Feature Extraction and Selection for EEG and Motion Data in Tasks

of the Mental Status Assessing

Pilot Study using Emotiv EPOC+ Headset Signals

Alexey Syskov, Vasilii Borisov, Vsevolod Tetervak and Vladimir Kublanov

Ural Federal University named after the first President of Russia B.N. Yeltsin, 19 Mira str., 620002, Yekaterinburg, Russia

Keywords: Accelerometer, Brain-Computer Interface, Electroencephalography, Machine Learning, Mental Evaluation,

Test of Variables of Attention, Principal Component Analysis.

Abstract: In the paper the results of extracting and selection the features of EEG data and accelerometer for mental

status evaluation are shown. We have used 14 channel wireless EEG-system Emotiv EPOC+ with

accelerometer (motional data - MD) for short-term recording under several functional states for 10 healthy

subjects: Functional rest (rest state), TOVA-test (mental load), Hyperventilation (physical load) and

Aftereffect (after test state). We then extracted core features from EEG-only and MD-only data using principal

component analysis. After that, supervised learning methods were used for mental state classification: EEG-

only core features for AF3, T7, O1, T8, AF4 channels, MD-only core features and EEG- MD integrated core

features. Experimental results showed that integrated core features for mental status evaluation have higher

prediction accuracy 92,0% for decision tree method.

1 INTRODUCTION

Evaluation of human mental status is a complex and

complicated task. Electroencephalography (EEG) is

well known method for assessing mental state and

optimizing conventional performance: attention;

workload; emotion (Wolpaw and Wolpaw, 2012).

Acquisition of EEG signal in real-world

conditions is characterized by the usage of mobile and

wearables devices (Lin and Jung, 2017; So et al.,

2017; Sun et al., 2012). Combinations of different

modalities sensors are used for assessing and

controlling the subject’s function state (Silva et al.,

2014).

Accelerometer is one of widely used sensors for

assessing body movement artefact during ECG, EEG

recording. An accelerometer signal is acquired in

order to identify areas of the signal with motion

artifacts (Y. Kishimoto et al., 2007). In (Wu et al.,

2017) operator’s mental workload is measured with

EEG headset. EEG headset was composed of two

electrodes and an accelerometer attached to the

electrodes. When in some epoch the acceleration of

the electrodes exceeds a certain value, EEG data

corresponding to that epoch were removed from

further analysis.

Moreover, there are few works where

accelerometer-only data were used to study

neurological diseases (Kutilek et al., 2010). In

(Danilov et al., 2008) the vestibular system is

considered as important in virtually every aspect of

our daily life. Head acceleration information is

essential for our adequate behavior in three-

dimensional space not only through vestibular

reflexes that act constantly on somatic muscles and

autonomic organs, but also through various cognitive

functions such as perception of self-movement,

spatial perception and memory, visual spatial

constancy, visual object motion perception. Thus,

accelerometer data can be used for subject’s

functional state classification in combination with

other sensors.

A small and light-weight wearable

electrocardiograph (ECG) equipment with a three-

axis accelerometer (x, y and z-axis) was developed

for prolonged monitoring of everyday stress (Okada

et al., 2013). In that study, the waveform of

acceleration data were used as the pattern for a

subject’s movement or posture in long-term

monitoring. In (Wu et al., 2015) two modalities of

sensors: HRV recorders and accelerometers were

integrated to monitor the stress levels in daily life.

The accuracy of stress level classification was

164

Syskov, A., Borisov, V., Tetervak, V. and Kublanov, V.

Feature Extraction and Selection for EEG and Motion Data in Tasks of the Mental Status Assessing - Pilot Study using Emotiv EPOC+ Headset Signals .

DOI: 10.5220/0006593001640172

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 1: BIODEVICES, pages 164-172

ISBN: 978-989-758-277-6

improved by 4.9% on average in comparison with

HRV-only feature set. Therefore, accelerometer data

can be used in long-term monitoring as tool to

identify areas of the signal with motion artifacts and

subject’s activity classify.

In our paper, we have used the Emotiv EPOC+

head set for gathering both motion and EEG data in

short-term experiments under several functional

states. The aim of the study is evaluation of feature in

EEG-only, accelerometer-only and integrated feature

spaces in series of short-term experiments.

