Evaluation of Gel and Dry Electrodes for EEG Measurement to

Compare Their Suitability for Multimodal Workload Detection in

Humans

Judith Bütefür

, Mathias Trampler

and Elsa Andrea Kirchner

1,2 a

Institute of Medical Technology Systems, University of Duisburg-Essen, Duisburg, Germany

Robotics Innovation Center, German Research Center for Artificial Intelligence (DFKI GmbH), Bremen, Germany

Keywords: Workload, N-Back Task, EEG Frequency Power, Task Load Index, Dry Electrode Headset.

Abstract: In this paper we aim to investigate whether the use of dry electrodes to detect multimodal workload could be

a viable way forward in the future. Therefore, we did a comparative study with gel (6 subjects) and dry

electrodes (2 subjects) and analysed the data using the Task Load Index (TLI) and the power spectrum of

different frequency bands. The results show that the TLI is significantly increasing for higher workload

condition (p < 0.04) and expected changes in the frequency bands are significant for both gel and dry

electrodes in subject-specific frequency bands. In conclusion, the results look promising, and it is worthwhile

to conduct another study with more subjects using dry electrodes.

1 INTRODUCTION

To know the overall workload level of a person

during a certain task is helpful in different areas. For

the prevention of mental disorders as, for example,

burnout due to permanent stress and overload, it is an

advantage to know the overall workload level of a

person (Greif & Bertino, 2022), as the tendency

towards mental disorders increased in the past (World

Health Organization, 2023) and this must be avoided

as much as possible. Safety-critical environments in

particular need to be better monitored in terms of

workload to protect the people who work in them. In

space flight, for example, it is important to know the

workload level of each astronaut, since a higher level

of workload is related to a higher risk to make

mistakes (Morris & Leung, 2006) and this can quickly

end fatally. Further, microgravity on ISS and in space

(ESA, 2023) will likely have an impact on the overall

workload since astronauts are not used to it in general.

The Multiple Resource Model by Wickens (2008)

defines different dimensions influencing workload.

Objects that are in microgravity behave significantly

differently than those in Earth gravity. As a result,

visual processing and special activities consume more

resources because the objects astronauts see behave

https://orcid.org/0000-0002-5370-7443

differently than they would expect. Thus,

investigation of the adaption of workload under

different gravitational conditions is important.

The literature shows that workload can be

determined based on different physiological signals

(Fairclough & Mulder, 2011; Singh, Ponzoni

Carvalho Chanel, & Roy, 2021; Volden, Alwis, de

Viveka, & Fostervold, 2018; Ding, Cao, Duffy,

Wang, & Thang, 2020).

Different modalities can be investigated to

estimate workload under different gravitational

conditions. The following modalities are of special

interest for our future research:

• Electroencephalogram (EEG),

• Eye Tracking (ET),

• Electrocardiogram (ECG) and

• Respiration (RESP)

EEG and ET are very common parameters for

workload estimation. ECG and RESP are very

interesting for space applications, since different

gravity conditions have an impact to the

cardiovascular system of a person (Schlegel, et al.,

1998) as well.

The aim of this paper is to see, if a measuring

system with dry electrodes in form of a headset could

Bütefür, J., Trampler, M. and Kirchner, E.

Evaluation of Gel and Dry Electrodes for EEG Measurement to Compare Their Suitability for Multimodal Workload Detection in Humans.

DOI: 10.5220/0012357800003657

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024) - Volume 1, pages 747-754

ISBN: 978-989-758-688-0; ISSN: 2184-4305

747

be used for EEG measurement, since one big

advantage of such a device is that every person could

set this up on their own in a few seconds (Trampler,

Tabie, Rotonda, Heere, & Kirchner, 2021) which

would be required under conditions such as space

exploration with few persons and time available. To

test this, we conducted a study with both, a gel and

dry electrode system. Subjects had to solve the same

cognitive demanding tasks. To verify, which of the

systems is better suited for our aim, we looked at the

data measured during an N-back task (Kirchner W.

