Automated Detection of Mind Wandering: A Mobile Application
Marcus Cheetham
1,2,3,
, C
´
atia Cepeda
4,
and Hugo Gamboa
4,5
1
University Research Priority Program Dynamics of Healthy Aging, University of Zurich, Zurich, Switzerland
2
Department of Psychology, Nungin University, Seoul, South Korea
3
Center of Competence Multimorbidity, University of Zurich, Zurich, Switzerland
4
Department of Physics, FCT-UNL, Lisbon, Portugal
5
PLUX Wireless Biosignals S.A., Lisbon, Portugal
Keywords:
Biosignals, Mind Wandering, Mindfulness, Attention, Signal-Processing, Stress, Mobile Phone App.
Abstract:
There is growing interest in mindfulness-based training of attention. A particular challenge for novices is
learning to sustain focused attention while ensuring that the mind does not wander. This paper presents the
development of a tool for the automated detection of episodes of mind wandering (MW), on the basis of
biosignals, while normal healthy participants engaged in brief mindfulness-based training (BMT) of attention.
BMT required ve 20-minute training sessions on consecutive days and entailed practice of breath-focused
attention, a typical exercise in mindfulness-based techniques of stress-reduction. Heart rate, respiratory rate,
electrodermal and electromyographic activity were measured, and participants pressed a button to indicate the
subjective detection of MW during training. The data showed that BMT did not influence our measures of
stress but BMT was effective in reducing the frequency of subjectively detected MW events. The algorithm
for offline detection of MW achieved an accuracy of 85%. Based on this algorithm, a mobile application
was developed for automated MW detection in real-time. The application requires the use of easily placeable
sensors, provides a new approach to the real-time MW detection, and could be developed further for use in
MW-related investigations and interventions (such as mindfulness-based training of focused attention).
1 INTRODUCTION
While Mindfulness has, like Zen, Tibetan Buddhism
and Vipassan, a long history (Kabat-Zinn et al., 1985;
Austin, 1999; Gunaratana, 2002), mindfulness-based
training is flourishing in the USA and Europe as an in-
tervention strategy for improving mental and physical
health (e.g., Chiesa and Serretti, 2010; Bishop, 2004;
Rubia, 2009; Lutz et al., 2006; Hofmann et al., 2010;
Grossman et al., 2004). A key feature of mindfulness-
based training is the self-regulatory control of atten-
tion (e.g., Goleman and Schwartz, 1976; Kabat-Zinn,
1982). Interest in using mindfulness-based training to
enhance attentional performance is therefore growing
(e.g., Lutz et al., 2008; Bishop, 2004).
A novice typically begins training by acquiring
skill in the focusing of attention (Vago and Silber-
sweig, 2012; Kapleau, 1965), for various forms of at-
tention see e.g., Jha et al., 2007. In mindfulness, this
*M. Cheetham and C. Cepeda contributed equally to this work.
skill is most widely practiced in the form of focused
attention (FA) meditation (Lutz et al., 2008). During
FA the practioner learns to maintain an upright sitting
posture, relax and direct full attention to a chosen ob-
ject (typically the breath) while ensuring that the mind
does not wander (Tops et al., 2014). In the event of
mind wandering (MW), the practioner is instructed to
detect this as early as possible and voluntarily redi-
rect the focus of attention back to the object where
the focus should then remain. A particular challenge
in learning to self-regulate the control of MW is that
there is little meta-cognitive awareness that attention
has actually become disengaged from the object of
focus and entered a state of MW (Mooneyham and
Schooler, 2013; Oken et al., 2006). Acquiring greater
skill in detecting this state therefore requires cogni-
tive effort and practice.
The aim of the present work was to develop a mo-
bile tool that could be used for automated detection
of MW in real-time during practice of eyes- closed
FA meditation. This tool could also be integrated in
other cognitive training strategies for improving fo-
198
Cheetham, M., Cepeda, C. and Gamboa, H.
Automated Detection of Mind Wandering: A Mobile Application.