2 MATERIALS AND METHODS

A series of experiments with headset Emotiv EPOC+

was carried out to study parameters which would

describe different subject mental status. The Emotiv

EPOC + headset provides information about the

induced electrical activity of the brain from 14

channels (David et al., 2014). This information

contains the voltage value for each electrode with a

sampling frequency of 128 Hz. Figure 1 depicted the

layout of the electrodes is AF3, F7, F3, FC5, T7, P7,

O1, O2, P8, T8, FC6, F4, F8, AF4 in standard 10-20

scheme. In addition, the motion data from three-axis

accelerometer integrated into the headset were

collected.

Each experiment contained five stages as

presented in Table 1. During each of the stages the

subject sits opposite the PC monitor and looks at the

screen with instruction and tasks.

Table 1: The cyclorama of the experiment.

Stage

Duration, sec

1. Rest state (RS)

300

2. TOVA test (T1)

180

3. Hyperventilation load (HL)

180

4. TOVA test (T2)

180

5. Aftereffect (AE)

300

Recording the stage of rest state involves

biomedical signals data from the subject, who looks

at the black screen and does nothing.

The next stage is carried out with the TOVA test,

which is the test of attention of variability - a psycho-

physiological test to evaluate conventional

performance related to attention and control of the

reaction. The Pebl software was used for the test

procedure. During the test squares and circles appears

alternately at the top and bottom of the computer

screen. The task of the subject is to press a space on

the keyboard when a square appears at the top of the

screen (Mueller and Piper, 2014).

The stage of hyperventilation is standard

functional load, when the subject often breathes

throughout the entire length of time, simulating

breathing during heavy sport loads.

Collected in each experiment raw EEG and

accelerometer data were saved into storage with

additional information about the subjects and events

marks (Borisov et al., 2017).

2.1 EEG Feature Engineering

Collected during the experiments raw EEG data were

processed in several steps of feature extraction and

selection. The process of feature engineering is

presented in Figure 1.

Figure 1: EEG feature engineering.

In the first step, all EEG data were transformed to

the frequency domain. To separate EEG – rhythms

from the signal, a second-order Butterworth bandpass

filter were applied. Rhythms borders were: Theta (4-

7) Hz, Alpha (7-15) Hz, Beta-Low (15-25) Hz, Beta-

High (25-31) Hz. Discrete Fourier transform method

was used for frequencies’ magnitudes extraction. As

result, four coefficients are calculated for each of 14-

th channel. Each coefficient is sum of magnitudes for

one of the rhythms. Thus, EEG data in frequency

domain are described as 56-dimension feature space.

After that, on the feature selection step (Egorova

et al., 2014), principal component analysis method

(PCA) (Jolliffe, 2014) in combination with linear

Feature Extraction and Selection for EEG and Motion Data in Tasks of the Mental Status Assessing - Pilot Study using Emotiv EPOC+

Headset Signals

165

discriminant analysis (LDA) (McLachlan, 1992), are

used for reducing 56-dimension feature space. Data

sets for analysis contained EEG recordings for all

subjects and the following pairs of stages: RS and HL;

RS and T1; T1 and HL.

LDA was used for evaluation of the principal

components pairs (Kublanov et al., 2016). The pair of

components with best accuracy and maximum

described variance were selected as base for new

feature space.

Finally, information about PCA loadings were

used for selecting EEG channels and frequency bands

as EEG new feature space. After that, supervised

learning methods were used for mental state

classification.

2.2 Accelerometer’s Feature Space

The Emotiv EPOC + headset, in addition to

information about the induced electrical activity of

the brain, provide data from a three-axis

accelerometer, which allows assessing the movement

of the headset in space during the experiment.

Accelerometer data is recorded to a separate file, each

record contains the values of the acceleration for each

axis and the data recording time. The scheme of the

accelerometer axis is shown in Figure 2.

Figure 2: Accelerometer axis orientation (“EMOTIV Epoc

- 14 Channel Wireless EEG Headset,” n.d.).

The three-axis accelerometer provides

information on the magnitude of the acting

accelerations along the three axes, respectively. The

acceleration value for each axis is registered through

equal time intervals. The signal measured by the

accelerometer is a linear sum of three components

(Machado et al., 2015):

• Body Acceleration Component (BA) is

acceleration resulting from body movement;

• Gravitation Acceleration Component (GA) is

acceleration resulting from gravity;

• Noise inherent to the measuring system.