K., 1958).

The remainder of the paper is structured as

follows. In the next section, we provide an overview

about the EEG parameters that change due to

different workload conditions. In section 3, we

introduce the experimental setup of the study and

discuss the used methods. Afterwards, we explain the

results of the EEG analysis and discuss them in

section 5. In section 6 we give a conclusion about the

outcome.

2 WORKLOAD DETECTION

BASED ON THE

ELECTROENCEPHALOGRAM

This section provides information about the EEG and

the expected changes caused by “lower” and “higher”

workload. “Lower” workload was evoked by a clearly

simpler task with less demand on working memory.

The EEG measures brain activity with very high

time resolution by measuring the potential difference

between two electrodes (Berger, 1934). Some

parameters in the EEG are reported in literature that

change with different levels of workload.

For features in the time domain Pergher et al.

(2018) reported a higher P300 amplitude for lower

workload and the highest at the electrodes Fz, Pz and

Cz. A reduced amplitude of the P3a in an N-back task

was found as well (Putze, Mühl, Lotte, Fairclough, &

Herff, 2018). Kirchner et al. (2016) showed a reduced

P3b for high task load, i.e., workload caused by a task.

For features in the frequency domain a lot more

literature can be found in context to N-back tasks and

workload in general. Klimesch (1999) and Andreassi

(1995) reported that theta and alpha oscillations are

sensitive to task difficulty. Some groups reported a

change in the alpha band power over parietal sites

(Ding, Lu, Lin, & Tseng, 2016; Ewing, Fairclough, &

Gilleade, 2016). Ding et al. (2016) reported in detail

that they found a stronger alpha 1 (8-10 Hz) activity

in insula but a weaker alpha 2 (10-12 Hz) activity in

the anterior cingulate cortex for higher workload after

source reconstruction, compared to lower workload.

Ewing et al. (2016) calculated the frequency bands for

every subject individually and reported a decrease in

lower alpha band power (7.5-10 Hz) in the right

hemisphere. For upper alpha band power (10.5-13

Hz) they reported a decreasing power with increasing

demand.

The theta band power (4-8 Hz) was shown to

change during an increase of workload, while the

theta band power in the frontal sites does increase

(Bagheri & Power, 2020; Ewing, Fairclough, &

Gilleade, 2016; Shou & Ding, 2013). Nowak et al.

(2021) showed that an increase in theta band power at

frontal electrodes leads to better results in N-back

tasks. Ding et al. (2020) reported this especially for

the Fz electrode. Another group showed a stronger

theta activity in temporal regions 335 ms after the

stimulus onset (Ding, Lu, Lin, & Tseng, 2016).

Two groups showed an increase in beta band

power (13-25 Hz) for higher workload compared to

lower workload. Matthews et al. (2017) explained

that they would interpret the higher beta band power

as a direct expression of attentional overload or as an

indirect product of cognitive self-regulation. Singh et

al. (2021) found the higher beta band power mostly in

the fronto-central, temporal and occipital sites.

Changes in gamma band (25-45 Hz) are also

dependent on workload. Singh et al. (2021) showed

an increase in gamma band power for higher

workload, compared to lower workload in the brain

areas in which changes in beta activity were found.

To define the level of workload of a person, it is

also common to use the ratio of frequency band

powers of certain electrodes. For example, the Task

Load Index (TLI) is defined as the ratio between the

averaged power of the theta band at Fz and the

averaged power of the alpha band at Pz (Smith,

Gevins, Brown, Karnik, & Du, 2001).

3 METHODS

This section contains information about the dataset in

general, the experimental setup and procedure, the

data recording and pre-processing and the EEG

analysis.

3.1 Data

The data were recorded in two separated studies but

with the same experimental setup. The data for dry

electrodes and detailed information about the headset

are already published (Trampler, Tabie, Rotonda,

BIOSIGNALS 2024 - 17th International Conference on Bio-inspired Systems and Signal Processing

748

Heere, & Kirchner, 2021). These data were originally

recorded to explore the fit of the headset with five

subjects. The headset and its layout can be seen in

Figure 1.

