmal
Artefact
Removal
Artefact Subspace
Reconstruction
(ASR) - Burst
Criterion 10 SD
Stationary
Artefact
Removal
(Independent
Components)
AMICA - ICs:
[Horizontal &
Vertical Eye
Movement, Blinks,
Discontinuities]
Feature Extraction (R)
Step
Parameters
Processing
Inspection
Visual Comparison
of Input & Output
Frequency
Power
Extraction
(Morlet
Wavelets)
55 Frequencies,
Range [3, 60],
Wavelet Cycle
Range [3,10] log.
spaced with
frequencies
dB Power
Conversion
10*log10(mV
2
/Hz)
Frequency
Band
Extraction
Theta, Alpha &
Beta Bands from
IAF Peak
Change Score
Computation
= Task Eyes
Open Baseline
EEG data was processed primarily along the
guidelines of Cohen (2014). 7 data sets had to be
excluded due to recording errors, insufficient data
quality, or lack of survey report data. The retained,
complete sample for EEG analysis comprised 34
participants. Data preparation, feature extraction, and
analysis were conducted in R, signal processing and
Flow and Optimal Difficulty in the Portable EEG: On the Potentiality of using Personalized Frequency Ranges for State Detection
185
artefact removal in Matlab (EEGLab). The automated
EEG data preparation process is outlined in detail in
Table 2. Signal data was additionally screened before
and after signal processing to ensure no critical errors
occurred in the pipeline. Parameters for the
processing steps were tuned for the Epoc+ headset.
For almost all feature aggregation steps median
averaging was used to reduce the impact of outliers in
the data (Cohen, 2014).
Frequency bands were extracted following
Klimesch (1999). To account for inter-individual
differences, individualized Alpha frequency (IAF)
peaks were identified. As Alpha is known to also vary
regionally (being slower at anterior sites), yet as not
all participants showed such clear peaks for all sites,
a global IAF maximum was determined as lying 0.5
Hz below the occipital Alpha maximum during an
eyes-closed resting phase (cf. Figure 2).
Figure 2: PSDs for one participant during an eyes-closed
resting phase with pooled anterior (AF3, AF4, F3, F4, F7,
F8, FC5, FC6), temporal (T7, T8), parietal (P7, P8), and
occipital electrodes (O1, O2). Dots show regional peaks.
Figure 3: Grand Average PSD for all participants during an
eyes-closed resting phase with all 14 electrodes pooled,
demonstrating the frequency band decomposition into
narrow Theta, Alpha and Beta bands.
Based on this IAF, 2 Hz Theta and Alpha sub-
bands were extracted (cf. Figure 3). To extend the
personalized and band-refined approach, the Beta
band was similarly decomposed. In line with previous
research that has extracted low, mid, and upper Beta
bands with 3 Hz, 5 Hz, and 10 Hz ranges respectively
(Berta et al., 2013), the previous IAF-based
decomposition was continued using these ranges.
4 RESULTS
In the following analyses, one-way repeated measures
analyses of variance (ANOVA) with Greenhouse-
Geisser (GG) correction were used to assess main
effects, followed up by pairwise Welch’s t-Tests with
Benjamini-Hochberg (BH) correction. Error bars in
all figures show standard errors. To check the
manipulation success, perceptions of task difficulty
and flow are evaluated. For task difficulty, a
difference between the conditions is found (F(3, 102)
= 161.81, p < .01,
2
G
= .71) with stepwise increases
in perceived task difficulty per condition (p < .01).
Also, for flow, a main effect for condition is found
(F(3, 99) = 29.63, p < .01,
2
G
= .30). Follow-up tests
show a stepwise increase in flow from the EASY to
the OPTIMAL conditions, with OPTIMAL-Cal.
being increased from EASY at trend level (p = .085),
and OPTIMAL-Chos. being increased to both former
conditions (i.e. maximal) (p < .01). In the HARD
condition, flow is decreased compared to all other
conditions (p < .01), showing the expected inverted
U-shape pattern of flow with increasing difficulties
(cf. Figure 4). Together, the findings lead to the
assumption, that flow is increased in the OPTIMAL
conditions, with a maximum in OPTIMAL-Chos.
Figure 4: Perceived difficulty & flow reports.
To assess the changes in frequency band activity
over several scalp locations, separate one-way
repeated measures ANOVAs (GG corrected) were
conducted for each available electrode pair (i.e. data
CHIRA 2019 - 3rd International Conference on Computer-Human Interaction Research and Applications
186
were pooled for electrodes at AF3 & AF4 (= AF), F3
& F4 (= F-M), F7 & F8 (= F-L), FC5 & FC6 (= FC),
T7 & T8 (= T), P7 & P8 (= P), O1 & O2 (= O) (cf.
