Automated Detection of Mind Wandering: A Mobile Application

Marcus Cheetham

1,2,3,∗

, C

atia Cepeda

4,∗

and Hugo Gamboa

4,5

University Research Priority Program Dynamics of Healthy Aging, University of Zurich, Zurich, Switzerland

Department of Psychology, Nungin University, Seoul, South Korea

Center of Competence Multimorbidity, University of Zurich, Zurich, Switzerland

Department of Physics, FCT-UNL, Lisbon, Portugal

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 ﬁve 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

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)

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

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

= 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

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

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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