Figure 1: A: Dry electrode headset with integrated

electrodes; B: layout (Trampler, Tabie, Rotonda, Heere, &

Kirchner, 2021).

The data with gel electrodes were afterwards

recorded with six subjects to compare dry electrodes

with gel electrodes.

The particular challenges of dry electrode systems

are both the signal quality and the wearing comfort

for the subjects, especially during longer

measurements. The electrodes must be placed on the

skin with a certain amount of pressure in order to

establish contact between the electrode and the scalp

with the appropriate impedance and to ensure good

signal quality. However, the pressure must not be too

high, as the test subjects would otherwise suffer pain.

This can be influenced by the flexibility of the

headset. The number of pins per electrode also has a

further influence on wearing comfort, as the pressure

is distributed over a larger area with more electrodes

and has no influence on the impedance (Fiedler, et al.,

2018).

3.2 Participants

EEG and ET data from eight healthy subjects (6 male,

average age = 29,8 ± 6,8) were included in this study.

All subjects gave their written informed consent and

were told that they could stop the experiment at any

time without consequences. The studies were

approved by the local Ethical Committee of the

University of Bremen. Subjects received a monetary

compensation of 10€ per hours.

3.3 Experimental Setting

Throughout the experiment, every subject executed

three sets with four different tasks each, always in the

same order. After every task the subject had to answer

the NASA-TLX questionnaire (Hart & Staveland,

1988). There was a 60-seconds break between every

task. After each set there was a break of five minutes.

The difficulty of the tasks increased with each set.

The experimental design can be seen in Figure 2.

The first task was a mental rotation task

(Shephard & Metzler, 1971) where subjects had to

decide which of the shown objects are the same but

rotated.

The second task was a visual N-back task

(Kirchner W. K., 1958). The easiest level was N=1,

the middle level N=2 and the most difficult N=3.

Square figures, as shown in Figure 2, were shown to

the subjects. Subjects were instructed to press a

button if the stimulus was a target. The number of

targets were between 20 and 30 and the number of

non-targets between 160 and 248 for every subject.

This difference in the number of stimuli is due to the

experimental design. Subjects had a time limit for the

task and had to process as many stimuli as possible.

The presentation time for each figure was 500 ms and

the inter-stimulus interval 2000 ms.

The third task was an arithmetic task in which the

subjects had to perform an addition or subtraction

with two numbers. The time limit was ten seconds for

each level of difficulty.

The last task was the Stroop test (Stroop, 1935).

Here, the levels of difficulty were always the same as

this task was a control task for workload conditions.

For the purpose of this paper only the EEG-data

from the N-back task are important and used for

evaluation.

Figure 2: Experimental design.

3.4 EEG Recording and Pre-Processing

Before the experiment started, each subject was

prepared with the Pupil Core Eye Tracker from Pupil

Labs (https://pupil-labs.com/products/core/) with a

sampling frequency of 200 Hz @ 192x192px and an

accuracy of 0.60°.

Evaluation of Gel and Dry Electrodes for EEG Measurement to Compare Their Suitability for Multimodal Workload Detection in Humans

749

Subjects were also prepared with the EEG

system. ANT eego myLab (https://www.ant-

neuro.com/products/eego-mylab) with a sampling

rate of 500 Hz was used. Six of the subjects (WK76,

RR09, JR48, AA70 VA13 & BS09) were prepared

with 64-channel Ag/AgCl active gel electrodes,

positioned according to the 10-20 system with

reference at FCz. The other two subjects (FW00,

SD50) were prepared with a 24-electrode tailor-

developed headset with dry electrodes also according

to the 10-20 system, where each electrode is

positioned by an arch that adjusts its pressure to the

appropriate force (for more detailed information see

(Trampler, Tabie, Rotonda, Heere, & Kirchner,

2021)). The 24 electrodes used were defined as the

optimal minimum before the headset was built. As

explained in Trampler et al. (2021), three other

subjects were measured with the dry electrode

headset, but we were unable to record an EEG signal

because the electrode cap did not fit properly. The dry

electrode headset was tailor-developed to fit subject

FW00 perfectly.