Table 3). All ANOVA significance levels were BH-
adjusted, as were the significance levels in the follow-
up pairwise Welch’s t-tests.
Table 3: EEG Frequency Power ANOVAs Only showing
significant results. Exact p-values when p > .01.
Site
Test Result
Theta (IAF-6 to IAF-4)
T
F(3, 75) = 3.72, p = .0686,
2
G
= .02
Lo2Alpha (IAF-2 to IAF)
P
F(3, 87) = 11.69, p < .01,
2
G
= .04
O
F(3, 90) = 9.24, p < .01,
2
G
= .02
HiAlpha (IAF to IAF+2)
F-M
F(3, 87) = 7.04, p < .01,
2
G
= .03
P
F(3, 84) = 12.86, p < .01,
2
G
= .04
O
F(3, 87) = 14.16, p < .01,
2
G
= .04
Alpha (IAF-4 to IAF+2)
P
F(3, 87) = 11.52, p < .01,
2
G
= .04
O
F(3, 90) = 8.89, p < .01,
2
G
= .02
LoBeta (IAF+2 to IAF+5)
F-M
F(3, 87) = 10.65, p < .01,
2
G
= .05
P
F(3, 90) = 7.70, p < .01,
2
G
= .03
O
F(3, 93) = 11.55, p < .01,
2
G
= .03
MidBeta (IAF+5 to IAF+10)
F-M
F(3, 84) = 3.34, p = .0840 ,
2
G
= .03
HiBeta (IAF+10 to IAF+20)
AF
F(3, 81) = 5.97, p = .0213,
2
G
= .08
F-M
F(3, 81) = 4.03, p = .0524,
2
G
= .05
F-L
F(3, 78) = 7.00, p < .01,
2
G
= .06
FC
F(3, 75) = 5.39, p = .0196,
2
G
= .05
T
F(3, 75) = 10.14, p < .01,
2
G
= .09
P
F(3, 84) = 5.96, p = .0152,
2
G
= .06
O
F(3, 87) = 5.65, p = .0193,
2
G
= .04
Altogether, Theta power shows almost no
difference across the conditions, with only a trend
level effect being present at temporal locations. For
this location, post-hoc tests showed trend level
increases of OPTIMAL-Cal. (p = .0537) and
OPTIMAL-Chos. (p = .0952) from EASY as the only
differences. To complete this assessment, the
neighbouring Lo1Alpha band did not show changes
at any site, and neither did the Theta band when
extracted for a more traditional and non-
individualized 4-7.5 Hz range (all p > .1).
While no effects were found in the lowest Alpha
range (Lo1Alpha), both Lo2Alpha and HiAlpha
showed Alpha suppression at posterior regions (both
at P & O) with similar effects. Namely, a decrease in
Alpha from EASY, with all other conditions being on
the same level (all p < .01). For Lo2Alpha at occipital
sites (p = .0247) and HiAlpha at occipital sites (p =
.0513 - trend level), there was also a lower level in
HARD than OPTIMAL-Cal. indicating that occipital
alpha suppression was somewhat in line with
perceived difficulty increases. In relation, for LoBeta
at posterior sites, power reductions are found. At
parietal sites LoBeta power drops from EASY to
OPTIMAL-Cal. (p < .01) and OPTIMAL-Chos. (p =
.0321). At occipital sites, LoBeta power drops from
EASY compared to all other conditions (all p < .01)
(cf. Figure 5).
Figure 5: Posterior Alpha decreases from EASY are visible
in all Alpha frequencies. Occipital sites show more
sensitivity to difficulty changes.
While the same pattern of posterior Alpha
reductions is also generally visible in the broad 6 Hz
Alpha band (also here with occipital Alpha being
slightly higher at OPTIMAL-Cal. than HARD p =
.0174), the most noticeable difference revealed
through the Alpha split is a reduction of frontal Alpha
power at sites closer to the midline (F-M), that is
visible in HiAlpha, but not in the lower Alpha bands,
nor the broad 6 Hz Alpha band. HiAlpha at F-M is
suppressed in all other conditions when compared to
EASY (all p < .05) (cf. Figure 6). This effect is
identically found in the adjacent LoBeta band (and in
MidBeta at trend level here the post-hoc contrast is
visible for EASY compared to OPTIMAL only and
HiBeta at trend level, p = .0893 and p = .0701 also,
for F-M HiBeta a minimum in OPTIMAL is visible
due to an increase in HARD compared to OPTIMAL-
Cal. p = .0302 and OPTIMAL-Chos. p = .0185).
