A Hardware/Software Platform to Acquire Bioelectrical Signals. A Case

Study: Characterizing Computer Access through Attention

Alberto J. Molina, Isabel M. G

omez, Jaime Guerrero, Manuel Merino, Juan A. Castro,

Royl

an Quesada, Santiago Berrazueta and Mar

ıa Hermoso-de-Mendoza

Departamento de Tecnolog

ıa Electr

onica, Universidad de Sevilla, Spain

Keywords:

Attention, Biofeedback, BCI, HRV, GSR, EDA, SC, Affordable Devices.

Abstract:

This paper describes a hardware/software platform to acquire human body signals. In the ﬁeld of physiological

computing it is desirable to have a system that allows the synchronized acquisition of signals coming from

different sources. Here is described how to unify the whole process of acquiring signals from both customized

hardware and low cost commercial devices such as Neurosky’s mindwave. A case study using this platform

is also shown: studying the feasibility of using sustained attention to access a computer. In order to do that

brain activity was measured using Neurosky’s mindwave. The participants in this study were asked to keep

their attention high/low for as long as possible during several trials. Experimentation was performed by 7

normally developed subjects and 3 people with cerebral palsy (CP). Our preliminary work shows that 60%

of participants might be potential users of this technology. Eventually, modulating the attention to access a

communication board needs a scanning period greater than 5.76s.

1 INTRODUCTION

Communication is vital for human beings. A system

allowing people with disabilities to access a computer

or a communication system reliably would be highly

beneﬁcial. We can ﬁnd several devices on the market

and scientiﬁc papers which translate user intentional-

ity into discrete events. The simplest and one of the

most extended is based on a binary switch (on/off con-

tacts), whereby people with disabilities can use soft-

ware applications, particularly those based on scan-

ning methods. A good survey for assistive devices

can be found in (McMurrough et al., 2012).

For people with severe disabilities these simple

devices are still very difﬁcult to use. For them, brain

computer interfaces (BCI) could be a feasible alterna-

tive. BCI systems (Nicolas-Alonso and Gomez-Gil,

2012; Mill

an et al., 2010) are based on recording cor-

tical neuronal activity, and one way to achieve this is

by means of EEG (Electro-Encephalo-Graphy) which

requires several electrodes placed on the scalp. One

possible drawback with these systems is their cost

which prevents most people with disabilities from ac-

quiring it. Nevertheless, some companies, such as

Emotiv and Neurosky have released their wireless

BCI headsets (Emotiv Epoc, Neurosky mindwave,..)

for entertainment uses such as brain gaming and mind

monitoring with affordable prices for the consumers.

Emotiv has up to 14 channels covering all the cere-

bral lobes and the two hemispheres and it has also

studied as potential BCI system for people with dis-

abilities (Welton et al., 2016). NeuroSky mindwave is

cheaper than Emotiv epoc and it has only one channel

placed at the pre-frontal left position, Fp1. In (Das

et al., 2014) a comparison was carried out between

both low-cost systems, to detect cognitive loads. The

authors found that Emotiv provided better results but

recognized the advantages of Neurosky because it is

more user-friendly, easier to setup and maintain.

It is known that cognitive tasks inﬂuences signals

captured from the human body in several ways. For

example, stress affects brain rhythms, reducing the

power of α waves in EEG (Tyson, 1987), inﬂuences

the heart rate variability (Taelman et al., 2009) or pro-

duces changes in the electrodermal activity (EDA)

(Villarejo et al., 2012). Attention is a cognitive pro-

cess and there are several types of attention the human

beings use during daily activities. One of them is the

Sustained Attention which can be deﬁned as the abil-

ity to focus on one speciﬁc task for a lapse of time

without being distracted (e.g.: during playing a video

game) (Barkley, 1997).

Training sustained attention can be beneﬁcial for

children with Attention Deﬁcit Hyperactivity Dis-

Molina, A., Gómez, I., Guerrero, J., Merino, M., Castro, J., Quesada, R., Berrazueta, S. and Hermoso-de-Mendoza, M.

A Hardware/Software Platform to Acquire Bioelectrical Signals. A Case Study: Characterizing Computer Access through Attention.

DOI: 10.5220/0006412200760083

In Proceedings of the 4th International Conference on Physiological Computing Systems (PhyCS 2017), pages 76-83

ISBN: 978-989-758-268-4

order (ADHD) or people with motor disabilities.

In (Mu

noz et al., 2015) the authors developed a

videogame to train sustained attention for children

with ADHD using as an action mechanism the neuro-

modulation of θ and β waves through an electrode lo-

cated on the central part of the forehead. In (Heidrich

et al., 2015) people with cerebral palsy (CP) took part

in a experiment in which they had to control their at-

tention to play with different games. In those games,

the players had to reach a certain level of attention

or/and to keep it over a preset value to make the game

advance.