During the experiment, both EEG and ET were

measured the entire time.

Pre-processing was done with the MNE python-

library. The data were down-sampled to 256 Hz and

a bandpass filter between 0.1 and 40 Hz was applied.

3.5 EEG Analysis

To analyse the EEG data, the N-back task data were

segmented into epochs of 15 seconds without any

overlap and without consideration of the

target- / non-target-events. Power Spectral Density

(PSD) in µV

/Hz was computed for the different

frequency bands using the multitaper method. The

frequency bands were defined for every subject

individually.

The peak was determined in a fixed frequency

band using the frequency ranges (Samima & Sarma,

2019), which are showed in Table 1. Peaks were

detected using Brain Vision Analyzer 2.2 (Brain

Products GmbH, Gilching, Germany).

The electrodes were chosen based on the expected

changes with different levels of workload in the

individual brain areas (see Sec. 2). For beta and

gamma FCz electrode was used for active electrodes

and T7 for dry electrodes, since the FCz electrode was

set to GND and cannot be recalculated (see Figure 1).

T7 was chosen instead, since beta and gamma

changes can also be detected in temporal brain

regions (Singh, Ponzoni Carvalho Chanel, & Roy,

2021). The peak detection was done for both, low and

high workload conditions in the predefined frequency

band. Afterwards, the average of both peak frequency

values was calculated to obtain a value for defining

the frequency band.

Table 1: Used frequency ranges for peak detection.

Frequency Range (Hz) Electrode

Theta 4.0 – 8.0 Fz

Alpha 8.0 – 13.0 Pz

Beta 13.0 – 25.0 FCz / T7

Gamma 25.0 – 45.0 FCz / T7

To determine the final individual frequency band,

we used a 2 Hz frequency band for theta and alpha

with the average values as the centre. For beta and

gamma, we used a 4 Hz frequency band around the

average values, also using them as the centre.

After all individual frequency bands were

determined, the average power within this range was

determined for each epoch individually. This was

done for all electrodes in the respective relevant brain

areas. In Table 2 the used electrodes are listed. FCz

was not used for the analysis, in contrast to peak

detection, of beta and gamma bands, because for the

dry electrode data it does not exist and the used

electrodes for analysis should be the same for all data.

Table 2: Electrodes used for analyses of different frequency

bands.

Frequency bands Used electrodes

Theta F3, F4

Alpha P3, Pz, P4

Beta/Gamma

F3, Fz, F4, C3, Cz, C4,

T7, T8, O1, O2

For statistical analysis, it must be checked

whether the data are normally distributed. Hence, the

Kolmogorov-Smirnov test was applied. Since it

turned out that the data are not normally distributed,

the Wilcoxon signed-rank test was used to check for

statistical significance. If frequency bands were

significantly different, the absolute values were used

to see if the conditions (e.g., alpha power for N=1 >

N=3) were fulfilled (see Figure 4 for an example).

Also, the TLI was calculated for each subject

individually, using the average power over all epochs

for theta band of Fz electrode and the average power

over all epochs for alpha band of Pz electrode.

Although the N-back task is not a typical task for task

load, the frequency bands considered for workload

are very similar. The TLI can therefore be a first

indication of whether a subject’s workload level is

changing. In addition, a specific task was performed

during the N-back task, which affects the workload.

Also, a study by Hamann et al. (2023) showed the

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750

sensitivity of the TLI for workload. Normal

distribution was again tested using the Kolmogorov-

Smirnov test. Since there was no normal distribution

of the data, the Wilcoxon signed-rank test was used

to test statistical significance to see, if the TLI is

significant higher for N=3 in comparison to N=1. The

TLI was used to show in the first step whether a

significant difference in the frequency bands could be

seen at all for individual subjects within the different

levels of workload before the individual frequency

bands were analysed. The TLI was used, since it is

often used for workload estimation, even if ratios of

frequency bands should be used with caution

(Boumann, Hamann, Biella, Carstengerdes, &

Sammito, 2023). It can be used in this study because

the subjects have to actively perform a task, to which

the workload condition is linked.