Furthermore, the (almost complete) absence of
effects in the MidBeta range further highlights the
utility of the narrower frequency inspection. In
particular, HiBeta is found to be increased at AF sites
in HARD compared to EASY (p = .0411), and
Flow and Optimal Difficulty in the Portable EEG: On the Potentiality of using Personalized Frequency Ranges for State Detection
187
OPTIMAL-Cal. compared to EASY at trend level (p
= .0721), at F-L sites in HARD compared to EASY
(p < .01), to OPTIMAL-Cal. (p = .0744 trend level),
and to OPTIMAL-Chos. (p = .0150), with EASY
showing lower power than the OPTIMAL conditions
at trend level (p = .0608 and p = .0.798). At FC sites,
HiBeta is found to be increased in HARD compared
to all other conditions (all p < .05). At T sites, HiBeta
shows an increase from EASY to OPTIMAL-Chos.
(p = .0335) and from there an increase to HARD (p =
.0385). HARD is also higher compared to EASY (p <
.01) and OPTIMAL-Cal. (p = .0335). OPTIMAL
Chos. is found to be higher than OPTIMAL-Cal. at
trend level (p = .0778), which means that temporal
HiBeta slightly indicates a stepwise increase in power
that would be in line with difficulty perception
changes. HiBeta is also elevated in HARD at parietal
and occipital sites when compared to all other
conditions (all p < .05), with the other conditions
being equal. Altogether, this means that HiBeta
mostly reveals maxima during HARD task conditions
over the whole scalp (cf. Figure 7), which represents a
useful contrast to the lower frequency effects, in
particular the Alpha increase at posterior and fronto-
medial sites during the EASY condition.
Figure 6: Fronto-Medial Alpha and HiBeta progressions.
This points to an interesting potential of
combining (Hi-)Alpha and (Hi-)Beta powers, to
identify states of OPTIMAL difficulty. Such
approaches have been previously undertaken on the
Workload Index (Berka et al., 2007), that have also
been employed in flow research (Chanel et al., 2011)
and are traditionally either used for pooled electrodes
over the whole scalp or for central midline electrodes.
For the sake of comparison this Workload Index (WI
= Beta / (Theta + Alpha)) was also computed for
pooled electrodes here using non-individualized
broad bands (Theta = 4-7.5 Hz, Alpha = 7.5-12.5 Hz,
Beta = 12.5-30 Hz). No significant effect was found
in a one-way repeated measures ANOVA using the
difficulty conditions as within-subjects factor.
Figure 7: Whole Scalp HiBeta progressions for better
visibility only the most distinctive patterns are shown (O
was very similar to P and FC very similar to F-L).
5 DISCUSSION AND
CONCLUSIONS
Of particular importance is the finding that through
frequency band separation, HiAlpha suppression in
frontal medial sites emerged independent of LoAlpha
frequencies, similarly to the work by Ewing et al.
(2016). This is an interesting finding that not only
points to the utility of frequency band separation, but
might provide an alternative to detection of increased
task demands in real-world scenarios where posterior
Alpha blocking has been named as a prominent
confounding factor (Blankertz et al., 2016).
Furthermore, through segmentation of the Beta band
it was also found that while a larger similarity is
visible between HiAlpha and LoBeta ranges, the
MidBeta range showed mostly no variation across
conditions, and the HiBeta range primarily showed
increases with very high task demands.
Using the refined approach, we did not find
features that clearly reflect the variation in reported
flow. This is not completely surprising given that
related work has not uncovered such markers using
frequency power comparisons in DM tasks.
Although, here, two observations tentatively indicate
such reactivity, namely temporal Theta and frontal
medial HiBeta activity at trend levels. Both are in line
with previous findings of increased Theta activity
during moderate to high task difficulty (Ewing et al.,
2016), and with documentations of negative
correlations between frontal Beta and flow self-
reports (Léger et al., 2014). Therefore, these results
would appear to warrant further investigation. As an
CHIRA 2019 - 3rd International Conference on Computer-Human Interaction Research and Applications
188
initial proposition, it would be plausible to consider
simultaneous, localized HiAlpha suppression
together with HiBeta reductions as a sign of neural
efficiency. The reasoning behind this thought builds
on the proposition of HiAlpha reflecting task-specific
information processing (Klimesch, 1999), and Beta
reflecting increased local communication (Buzsáki
and Draguhn, 2004). Therefore, HiAlpha suppression
could be indicative of a cortical region being recruited
to process a particular task, while at the same time a
reduction in local Beta would reflect a reduction in
communication among local neuron populations. In a
similar manner as frontal Theta and widespread Beta
increases are considered as coping mechanisms
during (too) hard tasks (Sauseng et al., 2005), a
reduction of local Beta might be indicative of the fact
that local regions are coping well and only recruit
absolutely required neuron groups.