GA provides information about the spatial orientation

of the device, and the BA provides information about

the movement of the device and subject’s head

movement. The frequency spectrum of accelerations

caused by human motion is located in the range from

0 to 20 Hz. The gravitational component is located in

the range from 0 to 0.3 Hz.

The component containing instrumental noise is

located generally in the range above 20 Hz. To isolate

the motion component from the signal, a second-

order Butterworth window filter with frequencies

from 0.3 to 20 Hz was applied (Mathie, 2003). In

Figure 3 the accelerometer signals before and after

filtering are represented. According to the article (Wu

et al., 2015), the most revealing motion data (MD)

features of the accelerometer signal are present in

Table 2.

Because of the discrete nature of the

accelerometer signal, ZCR was calculated as the

number of sections where the previous sign differs

from the current sign.

Activity - the value characterizing the change in the

signal over time was calculated by the following

formula (2):





























(1)

where 



= (







);

i-1

, x

are consecutive counts for x axis;

∆y, ∆z are calculated in same way for y and z axles

The average activity time is the ratio of the total

activity time, which exceeds the average level by

10%, to the number of stages exceeding this level.

The level of 10% was chosen as the most informative.

After calculating all features for each subject, the

data was written into the matrix F by N, where F is

the number of features, and N is the result of

multiplication the number of stages by the number of

subjects.

3 RESULTS

The results of feature selection for EEG in different

data sets are shown in section 3.1.

In section 3.2 results of classification for EEG-

only feature, accelerometer-only feature and

integrated feature space are shown.

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Figure 3: Accelerometer axes signal before and after filtering.

Table 2: MD features of the accelerometer signal.

MD feature

Description

Axis features

Max

Maximum value is the maximum acceleration value at a given time interval

Min

Minimum value is the minimum value of acceleration at a given time interval

Average value

The average value of acceleration at a given time interval

STD

Indicates the dispersion to the mean of the signal over time a given time interval

ZCR

Zero cross rate is the number of intersections by the zero signal.

Energy

Signal energy at a given time interval

Non axis features

Mean ZCR

Mean zero cross rate for three axes for current stage

Mean Energy

Mean energy for three axes for current stage

Activity

Characteristic of signal change

Average activity time

Mean time of high-level activity

3.1 EEG Feature Selection

Here we show the results for all subjects and

combination of stages in the following pair of data

sets:

1. HL and RS;

2. T1 and RS;

3. T1 and HL.

3.1.1 Hyperventilation Load and Rest Data

Set

Figure 4 depicted cumulative sum of variance for

principal components. The first two components

explained 82% of total variance.

Figure 4: Cumulative sum of variance for HL and RS.

Feature Extraction and Selection for EEG and Motion Data in Tasks of the Mental Status Assessing - Pilot Study using Emotiv EPOC+

Headset Signals

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After that, a classification was performed using

LDA for component 1 and 2. Training data contains

9 subjects for two classes RS and HL (functional rest

and hyper ventilation load).

Next step we calculate the prediction accuracy

estimation on an independent data sets by doing

cross-validation. During iterative procedure, we

remove one of the subject in training set. Figure 5

shows the result of classification. The average

accuracy of classification 94%.

Figure 5: Classification of subjects for HL and RS.

We used the equation (2) for interpretation of

LDA linear coefficients, where K – vector of

constant, L – vector of linear coefficients, v – data

vector.

K + L*v = 0

(2)

Figure 6 presents stats boxes for normalized linear

coefficients L from (2) for independent sets for PCA

scores 1 and 2. Component N-2 more significant for

discrimination on data set with two classes of RS and

HL.

Figure 6: LDA’s linear coefficients boxes for HL-RS.

Figure 7 (a) represents image plot of PCA-

loadings. Loadings are structured along the channels

and rhythms of the EEG. For each channel, the values

for Theta, Alpha, Beta-low and Beta-High EEG-

rhythms are presented. The EEG-rhythms order is

shown in the figure. The values of the loading are

normalized and a color scale is introduced.

