A MEG Study of Different Motor Imagery Modes in Untrained Subjects

for BCI Applications

Alexander E. Hramov

1 a

, Elena N. Pitsik

1 b

, Parth Chholak

, Vladimir A. Maksimenko

1 c

Nikita S. Frolov

1 d

, Semen A. Kurkin

1 e

and Alexander N. Pisarchik

1,2 f

Neuroscience and Cognitive Technology Laboratory, Innopolis University, 1 Universitetskaya str., Innopolis, 420500,

The Republic of Tatarstan, Russia

Center for Biomedical Technology, Technical University of Madrid, Campus Montegancedo, 28223 Pozuelo de Alarc

on,

Madrid, Spain

Keywords:

Brain-computer Interface, MEG, Motor Imagery, Exoskeleton, HCA, Artiﬁcial Neural Network, Wavelet

Analysis.

Abstract:

Motor imagery is a most commonly studied neurophysiological pattern that is used in brain-computer inter-

faces as a command for exoskeletons, bioprostheses, wheelchair and other robotic devices. The mechanisms

of motor imagery manifestation in human brain activity include dynamics of motor-related frequency bands

in various brain areas, among which the most common is sensorimotor rhythnm. In present work we con-

sider time-frequency structure of magnitoencephalographical (MEG) motor imagery in untrained subjects.

We conduct series of experiments to collect MEG motor imagery dataset in untrained subjects. We conﬁrm

the emergence of two types of motor imagery – visual (VI) and kinesthetic (KI), which cause different types

of event-related potentials (ERP) dynamics and require different approaches to classiﬁcation using mashine

learning methods. We also reveal the impact of dataset optimization on the artiﬁcial neural network perfor-

mance, which is essential topic in brain-computer interface (BCI) development. We show that developing

classiﬁcation stratedy based on time-frequency features of the particular MEG signal can increase classiﬁca-

tion accuracy of the VI mode to the level of the KI.

1 INTRODUCTION

Known brain-computer interfaces (BCIs) applications

under study include: mental control of robotic de-

vices such as exoskeletons and prostheses for peo-

ple with motor disabilities, which allows to perform

basic tasks such as relocation in a wheelchair, limbs

movement etc. (Mirza et al., 2015); real-time recog-

nition of cognitive activity, i.e. emotions, alertness

and concentration (Victorino et al., 2015), which is

widely used in support systems for education and pro-

fessional skills training; neurofeedback for rehabilita-

tion based on motor imagery (Yu et al., 2015). The

focus of this study is active BCIs, which provide feed-

back to the user based on the brain activity measure-

https://orcid.org/0000-0003-2787-2530

https://orcid.org/0000-0003-1850-2394

https://orcid.org/0000-0002-4632-6896

https://orcid.org/0000-0002-2788-1907

https://orcid.org/0000-0002-3438-5717

https://orcid.org/0000-0003-2471-2507

ments and feature extraction in order to alter this ac-

tivity in the ”right direction” (Nijboer et al., 2009).

The most widely used pattern is motor imagery (MI)

for application in BCI for exoskeleton or robotic con-

trol (Frolov et al., 2017; Meng et al., 2016). Recog-

nition and classiﬁcation of MI using artiﬁcial intelli-

gence methods is a challenging task, despite that some

of the features of this pattern are well-known.

Sensorimotor rhythm (SMR) is perhaps the most

common in the scientiﬁc literature in the context of

motor imagery application in BCI (McFarland and

Wolpaw, 2005; K

ubler et al., 2005). SMR is an os-

cillatory idle rhythm of synchronized electromagnetic

brain activity. It appears in spindles in recordings

of EEG, MEG, and ECoG with the frequency range

of 7 − 13 Hz (Arroyo et al., 1993). SMR training

for BCI is widely employed technique for BCI-based

therapy, which often includes muscle stimulation or

exoskeleton control (Norman et al., 2018). Any mo-

tor action, including real movements, motor prepara-

tion and motor imagery, are resulting in changes in

188

Hramov, A., Pitsik, E., Chholak, P., Maksimenko, V., Frolov, N., Kurkin, S. and Pisarchik, A.

A MEG Study of Different Motor Imagery Modes in Untrained Subjects for BCI Applications.

DOI: 10.5220/0007810001880195

In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019), pages 188-195

ISBN: 978-989-758-380-3

SMR dynamics: so-called event-related desynchro-

nization and synchronization (ERD/ERS). It is nec-

essary to emphasize that real motor action is not re-

quired for changes in the SMR amplitude (Yi et al.,

2016;

Ozt

urk and Yilmaz, 2018). The user of the BCI

can train to generate ERD or ERS in SMR (Penaloza

et al., 2018; Tacchino et al., 2017) in response for ex-

ternal stimulation, which can be biological feedback

integrated in BCI.

One of the most important problem which accom-

panies the development of SMR-based BCI is the

variability of the neural activity from one subject to

another(Ferreira et al., 2008; Ranky and Adamovich,

2010; Murphy et al., 2017). This variability is es-

pecially well pronounces in case of untrained sub-

jects, because such BCI user may try different ways

to perform MI without knowing which one of them

BCI will ”understand”. It is known that training can

partially resolve this issue (Duann and Chiou, 2016).

The other possible solution is to use additional in-

formation about the nature of the signals. Recently,

the practice of dividing MI modes on visual (VI) or

kinesthetic imagery (KI) has become widespread (La

Touche et al., 2018; Mehler et al., 2019a). These

two perspectives are signiﬁcantly different and are

pronounced in neurophysiological activity in differ-

ent ways (Filgueiras et al., 2018): during the VI,

the subject imagines him/herself performing an ac-

tion from the from the third-person point of view, or

”looks throughout his/her own eyes” (Callow et al.,

2017). During the KI the subject imagines the feeling

and experience of movements without overt move-

ment (Mehler et al., 2019b; Hanakawa, 2016).