4 RESULTS

The following section presents the results of the EEG

analysis in the frequency domain.

4.1 Task Load Index

The Wilcoxon signed-rank test showed a significant

increase of workload between the lowest (N=1) and

highest (N=3) task level (p < 0.04) over all subjects

measured with the gel electrodes. When looking at the

individual subjects, a difference in TLI can be seen

for all subjects (see Figure 3). For subjects with dry

electrodes the TLI was also calculated. However, the

sample size is not large enough for a statistically

significant statement regarding TLI.

Figure 3: TLI for subjects with gel electrodes for different

workload levels.

4.2 Single EEG Bands

We compared the power of the individual EEG

frequency bands under different workload conditions

(N=1 and N=3) for each subject individually.

For the theta band power, we analysed the F3 and

F4 electrodes. We could show a significant increase

in the power for four subjects with gel electrodes

(p < 0.003). For the dry electrodes one subject

showed a significant increase (p < 0.001). The results

can be seen in Table 3.

For the alpha band power, we used the electrodes

P3, Pz and P4 and could show a significant decrease

in power (p < 0.03) for four subjects, but not for the

same subjects as for theta power. For the subjects

with dry electrodes, we could show a decrease in

power of the alpha band for one subject (p < 0.01).

For subject FW00 the difference between the band

power of N=1 and N=3 was significant, but alpha

power increased from the lower workload condition

to higher workload condition, which can be seen in

Figure 4. This value is marked with an asterisk. For

individually results of all subjects see Table 3.

Figure 4: Averaged PSD values for alpha band power of

subject FW00 under different workload conditions.

For beta and gamma band power we used the

electrodes F3, Fz, F4, C3, Cz, C4, T7, T8, O1 and O2.

For beta band power we could show a significant

increase in power for one subject with gel electrodes

(p < 0.05) and for one with dry electrodes (p < 0.001).

For four of the other subjects the difference between

the beta band power was significant, but the power

for N=3 decreased in comparison to N=1, instead of

increasing. In Table 3, these values are marked with

an asterisk as well.

Evaluation of Gel and Dry Electrodes for EEG Measurement to Compare Their Suitability for Multimodal Workload Detection in Humans

751

Table 3: p-values for each subject from Wilcoxon signed-

ranked test.

Subject

θ α β γ

WK76 <0.001 <0.001 <0.001* n.s.

RR09 <0.001 <0.03 <0.05 <0.01

JR48 <0.001 <0.001 n.s. n.s.

AA70 <0.003 n.s. <0.001* n.s.

VA13 n.s. n.s. n.s. <0.001

BS09 n.s. <0.001 <0.003* <0.001

FW00

<0.001 <0.001* <0.004* <0.004

SD50

n.s. <0.009 <0.001 <0.001

*Significantly different, but power does not change in the

direction as expected

Subjects measured with dry electrodes

For gamma band power we could show a

significant increase in gamma band power for three

subjects with gel electrodes (p < 0.01) and for both

subjects with dry electrodes (p < 0.004). The results

for beta and gamma band power can also be seen in

Table 3.

5 DISCUSSION

The main objective of this study was to investigate, if

an EEG headset system with dry electrodes is suitable

for determining workload levels of humans. To test

this, we did a study with gel and dry electrodes. Dry

electrodes were placed in a custom-made headset

optimized to fit a specific person. For this

comparison, subjects had to do an N-back task with

three conditions (low (N=1), medium (N=2) and high

(N=3) workload). For the analysis we only looked

into the low and high workload data and compared

them with each other.

For data analysis objective measures were used.