Component number

(a)

(b)

(c)

Figure 7: Normalized PCA-loadings image plots.

a) – image plot of PCA-loadings for HL and RS data set;

b) – image plot of PCA-loadings for T1 and RS data set;

c) – image plot of PCA-loadings for T1 and HL data set.

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First component reflects variance caused by Theta

and Alpha frequency band activity. Its concerns all

EEG channel. Second component reflects variance

caused by changing loadings for AF3, P7, T7, O1, T8,

AF4 channels.

3.1.2 TOVA and Hyper Ventilation Data Set

Figure 8 depicted cumulative sum of variance for

principal components. The first two components

explained 86% of total variance.

Figure 8: Cumulative sum of variance for HL and T1.

In Figure 9 presented result of linear discriminant

analysis on training set for TOVA and rest data set.

The average accuracy of classification is 100%.

Figure 9: Classification of subjects for RS and T1.

Figure 10 depicts stats boxes for normalized linear

coefficients L form (2) for independent sets for

components 1 and 2.

As we can see, the both components are equally

significant for discrimination on data set with two

classes of RS and T1 load.

Figure 10: LDA’s linear coefficients boxes for T1-RS.

In Figure 7 (b) showed image plot of loadings.

First component reflects variance caused by Theta

and Alpha frequency band activity. Its concerns all

EEG channel. Second component reflects variance

caused by changing loadings for AF3 and O1

channels.

3.1.3 TOVA and Hyper Ventilation Data Set

Figure 11 presents cumulative sum of variance for

principal components. The first two components

explained 61 % of total variance. Sum of variances

for 1 and 2 components a sufficient less in

comparison with previous cases.

Figure 11: Cumulative sum of variance for HL and T1.

In this case, we try to classify subjects in spaces

for all pair combination of components with LDA.

The results with accuracy more than 70% depicted in

Table 3, where sum of variance for pair based on

Figure 12 data, weighted index is multiplication

accuracy and sum of variance for pair.

Feature Extraction and Selection for EEG and Motion Data in Tasks of the Mental Status Assessing - Pilot Study using Emotiv EPOC+

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Table 3: Pairs of components, accuracy and sum of

variance.

Component

pair

Accuracy

Sum of

variance for

pair

Weighted

index

1-7

0.83

0.39

0.32

2-7

0.78

0.29

0.23

1-2

0.72

0.61

0.44

Perform classification using discriminant analysis

for 1, 2 components with maximum of weighted

index. Figure 13 depicted result of linear discriminant

analysis on training set for TOVA and

hyperventilation data set.

Figure 12: Classification of subjects for HL and T1.

Figure 13 shows stats boxes for normalized linear

coefficients L form (1) for independent sets for

components 1 and 2. As we can see, the both

components are equally significant for discrimination

on data set with two classes of RS and T1 load.

Figure 13: LDA’s linear coefficients boxes HL-T1.

Figure 7 (c) presented image plot of loadings.

Second component reflects variance caused primary

by Alpha and Betta frequency band activity for O1

channel. Channels P7, F7 appears with significant

less loadings weights.

3.2 Classification in Integrated Feature

Space

Initially EEG feature vector contained 54 components

for 14 channels their rhythms borders were: Theta (4-

7) Hz, Alpha (7-15) Hz, Beta-Low (15-25) Hz, Beta-

High (25-31) Hz. Based on results in section 3.1 AF3,

T7, O1, T8, AF4 channels with Theta and Alpha

frequency bound are selected for EEG feature space.

Integrated features space was created from EEG

selected features and accelerometer MD features as

showed on Figure 15.

Figure 14: Integrated feature vector.

Full feature space for accelerometer as described

in section 2 are used. It need to be mentioned we don’t

use any weighted coefficients for selected EEG

features in model generalization purpose (Wolpaw

and Wolpaw, 2012).

LDA, Naïve Bayes (NB) and Decision Trees (DT)

classification methods are used for EEG feature

space. LDA method applied to finding linear

combinations of features that best distinguish object

classes. NB method - special case of the Bayesian

classifier. The method based on the assumption that

the objects are described by the statistically

independent variables. DT are nonparametric

method. This method does not require any

assumptions about the distribution of the variables in

each class (Kublanov et al., 2017).

The prediction accuracy evaluated on an

independent sets by doing “leave one out” cross-

validation (Refaeilzadeh et al., 2009). Table 4

contains the mean accuracy for 10 test data sets for all

five stages.