Besides these theoretical potentials, the remaining
present findings show an alternative practical
potential to differentiate levels of perceived difficulty
(and thereby indirectly flow) through feature
combination. In this regard, it should be pointed out
first, that barely any significant changes were
detected in the Theta band. This was an unexpected
finding, as a large amount of literature is available
documenting frontal Theta increases with increasing
task demands (Klimesch, 1999; Borghini et al., 2014).
As to why such a pattern is here only reflected in trend
level temporal Theta power changes, several
explanations are possible. Theta changes have been
documented to occur strongly with prolonged task
exposure (e.g. in studies with airplane simulators
Borghini et al., 2014). By adding the first task round
(the task introduction using the EASY treatment), to
a one-way repeated-measures ANOVA we do find a
difference for frontal Theta at F3 & F4 (F-M) (F(4,
104) = 4.33, p < .01,
2
G
= .05), with the lowest Theta
power in the introduction phase. This points to the
fact that time might have acted as a confounding
effect on Theta power changes. Further, frontal Theta
effects are typically identified in midline positions
(e.g. Fz see Ewing et al., 2016). Thus, as these
electrodes are missing for the Epoc+ headset, it might
not be possible to find Theta effects in some EEG
devices. This is a limitation that could easily occur in
real-world measurement scenarios using portable
EEG systems with few electrodes.
However, employing the frequency separation
approach, an interesting pattern emerged in that some
narrow frequency features showed significant
reaction to changes in perceived difficulty, with some
indicating stepwise, monotonous increases, some
indicating increases with moderate level plateaus for
OPTIMAL conditions and some indicating maxima
for either the EASY or the HARD condition when
compared to all other conditions. The functional
explanation of these patterns should be the subject of
future work as would be the development of sensitive
and robust compound indices, ideally based on more
than one task type (see e.g. Berka et al., 2007).
Presently, it is primarily argued that these patterns
allow to discuss flow related changes in a refined
manner and that they pose interesting alternatives for
the detection of situations of optimal difficulty,
especially in scenarios where less information might
be available than typically is in laboratory setups (e.g.
fewer and unevenly distributed electrodes). When
considering how for example neuro-adaptive systems
employ thresholds to inform adaptation rules (cf. e.g.
Ewing et al., 2016), features indicating maxima
during EASY or HARD conditions could be valuable
indicators, given that they would be subject to lower
variation except in the boundary cases. In this regard
they could firstly be employed to robustly identify
when difficulty is unbalanced and flow unlikely.
In conclusion, this study posits that flow research
could benefit from nuanced frequency power
analyses, in general by identifying Alpha and Beta
power changes that could relate to neural efficiency
(in a local form), and in particular when (portable)
EEG systems are used that lack midline electrodes. In
line with previous research (Klimesch, 1999), the
herein presented initial analyses support the
understanding that a personalized and narrow
frequency power analysis helps to avoid to miss
frequency specific effects. Specifically, this research
contributes to the literature on flow by highlighting
that frontal medial HiAlpha decreases in increased
task difficulty (and HiBeta decreases during
optimally balanced task difficulties) as well as
widespread HiBeta increases in very hard task
conditions provide additional avenues to
automatically and unobtrusively detect boundary
situations to flow. Thus, these metrics, indirectly
allow to improve the identification of situations with
optimal preconditions for flow to emerge.
Importantly, the frequency segmentation approach
would appear to be a valuable alternative when
portable EEG systems are used that don’t include
midline electrodes. As an additional effect, Alpha and
Beta bands appear unaffected by influences from task
exposure durations providing an interesting
alternative to the more established metric of frontal
midline Theta power increases with higher task
demands (e.g. Ewing et al., 2016). Ideally by taking
narrow frequency power analysis into account, future
flow research will thus move closer to identifying
Flow and Optimal Difficulty in the Portable EEG: On the Potentiality of using Personalized Frequency Ranges for State Detection
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robust concomitants and markers of flow that can be
employed in neuro-adaptive systems using portable
EEG in real-world scenarios. Eventually, systems
able to adapt to flow intensities could then reduce
flow interruptions (e.g. by blocking incoming
messages) or provide feedback information to
improve flow self-regulation (e.g. by self-adjusting
task difficulty, or by optimizing arousal levels and
catalysing task focus through EEG-neurofeedback).
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