We did a frequency analysis, because if we find more

workload-related and relevant features, these could

also be used in addition to the time domain features

for machine learning. First the TLI was calculated. As

can be seen in Figure 3 a change in TLI, which

basically means a change in the ratio between theta

band power in Fz electrode and alpha band power in

Pz electrode can be seen for all subjects with gel

electrodes. The difference is also statistically

significant (p < 0.04).

Unfortunately, we cannot provide statistics

regarding TLI with dry electrodes because the sample

size of two subjects is too small. For this, more

subjects must be measured with dry electrodes. This

was not possible, since the dry electrode headset is

customized to fit one person, as mentioned above, and

would not fit very well to other subjects. We tried to

measure more subjects, but if the size of the head is

too small, we could not get any results, because there

is no contact between the electrodes and the head

surface. If the size of the head is too big, subjects

would easily get a headache because of too much

pressure. This is definitely a disadvantage of dry

electrode headsets compared to gel electrode caps, as

already discussed in Trampler et al. (2021), although

they are easily to put on by the users themselves.

For the analysis of the power of the frequency

bands we used different electrodes for the bands,

since the changes of power are detected in different

brain areas (Ding, Lu, Lin, & Tseng, 2016; Ding, Cao,

Duffy, Wang, & Thang, 2020; Singh, Ponzoni

Carvalho Chanel, & Roy, 2021). For beta and gamma

frequency we used F3, Fz, F4 and C3, Cz, C4 instead

of FC1 and FC2 for fronto-central region, since the

dry electrode headset does not have these electrodes

and we want to have comparable results.

Based on the analysis, it can be said that the

frequency bands have different significance for the

analysis of workload. According to the results from

Table 3, it can be seen that theta and alpha band power

are significant for most of the subjects. At least one

of these two frequency bands is significant for all

subjects except VA13. For the subjects WK76, RR09

and JR48 even both power changes are significant.

Beta band power has less significance in relation

to theta and alpha. Its changes are just significant for

two subjects, whereas one was measured with gel

electrodes and one with dry electrodes.

Gamma band power changes are significant for

five subjects in total, but it is hard to interpret since it

is a really high frequency band and its changes could

also be affected by muscle activity from frontalis

and/or temporalis muscles (Goncharova, McFarland,

Vaughan, & Wolpaw, 2003).

Overall, based on our analyses we can state that

changes in frequency bands regarding different

workload conditions are very subject-specific. This is

also important for machine learning, as it makes

features very subject-specific as well.

Results from dry electrodes show that there are

significant changes in the power of frequency bands.

For the subject FW00 we found a significant change

in the power of alpha and gamma frequency bands.

For subject SD50 we could find significant changes

in the power of all frequency bands except alpha. For

both subjects we could see a very similar behaviour

for dry electrodes compared to gel electrodes.

For future work, the other modalities presented in

the introduction (ET, ECG, RESP) should be included

to increase the likelihood of the data being useful if

the EEG data cannot be recorded properly. The

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752

presented modalities also promise a good analysis of

the current workload. In addition, the headset must be

adapted in terms of cross-subject fit and comfort so

that a study with more than two subjects of good data

quality can be conducted.

6 CONCLUSION

In this study, we investigated whether the use of dry

electrodes to detect workload could be a viable way

forward, particularly using a headset that can be very

easily self-fitted. Our results suggest that dry

electrodes are a promising alternative for the

detection of workload if the headset fits the subject.

As a next step a study with a larger sample of subjects

is needed. However, the adaptability of the dry

electrode headsets is significantly less than that of gel

electrode caps. To improve this, either better suited

subjects with very similar head shapes can be selected

or better fitting headsets must be built.

ACKNOWLEDGEMENTS

We would like to express our gratitude to all the

subjects who participated in the study. Also, we

would like to thank the researchers from DFKI,

namely Marc Tabie and Mathias Trampler, who set

up the experiment and recorded the data. Special

thanks also goes to Dr. Su-Kyoung Kim for her help

with the statistical analysis.

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