Table 4: Accuracy for EEG and MD feature space.

Accuracy, %

Method

EEG-only

MD-only

Integrated

LDA

72.4

89.3

86.7

68.9

89.3

86.7

71.6

84.0

92.0

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The best result for integrated feature space have

higher prediction accuracy 92.0%, in comparison

with EEG-only (72.4 %) mental status evaluation for

AF3, T7, O1, T8, AF4 channels. The best results for

MD-only data are 89.3% for LDA and NB methods.

4 DISCUSSION AND

CONCLUSION

According to the results of the of EEG data analysis

for various combinations of functional loads, the most

informative channels for Theta and Alpha rhythms in

the frontal, hip and occipital areas were identified.

For three different classifiers, the best accuracy of

classification of the five functional states in the EEG

generated characteristic space is at the level of 72.4%

for LDA method.

In turn, the classification in the attribute space of

the accelerometer, for the LDA and NB classifiers,

allows to reach 89.3% accuracy of identification of

five functional states. The processing of the joint

indicative space of the EEG –MD integrated core

features allowed to increase the classification

accuracy to 92.0% for DT method.

The results obtained in this paper reflect changes

in the power levels of the EEG indices in various

functional states, which makes it possible to

characterize the functional state of a person. The

decrease in the control effect of the cerebral cortex

(alpha-rhythm activity) increases the amplitude of the

average acceleration of the head movement. The

rather high classification accuracy obtained for the

signs of EEG signals isolated using the PCA method

suggests that changes in physiological processes

underlie these changes.

An increase in the accuracy of classification (on

19.6% in comparison with EEG-only feature), when

using the characteristics of both feature spaces can

mean that each of the signals carries information only

about a part of the changes in functional processes.

Thus, the task of determining the relationship

between EEG signals and the accelerometer on a

wider set of functional samples, when classifying

different mental states of a person at short time

intervals, is promising.

ACKNOWLEDGEMENTS

The work was supported by Act 211 Government of

the Russian Federation, contract № 02.A03.21.0006.

REFERENCES

Borisov, V., Syskov, A., Tetervak, V., Kublanov, V., 2017.

Mobile Brain - Computer Interface Application for

Mental Status Evaluation., in: Proceedings - 2017

International Multi-Conference on Engineering,

Computer and Information Sciences SIBIRCON.

Presented at the 2017 International Multi-Conference

on Engineering, Computer and Information Sciences

SIBIRCON, IEEE, Novosibirsk, Russia.

Danilov, Y.P., Tyler, M.E., Kaczmarek, K.A., 2008.

Vestibular sensory substitution using tongue

electrotactile display, in: Human Haptic Perception:

Basics and Applications. Birkhäuser Basel, pp. 467–

480. doi:10.1007/978-3-7643-7612-3_39.

David, H., Whitaker, K.W., Ries, A.J., Vettel, J.M.,

Cortney, B., Kerick, S.E., McDowell, K., 2014.

Usability of four commercially-oriented EEG systems.

J. Neural Eng. 11, 046018. doi:10.1088/1741-

2560/11/4/046018.

Egorova, D.D., Kazakov, Y.E., Kublanov, V.S., 2014.

Principal Components Method for Heart Rate

Variability Analysis. Biomed. Eng. 48, 37–41.

doi:10.1007/s10527-014-9412-7.

EMOTIV Epoc - 14 Channel Wireless EEG Headset

[WWW Document], n.d.. Emotiv. URL https://

www.emotiv.com/epoc/ (accessed 9.5.17).

Jolliffe, I., 2014. Principal Component Analysis, in: Wiley

StatsRef: Statistics Reference Online. John Wiley &

Sons, Ltd. doi:10.1002/9781118445112.stat06472.

Kublanov, V., Dolganov, A., Borisov, V., 2016.

Application of the discriminant analysis for diagnostics

of the arterial hypertension: Analysis of short-term

heart rate variability signals. Presented at the

NEUROTECHNIX 2016 - Proceedings of the 4th

International Congress on Neurotechnology,

Electronics and Informatics, pp. 45–52.