DOI: 10.5220/0005702401980205
In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 4: BIOSIGNALS, pages 198-205
ISBN: 978-989-758-170-0
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
cused attention in which attentional disengagement is
indicated to the practioner or trainer as it occurs. Psy-
chophysiological measures are ideally suited for use
in eyes-closed meditation. But we are not aware of
the development of a tool for automated detection of
MW during cognitive training of attention, such as in
FA meditation (but see work toward automated detec-
tion of MW during so-called mindless reading, Drum-
mond and Litman, 2010; Franklin et al., 2011; Mills
and Mello, 2015; DMello, 2013; Bixler and DMello,
2014; Blanchard et al., 2014).
The present study applied the well-established
breath-focused meditation procedure (Levinson et al.,
2014; Ramsburg and Youmans, 2012; Kabat-Zinn
and Hanh, 1990; Lutz et al., 2008; Tang et al.,
2007). This requires the practioner to attend fully
to the subjective sensation of breathing where the
air passes the nostrils. Psychophysiological change
is linked to different states of attention (Andreassi,
2000).Specifically, MW has been reported to relate
to a change in heart rate and skin conductance levels
(Ottaviani et al., 2015; Smallwood et al., 2004; Small-
wood and Schooler, 2009; Smallwood and Schooler,
2006). Our focus was placed therefore on mea-
sures of heart rate and skin conductance. Given our
use of the breath-focused procedure and that respira-
tion has been shown, at least on self-report basis, to
indicate attention-related improvement during mind-
fulness training (Levinson et al., 2014), respiration
was also measured. Finally, electromyographic activ-
ity was measured to assess whether postural change
would occur during MW episodes. This was consid-
ered possible because inattention to perceptual infor-
mation during MW could render the cognitive pro-
cessing of posture more difficult (Kam et al., 2011;
McVay et al., 2009; Rushworth et al., 2003).
Training in FA meditation was guided by a brief
mindfulness-based training (BMT) protocol. BMT
is known to have beneficial effects on alertness and
attentional control (Elliott, 2014; Tang et al., 2007;
Vinci et al., 2014; Srinivasan and Baijal, 2007). For
the purpose of the present work, we adapted a train-
ing protocol (Zeidan et al., 2011; Zeidan et al., 2010)
to create five 20-minute breath-focused training ses-
sions. Our protocol placed the emphasis of train-
ing on the attentional cycle (Hasenkamp et al., 2012;
Cahn and Polich, 2006). The attentional cycle com-
prises the main attentional states that are iteratively
practiced during FA training: Directing of full at-
tentional focus on an object, MW, subjective detec-
tion of MW, and the reinstatement of attentional fo-
cus on the object. Participants were required to press
a button when they subjectively detected the occur-
rence of MW. This served both the purpose of en-
suring that MW detection had been acknowledged (as
part of the training protocol), as well as providing us
with a participant-determined approach to the analy-
sis of our psychophysiological data. Given that BMT
is also known to have a beneficial effect on the self-
regulation of stress (Cresswell and Gilmour, 2014),
we explored the impact of BMT of FA meditation on
levels of stress.
2 METHODS
2.1 Participants
Fifteen mindfulness-naive volunteers [8 female] par-
ticipated (M = 22.67 years; SD = 2.29), This num-
ber was considered sufficient for data acquisition
(Braboszcz and Delorme, 2011). All participants
were normal healthy, native speakers of Portuguese,
with normal or corrected-to-normal vision, and had
no record of neurological or psychiatric illness, and
no current use of medication or drugs. Written in-
formed consent was obtained before participation in
accordance with the guidelines of the Declaration of
Helsinki. All procedures and con sent forms were ap-
proved by the Ethics Committee of the University of
Lisbon. Each person received 15 Euro for participa-
tion.