This work looks into the feasibility of modulat-

ing sustained attention to control a system in a binary

way (on -high attention-, off -low attention-), such as

a switch, while using cheap BCI devices. Subjects

need to keep the attention low/high for a while and be

volitionally able to switch between them. We have de-

veloped a software/hardware platform to receive sev-

eral signals from the human body to train the mod-

ulation of the sustained attention and study how tir-

ing this method of interaction is based on physiolog-

ical signals like heart rate variability (HRV) and gal-

vanic skin response (GSR). In this preliminary work

we have just shown part of this study: the platform

designed and preliminary data based only on the EEG

signal.

Section 2 brieﬂy explains the fundamentals of at-

tention and some techniques used to measure it. Sec-

tion 3 shows the devices and tools employed in ex-

perimentation, section 4 describes the methodology

followed in experimentation and section 5 the results.

Finally, sections 6 and 7 contain the discussion and

the conclusions respectively.

2 MEASURING THE SUSTAINED

ATTENTION

From a temporal point of view, attention makes EEG

signals more complex, so its measurement could be

based on its fractal dimension. Several works have

shown the reliability of such an approach (Wang et al.,

2010; Wang and Sourina, 2013; Lee et al., 2000; Sia-

maknejad et al., 2014). There have also been some

works into the effects that attention or cognitive skills

have on power bands. In general, the α band increases

as the difﬁculty of the task diminishes or after task

practice, suggesting that fewer cortical resources are

required (Gevins et al., 1997). In the same work, in-

creases in θ suggested that focusing attention or in-

creasing the memory load require more effort. The

use of the ratio between frequency bands like θ/β,

known as theta-beta ratio (TBR), has also been re-

ported as an indicator for attention deﬁcit disorder

(ADD) or ADHD people (Lubar, 1991). TBR is in-

creased in frontocentral areas in children with atten-

tion deﬁcit disorders.

Some papers have shown the feasibility of detect-

ing attention using a reduced number of electrodes.

In (Rodr

ıguez et al., 2013) ﬁve different bipolar con-

ﬁgurations of two electrodes were investigated dur-

ing exercises of attention. Results showed that EEG

rhythms were observed with more amplitude in two

EEG channels: Fp1-A1 and FP1-T3. They adopted

the conﬁguration Fp1-A1, because those positions are

free of hair which allows an easy electrodes place-

ment (these are the positions used in Neurosky mind-

wave). They also found that the α, β and γ rhythms

presented signiﬁcant differences (p < 0.05) between

low- and high-attention level. For this reason, they

proposed an index, named Attention Power (AP),

based on the sum of the power α and β bands to con-

trol a game. The 80% of the subjects found corre-

lation between his/her attention level and the effect

exerted over the game.

3 THE DEVELOPED PLATFORM

In this section we show the devices that have

beenused, the designed circutis and the software made

to acquire and process signals coming from different

body sources.

3.1 Electrocardiogram Circuit

We have developed an electrocardiogram circuit

based on the one shown in (Spinelli et al., 2001). Fig-

ure 2 shows the schematic of the implemented circuit,

which uses three passive electrodes, one of them to

reduce the common mode interference. The circuit

has a frequency response ranging from 0.1 Hz up to

30Hz, using a one-pole high pass ﬁlter and a second

order low pass ﬁlter. The former reduces the signal

wandering while the latter helps to increase rejection

ratio at 50Hz as well.

Signal is sampled at a 250Hz ratio by an Arduino

platform which also implements a Notch digital ﬁlter

to reduce the 50Hz interference (Eq. 1). Filtered data

is sent to a computer by serial port at 115200 bps.

H(z) =

− 0.618z +1

− 0.601z + 0.92

(1)

An example of a 10-second ﬁltered signal while

performing an experiment is shown in Figure 2. It

can be observed the main waves of a typical electro-

cardiogram with very low interference noise.

A Hardware/Software Platform to Acquire Bioelectrical Signals. A Case Study: Characterizing Computer Access through Attention

Ref

OUT

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Figure 1: Electrocardiogram circuit based on the design shown in (Spinelli et al., 2001).

The ECG signal may be used to measure how stressful

a cognitive task might be (Merino et al., 2014) but

also to detect whether the subject is paying attention

or not (Chen et al., 2010).

t (s.)

0 1 2 3 4 5 6 7 8 9 10

ECG

250

300

350

400

450

500

550

600

650

700

Figure 2: A ten second ECG signal after applying the notch

ﬁlter.