Kublanov, V.S., Dolganov, A.Y., Belo, D., Gamboa, H.,

2017. Comparison of Machine Learning Methods for

the Arterial Hypertension Diagnostics [WWW

Document]. Appl. Bionics Biomech.

doi:10.1155/2017/5985479.

Kutilek, P., Charfreitag, J., Hozman, J., 2010. Comparison

of Methods of Measurement of Head Position in

Neurological Practice, in: XII Mediterranean

Conference on Medical and Biological Engineering

and Computing 2010, IFMBE Proceedings. Springer,

Berlin, Heidelberg, pp. 455–458. doi:10.1007/978-3-

642-13039-7_114.

Lin, Y.-P., Jung, T.-P., 2017. Improving EEG-based

emotion classification using conditional transfer

learning. Front. Hum. Neurosci. 11.

doi:10.3389/fnhum.2017.00334.

Machado, I.P., Luísa, G., Gamboa, H., Paixão, V., Costa,

R.M., 2015. Human activity data discovery from

triaxial accelerometer sensor: Non-supervised learning

sensitivity to feature extraction parametrization. Inf.

Process. Manag. 51, 201–214.

doi:10.1016/j.ipm.2014.07.008.

Feature Extraction and Selection for EEG and Motion Data in Tasks of the Mental Status Assessing - Pilot Study using Emotiv EPOC+

Headset Signals

171

Mathie, M., 2003. Monitoring and Interpreting Human

Movement Patterns Using a Triaxial Accelerometer.

McLachlan, G.J., 1992. Discriminant Analysis and

Statistical Pattern Recognition: McLachlan/

Discriminant Analysis & Pattern Recog, Wiley Series

in Probability and Statistics. John Wiley & Sons, Inc.,

Hoboken, NJ, USA. doi:10.1002/0471725293.

Mueller, S.T., Piper, B.J., 2014. The Psychology

Experiment Building Language (PEBL) and PEBL Test

Battery. J. Neurosci. Methods 222, 250–259.

doi:10.1016/j.jneumeth.2013.10.024.

Okada, Y., Yoto, T.Y., Suzuki, T., Sakuragawa, S., Sugiura,

T., 2013. Wearable ECG recorder with acceleration

sensors for monitoring daily stress: Office work

simulation study. Presented at the Proceedings of the

Annual International Conference of the IEEE

Engineering in Medicine and Biology Society, EMBS,

pp. 4718–4721. doi:10.1109/

EMBC.2013.6610601.

Refaeilzadeh, P., Tang, L., Liu, H., 2009. Cross-Validation,

in: LIU, L., ÖZSU, M.T. (Eds.), Encyclopedia of

Database Systems. Springer US, pp. 532–538.

doi:10.1007/978-0-387-39940-9_565.

Silva, H.P. da, Fred, A., Martins, R., 2014. Biosignals for

Everyone. IEEE Pervasive Comput. 13, 64–71.

doi:10.1109/MPRV.2014.61.

So, W.K.Y., Wong, S.W.H., Mak, J.N., Chan, R.H.M.,

2017. An evaluation of mental workload with frontal

EEG. PLoS ONE 12. doi:10.1371/

journal.pone.0174949.

Sun, F.-T., Kuo, C., Cheng, H.-T., Buthpitiya, S., Collins,

P., Griss, M., 2012. Activity-aware mental stress

detection using physiological sensors. Lect. Notes Inst.

Comput. Sci. Soc.-Inform. Telecommun. Eng. LNICST

76 LNICST, 211–230.

Wolpaw, J., Wolpaw, E.W., 2012. Brain-Computer

Interfaces: Principles and Practice. Oxford University

Press, USA.

Wu, M., Cao, H., Nguyen, H.-L., Surmacz, K., Hargrove,

C., 2015. Modeling perceived stress via HRV and

accelerometer sensor streams. Presented at the

Proceedings of the Annual International Conference of

the IEEE Engineering in Medicine and Biology Society,

EMBS, pp. 1625–1628. doi:10.1109/

EMBC.2015.7318686.

Wu, Y., Miwa, T., Uchida, M., 2017. Using physiological

signals to measure operator’s mental workload in

shipping–an engine room simulator study. J. Mar. Eng.

Technol. 16, 61–69. doi:10.1080/20464177

.2016.1275496.

BIODEVICES 2018 - 11th International Conference on Biomedical Electronics and Devices

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