2.2 Materials and Procedure
Five 20-minute training sessions (one on each of 5
consecutive days) were conducted, using the BMT
protocol (adapted from Zeidan et al., 2011). The first
and last sessions took place in the laboratory in order
to acquire biological signals. All sessions were con-
ducted in a quiet room, with comfortable temperature
and dimmed light. At the beginning of the first ses-
sion, the participant signed a consent form and pro-
vided demographic data. The participant also self-
assessed the general quality of sleep on a 5-point scale
(ranging from 1 ”poor sleep” to 5 ”good sleep”). The
analysis of the self-assessment sleep data indicated no
impact of sleep quality on MW. These data are there-
fore not reported any further. The sensors were then
attached, and the participant read the BMT protocol.
The experimenter verified the participant’s compre-
hension of the protocol and then left the room. After a
duration of 20 minutes, the experimenter entered the
room, asked the participant to stop training and the
biosensors were removed.
Automated Detection of Mind Wandering: A Mobile Application
199
2.3 Data Registration
Continuous acquisition of biosignals was performed
with a biosignalsPLUX device (Plux - Wireless
Biosignals S.A., Lisbon, Portugal). The acquired sig-
nals were respiration (RESP), using 2 piezo-electric
bands (chest and abdomen), heart rate (HR), elec-
trocardiogram (ECG), electromyogram (EMG), using
one electrode on the trapezius muscle of the right and
left side of the neck, respectively, and electrodermal
activity (EDA). The recorded button-press (BP) indi-
cated subjective detection of MW. The recorded sig-
nals were saved to text file using the signal process-
ing software OpenSignals (Plux - Wireless Biosignals
S.A., Lisbon, Portugal), with a sampling frequency of
100 Hz (12 bits ADC).
2.4 Data Pre-processing
The following Python Packages were used: Mat-
plotlib (Tape, 2001), Seaborn (Haslwanter, 2015),
NumPy (Bressert, 2013) and SciPy (Bressert, 2013).
The first and last minute of data acquisition, dur-
ing which the experimenter was present in the room,
were removed. All signals were aligned at baseline,
having removed the mean value. Raw data were rec-
tified and filtered offline with a bandwidth 0-0.3 Hz
for EMG, 0.2-0.5 Hz for EDA, 0.1-0.3 Hz for RESP,
and 5-15 Hz for ECG. For movement artefacts, out-
liers of ECG and RESP were detected and replaced by
the preceding correct value. For ECG, outliers were
defined as a peak occurring between 0.4s before or
2s after the previous peak, and for RESP, as a peak
occurring between 10s after or 1.5s before the previ-
ous peak. For EDA, peaks in skin conductance were
recorded.
2.5 Detection of Mind Wandering
Based on previous studies (Ottaviani et al., 2015;
Smallwood et al., 2004; Smallwood and Schooler,
2009; Smallwood and Schooler, 2006), we assumed
that HR and EDA would increase during periods of
MW. For the purpose of developing the algorithm and
in the absence of any suitable literature known to us,
we assumed - consistent with the increase expected
in HR and EDA - that RESP would also increase.
These changes reflect an increase in arousal (Note-
boom et al., 2001). Analysis for MW was applied
to 20s-long data epochs time-locked to the BP (Bra-
boszcz and Delorme, 2011).
2.6 Assessment of Stress Level
The same biosignals were used to assess the impact
of the BMT protocol on stress. Based on, for ex-
ample, Everly and Lating (2013), Greenbaum (2012),
Khazan (2013), Lenman (1975) we expected a gen-
eral decrease in the respective value of these measures
the between the first and final training sessions to in-
dicate a reduction in stress levels. For ECG and RESP,
we extracted the time and frequency domain parame-
ters related to changes in stress (see Klabund, 2014),
as presented in Tables 1 and 2.
Table 1: Stress level by ECG.
Time domain Frequency domain
Time between peaks (s) IHR (beats/min)
pNN20 (%) LF (%)
pNN50 (%) HF (%)
RMSSD (s)
LF
HF
Table 2: Stress level by RESP.