3.2 Electroencephalogram Device

Neurosky’s mindwave is a device that measures brain

activity using a sensor on the forehead (Fp1) and a

clip located on the left ear that acts as a ground and

reference. It delivers information that we can classify

in three levels of processing. From lowest to higher

levels, they are: raw EEG signal (Figure 3) at a sam-

pling rate of 512Hz and 12 bits of resolution, power

bands, δ, θ, α, β and γ and eSense, which includes

propietary meters for attention and meditation. Power

bands and eSense signals help reduce the processing

of the raw signals in external devices and allow to use

digital systems with low computation resources.

t (s.)

0 1 2 3 4 5 6 7 8 9 10

EEG

-600

-400

-200

200

400

600

800

BLINKS

Figure 3: A segment of raw EEG signal with ocular artifacts

while performing an experiment.

Neurosky’s manufacturer states that attention signal

has more emphasis on beta wave but the exact algo-

rithm has not been published. Nevertheless, it has

been shown that there is a positive correlation be-

tween the reported attention level of this device and

the self-reported attention levels of the participants in

a experiment which analyzed the Neurosky usability

in an assessment exercise (Rebolledo-Mendez et al.,

2009).

In this work we show the results obtained based

only on the attention signal delivered by the Neu-

rosky.

3.3 Galvanic Skin Response Circuit

The GSR circuit is shown in Figure 4. The ampliﬁer

on the left works in non-inverter mode which gain is

controlled by the skin resistance. Hence, as the skin

resistance increases, the gain also does. Oune elec-

trode is powered at 0.5v, while the other is connected

PhyCS 2017 - 4th International Conference on Physiological Computing Systems

the the ampliﬁer output. The second ampliﬁer basi-

cally shifts down the voltage 0.6v and ampliﬁes the

ﬁrst stage 1.5 times. A low pass ﬁlter with a cutoff

frequency of approximately 5Hz ﬁlters out most of

the signal noise.

Figure 4: Schematic of the GSR circuit.

The Arduino board samples this signal at 250Hz and

sends it to the computer wherein it is ﬁltered using

a 31-tap FIR low pass ﬁlter with cutoff frequency of

1Hz and, then, downsampled with a 25:1 ratio. A typ-

ical raw signal after applying these processes is shown

in Figure 5.

Time (s.)

0 10 20 30 40 50 60

Conductance

0.013

0.014

0.015

0.016

0.017

0.018

0.019

0.02

0.021

0.022

0.023

Figure 5: A segment of raw GSR signal.

3.4 Software

A Matlab

graphic user interface (GUI) was built to

train subjects’ sustained attention, capture informa-

tion received from different sensors and store data

for posterior analysis. Several functions read data

coming from Arduino and Neurosky’s mindwave, and

create input streams to a synchronization software

called labstreaminglayer (Medine, 2016). During

the experiment, the software sends marks to the lab-

streaminglayer to delimit the different phases of the

experiment. Another function reads and stores the

output streams.

Figure 6: A screenshot of the application during a trial.

4 EXPERIMENTATION

For neuro-feedback purposes, a great part of the

screen shows a bar which moves up and down chang-

ing its color according to the received attention values

which ranged from 0 to 100 like a percentage. The

higher the attention value, the higher the bar shown

on the screen. The color of such a bar is green for an

attention level over 60%, red if it is under 40% and

yellow otherwise.

4.1 Participants

Seven normally developed subjects (A1,..A7) aged

36.4 ± 10.2 formed group A (control group) and three

subjects with CP (B1,..B3) aged 35.3 ± 1.2 made up

group B, who were recruited from ASPACE Sevilla,

a non-governmental organization specialized in cere-

bral palsy. The recruitment into group B was done

according to the following inclusion criteria:

1. The access to a computer by traditional switch-

based devices is usually very hard to be carried on

or almost impossible,

2. Have good intellectual capabilities,

3. GMFCS Level V (Palisano et al., 1997),

4. CFCS Level IV (Hidecker and et al., 2011).

The participants agreed to take part in the experi-

ment and in the case of group B, their families were

informed and allowed their participation. The Ethics

Committee of the University of Seville also approved

this experiment.

4.2 Conditions

Experimentation was carried out in a quiet room with

dim lighting. The experiment was considered correct

A Hardware/Software Platform to Acquire Bioelectrical Signals. A Case Study: Characterizing Computer Access through Attention

if there were no interruptions. Participants belonging

to group A were told to set the environmental condi-

tions (temperature, lighting) so that they were com-

fortable during the experiment. For group B subjects,

experimentation was conducted by a caregiver who

was always present and set the environmental condi-

tions.

4.3 Phases in Experimentation

Experimentation consisted of two phases (see Figure

7). As explained below, in the ﬁrst phase the partici-

pants had to ﬁnd the strategies to control their atten-

tion. Those who would not have been able to control

their mental state properly did not perform the follow-

ing phase. The second phase was similar to the ﬁrst

with the difference that we recorded the information

sent by the sensor during the attention/non-attention

trials.

4.3.1 Phase 1

The main goal of phase 1, also called ”Freestyle”,

was to practice and try to ﬁnd the best strategies to

control attention levels. Previously, they were told

to follow a series of basic strategies. For instance,

to practice attention we told them: ”try to perform

mathematical operations”, ”try to plot an object men-

tally”, etc. To practice non-attention we suggested:

”try not to think about anything”, ”make your mind

go blank”, etc. These suggestions were to get them

going, they each had to ﬁnd the best way of control-

ling her/his level of attention. We used the software

explained above to give participants feedback about

how they were performing the experimentation. The

caregiver sometimes asked participants in group B to

perform several attention/non-attention actions to get

some feedback about their achievements.

The number of sessions in phase 1 depended on

the subject but to prevent this phase from becoming

too drawn out, we set an upper limit of 10 sessions of

roughly 15 minutes.

At the end of each session in this phase, group A

participants were asked to ﬁll in a short questionnaire

about how well they had performed the experiment.

Those who admitted not having controlled attention

properly in more than two out of the last ﬁve sessions,

were excluded from the following phase. In group B,

the caregiver was responsible for discriminating such

participants.

4.3.2 Phase 2

In this phase participants performed a sequence of

5, 14 minute, sessions (one per day). Each session

Figure 7: Experimental time sequence. Phase 1: Subjects

must ﬁnd the strategies to control their attention levels. A

maximum of ten 15-min sessions was set. Phase 2: Five

14-min sessions with 7 attention/non-attention trials.

Figure 8: The temporal sequence in a experimental session.

consisted of 7, 2-minute, trials divided into four 30-

second parts. In each part, subjects had to keep

their attention level above/below a threshold of 50%

as soon as the application requested it. In the sec-

ond/fourth 30-second part of the trial the subject had

to relax, and to help participants do so, the soft-

ware showed an idyllic landscape on screen. Figure

8 shows the time schedule of this phase.

5 RESULTS

Data were analyzed using GNU Octave version 3.8.1

and R version 3.0.2. The ﬁrst analysis was to ﬁnd out

how the method for identifying attentional states had

worked. As the variable selected to control feedback

to the user was the attention signal, the exploratory

analysis was based solely on this.

Phase 1 removed four participants from group A

and one for group B. Namely, participants A5-A7 and

B1 were unable to control their attention level and did

not go on the following phase.

Figure 9 shows boxplots containing the results

of phase 2 for each subject and session, differenti-

ating between attention trials (green boxes) and non-

attention ones (red boxes). Each box contains 7 values

representing the average of the attention percentages

of a trial in a session.

Table 1 shows the mean and standard errors of

some quantitative features which may characterize

experimental results:

PhyCS 2017 - 4th International Conference on Physiological Computing Systems

Figure 9: Attention levels for participants and sessions. Green boxes contain averaged values for attention trials; red boxes

the averaged values for non-attention trials.

• The initial time, t

or time elapsed, in average,

from the beginning of the trial until the sub-

ject made the attention level go above/below the

threshold in attention/non-attention trials respec-

tively. We can differentiate t

for attention and

non-attention trials calling it t

and t

o f f

respec-

tively.

• Sustained attention time, t

, shows how long, on

average, the subject could maintain the attention

level without crossing the threshold.

6 DISCUSSION

Figure 9 shows that subjects A1, A2 and A3 per-

formed the experiment rather well, as the attention

boxes generally contained higher values (above the

50% threshold) than the non-attention ones (below

50%) and there was not excessive overlapping among

them. It was clearly not easy to perform all sessions

of the experiment perfectly. For example, participant

A1 did not obtain good results in the last session; nei-

ther did, A2 in the ﬁrst and second sessions nor A3

Table 1: Initial time and sustained attention time for each

participant. Standard errors (SE) are also shown.