Time domain Frequency domain
Mean (%)
Mean (breaths/min)
RMSSD (s)
Inspiration time (s)
Expiration time (s)
Inspiration (%)
Expiration (%)
Pause of expiration (%)
Inspirationtime
Expirationtime
Pausetime
Expirationtime
3 RESULTS
3.1 Mind Wandering Assessment
Receiver operator characteristic (ROC) curve analy-
sis was conducted to determine the accuracy of our
psychophysiological measures as classifiers of MW
events in ROC space, that is, the plot of the true posi-
tive rate (sensibility) versus the false positive rate (1-
specificity) (Raslick et al., 2007). Accuracy is mea-
sured by the area under the ROC curve (Hanley and
McNeil, 1982). As a rough guide, a value between .80
and .90 is conventionally considered good. All data
analyses were performed using SPSS version 21.0
(IBM Corp., Armonk, NY, USA).
ECG and EDA accomplished the best results and
were classified as good in accuracy, with an area un-
der the curve (AUC) for ECG and EDA of 0.809 and
0.808, respectively. RESP (0.709) and EMG (0.695)
BIOSIGNALS 2016 - 9th International Conference on Bio-inspired Systems and Signal Processing
200
showed fair and poor accuracy, respectively. Combin-
ing ECG and EDA improved the AUC, with 0.854.
This AUC is a good result, reflecting a high degree
of accuracy compared to previous studies (though it
should be noted that previous studies relate to mind-
less reading). Figure 1 represents an example of the
algorithm used, in which the relation between the pro-
cessed signals and estimated events can be seen. We
then compared the BP data of each participant be-
tween the first and final sessions of training to deter-
mine whether BMT was effective in reducing the fre-
quency of subjectively detected MW episodes. Paired
t-tests were conducted (with p < 0.05 considered sig-
nificant), showing a highly significant decrease (t
14
=
2.045, p = 0.03) in MW between the first (M = 46.67,
SE = 1.85) and last training sessions (M = 42.00, SE
= 2.53).
3.2 Stress Level Assessment
Paired t-tests (significance level at p < 0.05), were
conducted to compare the first and last sessions of
training. The results showed one significant effect,
namely, a decrease (t
14
= 2.09, p = 0.03) in RESP
(respiration time) between the first (M = 4.99, SE =
0.36 ) and fifth sessions (M = 4.64, SE = 0.34). No
effects were found for ECG, EMG and EDA. The p-
values of all parameters tested are presented in Table
3.
4 MOBILE APPLICATION
Based on the offline algorithm for MW detection, a
mobile application was developed for automated MW
detection in real-time. The mobile phone application
was programmed in Intel XDK, which comprises de-
velopment tools for programming web applications
on the basis of the language HTML5 (Intel, 2014).
A plugin for Android devices was created to allow
the device to vibrate (as a form of biofeedback) dur-
ing the occurrence of a MW episode. The Android
device is linked to the biosignalsPLUX device (Plux
Wireless Biosignals S.A., Lisbon, Portugal) to acquire
the biosignals (ECG and EDA). JavaScript (Simpson,
2012) was used to process the ECG and EDA sig-
nals in real-time according to the flowchart in Figure
2 (Kuo et al., 2006).
5 DISCUSSION
The aim of the present study was to initiate work to-
ward development of a real-time algorithm for MW
Table 3: P-values for stress (comparing first with last ses-
sion).
Parameter Full sample
ECG
RMSSD 0.30
Time between beats 0.17
NN20
0.34
NN50 0.23
IHR 0.27
LF 0.28
HF 0.28
LF
HF
0.49
EDA
Stress level 0.37
Number of peaks 0.35
RESP
RMSSD 0.34
Respiration time 0.03
Inspiration time 0.47
Expiration time 0.08
Inspiration 0.25
Expiration 0.25
Pause 0.20
Inspiration
Expiration
0.45
Pause
Expiration
0.33
Respiratory rate 0.12
EMG Right
Stress level 0.48
Mean amplitude 0.19
EMG Left
Stress level 0.39
Mean amplitude 0.33
detection during eyes-closed meditation. The main
finding was that our combined measures of EDA and
ECG were good in detecting episodes of MW. The
data show also that the BMT protocol was effective
in improving the self-regulation of MW. Given the re-
sults of the ROC analyses, the specificity of the BMT
protocol for training the attentional cycle, subjective
reports at debriefing confirming that participants ad-
hered to the protocol, and that there was an improve-
ment in MW over training, we assume that the al-
gorithm and the selected psychophysiological signals
provide a sensitive means for classifying periods of
attentional disengagement before subjective detection
of MW.