Subject Condition )

± SE (s)

Attention 2.48 ± 0.87 19.3 ± 3.7

Non-attention 2.08 ± 1.02 18.6 ± 5.8

Attention 4.29 ± 1.44 18.8 ± 4

Non-attention 2.19 ± 0.62 17.7 ± 1.9

Attention 2.06 ± 0.30 16.1 ± 1.5

Non-attention 1.69 ± 0.19 12.5 ± 0.8

Attention 2.91 ± 0.66 10.7 ± 2.5

Non-attention 4.63± 1.67 11.4± 2.0

Attention 2.2 ± 0.60 12.4 ± 1.8

Non-attention 5.0 ± 2.14 7.8 ± 2.8

Attention 2.0 ± 0.46 11.0 ± 1.3

Non-attention 2.6 ± 0.62 11.0 ± 2.5

mainly in the attention trials in session 3. Participants

A4 and B3 behaved differently; they did not fulﬁll the

goals since many of their results in the attention tri-

als were below the threshold and many of those in the

non-attention trials were above it. However we should

remark that for these two subjects in each session, the

median values in the attention trials were higher than

in the non-attention ones. Participant B2 performed

similarly to A4 and B3 in the last three sessions. In the

A Hardware/Software Platform to Acquire Bioelectrical Signals. A Case Study: Characterizing Computer Access through Attention

others, the subject’s attention level was almost always

above the threshold with non-attention mean values

higher than those in attention trials. Anyway, it seems

to be plausible to set a threshold, different to the 50%,

which should be adjusted session by session.

Figure 10: Temporal parameters and their relationship with

scanning period. T

scan

≥ t

+ t

to select one pictogram

and t

also has to be greater than t

o f f

> t

o f f

) so as not

to select the following pictogram.

Initial time

and sustained time

are related to the

time needed to select a pictogram on a communicator

board, when accessing a computer by changing the at-

tentional state. Firstly, a threshold establishes the bor-

der between these two states, so a subject who wants

to select a pictogram has to exceed such a threshold

for a time. The time

in attention trials (t

) shows

the average time to cross such a threshold and reach

the attention state. In the same way, the time

in non-

attention trials (t

o f f

) shows the time taken to go back

to the non-attention state. In between them, the at-

tention level must be kept high for t

seconds so that

the system can detect the user’s intention (see Figure

10). The dwell time or scanning period t

scan

depends

on such temporal parameters. For example, partici-

pant A1 took t

=2.48s to move from ’resting’ to the

attentional state and t

o f f

=2.08s to come back again.

This means the scanning period, t

scan

has to be greater

than 2.48s (Eq. 2) on average and the t

greater than

2.08s to avoid selecting the pictogram next to the pre-

selected one (Eq. 3). The selection time, t

is also

related to sustained time,

, as the latter sets the up-

per limit for the former. Table 1 shows that all partici-

pants were not able to maintain their attention state for

more than 10.7s in group A or 11s in group B, which

could be a constrain to the number of pictograms on

the screen. Increasing t

could also be a solution to

the lack of control for low attentional values to reduce

the number of false selections.

scan

≥ t

(2)

o f f

< t

≤

(3)

Minimal t

scan

can be estimated by approximating

≈ t

o f f

, so t

scan

≈ t

+ t

o f f

whereas t

≤

. Ac-

cording to Table 1, all users comply with Eq. 3. The

minimal t

scan

is shown in Table 2.

Table 2: Minimal t

scan

according to Eq.2, Eq. 3 and Table

Subject t

scan

(s.)

A1 4.96

A2 6.48

A3 3.75

A4 7.54

B2 7.2

B3 4.6

7 CONCLUSIONS AND FUTURE

WORK

This study shows that not all participants were able to

manage their attentional state well enough. Three par-

ticipants from group A and one from B were not able

to start the phase 2 (40%) after 150 min of training.

For the rest of participants, the control of attention

to access a computer is possible, with an average t

scan

equal to 5.76s, although further research is needed.

One improvement will come from setting a classiﬁer

to discriminate between two attentional states, which

allows to automatically set the threshold and increase

the accuracy in classifying. Processing the raw sig-

nal, will also let us include other kind of algorithms

to detect the attention. In this sense, discarding EEG

segments containing artifacts is important for obtain-

ing power bands correctly. We know that when peo-

ple with disabilities used the EEG sensor, lot of arti-

facts where recorded due to the amount of involuntary

movements the participants of group B showed. We

do not know whether the Neurosky’s proprietary al-

gorithm rejects these contaminated segments.

Eventually, including other signals and psycho-

logical tests, will give us information of how tiring

this kind of method of access a computer is, espe-

cially, for people with disabilities.

ACKNOWLEDGEMENTS

The authors would like to thank the staff of ASPACE,

in particular Nacho Silva for his support in the re-

alization of this work and the anonymous reviewers

who helped us improve this document with their com-

ments.

PhyCS 2017 - 4th International Conference on Physiological Computing Systems

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