We cannot determine with this method what the
participant might be experiencing during attentional
disengagement. MW has been associated with two
specific changes in cognitive processing (Smallwood
and Schooler, 2006). Considered in terms of our
breath-focused meditation procedure, the first of these
would relate to the drift of attention away from the
sensation of breathing through the nostrils, resulting
in attenuated processing of perceptual information.
In other words, the attentional (or perceptual) disen-
gagement from the breath results in failure to maintain
FA. The second change is that this drift of attention is
Automated Detection of Mind Wandering: A Mobile Application
201
likely characterized by stimulus independent thought
during which attention is directed to internal thoughts
and feelings retrieved from memory (Smallwood and
Schooler, 2006), though this not need always be the
case (Stawarczyk et al., 2011). We did not collect
self-report data to retrospectively ascertain the pos-
sible content of experience during MW or whether
the content might have influenced psychophysiologi-
cal state (Smallwood et al., 2004; Smallwood et al.,
2004; Fahrenberg et al., 2001; Hinterberger et al.,
2011). But at debriefing, subjective detection of MW
episodes was most frequently cited as the most diffi-
cult part of the training (see Schooler et al., 2011).
This present study developed the algorithm pri-
marily with a view to its potential application in
mindful FA meditation. But this idea might be ex-
tended to other forms of cognitive training of atten-
tion. For training, this method could be used to cue
the participant to reinstate attentional focus when the
psychophysiological measures indicate occurrence of
attentional disengagement. This form of interven-
tion might be used to enhance the participant’s meta-
cognitive awareness of and skill in detecting MW
episodes (Sayette et al., 2009). Further research is
also required to consider when MW begins and when
it stops (Smallwood and Schooler, 2015). Our method
could be used also to examine inter-individual differ-
ences in the relationship between attentional disen-
gagement and psychophysiological measures. Inter-
individual differences might potentially relate, for ex-
ample, to differences in the depth of MW during
FA meditation, as considered in other contexts of
MW research (see the levels of inattention hypothe-
sis, Shad et al., 2011), change in day-today affective
state (Snippe et al., 2015), in the perceptual and in-
teroceptive awareness of body signals (Otten et al.,
2015), or in trait mindfulness (Bishop, 2004; Brown
and Ryan, 2004; Carmody and Baer, 2008; Thompson
and Waltz, 2007).
The data indicated that the BMT protocol was
not significantly effective in influencing our measures
of stress. This finding contradicts the subjective re-
ports of the participants at debriefing. One possi-
ble reason might relate to the RESP data. These
showed many artefacts, suggesting that the piezoelec-
tric bands were insufficiently reliable. Replacing the
piezoelectric with an inductive band might be con-
sidered in order to improve the reliability of the sig-
nal. The present study was based on novices. A
larger data set might include experienced practition-
ers of FA meditation to show how, for example, the
psychophysiological signature of attention disengage-
ment might evolve with practice. The mobile applica-
tion developed in the present study needs to be sub-
jected to further testing. This might be done with a
view to its potential use as a tool for training of MW
during practice of mindfulness-based breath-focused
attention. This tool requires use of easily placeable
sensors, provides a new approach to real-time MW
detection, and could be developed further for use in
MW-related investigations and interventions. Given
that MW in normal healthy individuals is reminiscent
of certain symptoms of Attention-deficit/hyperactivity
disorder (ADHD) (Seli et al., 2015), one area of ap-
plication might be in relation to ADHD.
ACKNOWLEDGEMENTS
The authors thank PLUX - Wireless Biosignals for
providing the acquisition system, plug-ins and sen-
sors.
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APPENDIX
Figure 1: Mind wandering detection results.
Figure 2: Real-time processing tools.
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