In present work, we consider time-frequency

structure of the MEG signal corresponding to these

two MI modes in untrained subjects. We evaluate

changes in event-related potentials amplitude in sen-

sorimotor rhythm, particularly focusing on µ− and

β−frequency bands, where ERD during motor activ-

ity emerges (Duann and Chiou, 2016; Maksimenko

et al., 2018). Then, we applied machine learning algo-

rithms to detect clusters among selected features and

then check how different frequency components af-

fect the artiﬁcial neural networks performance. The

results and observations focused in present paper pro-

vide the deeper insight into the VI and KI modes of

motor imagery and allow us to implement an optimal

classiﬁcation approach for both groups of subjects.

2 METHODS

2.1 Experimental Setup and Data

Preparation

First, we performed series of experiments to collect

magnetoencephalographical (MEG) MI dataset in un-

trained subjects. All experimental work was con-

ducted using the equipment of the Laboratory of Cog-

nitive and Computational Neuroscience (CTB, Tech-

nical University of Madrid, Spain). Seven volunteers

in age of 20–31 participated in the experimental study.

During the experimental sessions, participants were

sitting in the chair in the comfortable posture, which

allows to minimize any motor activity throughout the

experiment. A screen was installed in front of the

chair, which is used to transmit commands at each

stage of the experiment. To divide one trial from an-

other, audio signals were tuned on at random intervals

of 6-8 seconds.

The whole experimental session consisted of four

parts, each part included equal number of left or right

arm motor imagery tasks. Each part was preceded by

the announcement on the screen in front of the sub-

ject, and short audio signals was informing subjects

about the trial start.

306-channel (204 planar gradiometers and 102

magnetometers) MEG-recordings with sampling fre-

quency 1000 Hz was collected using Vectorview

MEG system (Elekta AB, Stockholm, Sweden). An

online anti-alias ﬁlter with cutoffs between 0.1 Hz

and 300 Hz was applied. Further preparations of the

data was conducted using Brainstorm application for

MATLAB and included selection of 5-seconds trials

corresponding to each motor imagery task and 20-

seconds fragments corresponding to the resting state

with closed eyes according to the experimental proto-

col.

2.2 Time-frequency Wavelet-based

Analysis

We used wavelet-based approach to perform time-

frequency analysis of obtained data. For each MEG-

channel X

(t) the wavelet energy spectrum was calcu-

lated:

( f ,t) =

( f ,t)

) (1)

where W

( f ,t):

( f ,t) =

t−

(t)ψ

∗

( f ,t)dt, (2)

A MEG Study of Different Motor Imagery Modes in Untrained Subjects for BCI Applications

189

is complex-valued wavelet coefﬁcients with MEG

channel number n = 1,...,102, ∗ standing for complex

conjugation and ψ( f ,t) is a mother wavelet function,

which in our case is the Morlet wavelet with central

frequency ω

= 2π, f

= 2π:

ψ( f ,t) =

f π

1/4

jω

f (t−t

)

f (t−t

)

, (3)

Morlet wavelet is often used in medicine for

analysis of neurological data and a heartbeat be-

haviour (Morlet et al., 1993).

Since we considering ERP in the context of mo-

tor imagery, we calculated values of wavelet energy

(t) and E

(t) for each channel n in frequency bands

β (8–13 Hz), and µ (15–30 Hz), where ERD and ERS

are well-known to be observed during both real and

imagery motor activity:

µ,β

(t) =

∆ f

f ∈µ,β

( f ,t)d f . (4)

20 40

-20

-40

-20

-40

-60

∆β, %

∆μ,%

Figure 1: MI wavelet energy differences for each of 7 sub-

jects.

The values of ERP for each type of trial, i.e. left

hand (L

(t) and L

(t)), right hand (R

(t) and R

(t))

and resting state (B

(t) and B

(t)), were values of

wavelet energies E

(t) and E

(t) averaged over the

trials.

ERD and ERS corresponding to the right and left

hand MI were calculated as integral differences δL

δL

and δR

, δR

between MI trials and the resting

state:

δL

µ,β

t∈T



µ,β

(t) − B

µ,β

(t)



dt, (5)

δR

µ,β

t∈T



µ,β

(t) − B

µ,β

(t)



dt, (6)

with T = 3 s standing for the trial length.

2.3 Hierarchical Cluster Analysis

(HCA)

One of the goals of this article is to reveal two groups

of the subjects corresponding to the different types of

motor imagery – VI and KI. We used the hierarchi-

cal cluster analysis (HCA), which is a machine learn-

ing method that measures the dissimilarity between

objects and unites objects into distinct subgroups, or

clusters. There are two main types of HCA: ag-

glomerative, which initially considers each element

as single-element cluster and combines it with other

elements into the bigger clusters, until all elements

became members of a cluster, and divisive hierar-

chical clustering, which is an inverse version of ag-

glomerative clustering. We applied complete-linkage

clustering (Defays, 1977), which is the agglomerative

clustering method that results in a dendrogram that

can give an insight into the clusters hierarchy. This

method is also known as a farthest neighbour clus-

tering (Fraley and Raftery, 1998), and the link be-

tween two clusters is also considered as a farthest dis-

tance between two objects in an M-dimensional fea-

ture space with M features describing, in our case, the

motor imagery trial of the subject. We calculated the

complete-linkage function as:

D(X,Y ) = max

x∈X,y∈Y

d(x,y), (7)

where x and y are objects in considered clusters X and

Y , respectively, and d(x,y) is the distance between

two objects in a feature space calculated using Eu-

clidean metric:

d(x,y) =

∑

i=1

− y

)

, (8)

where x

and y

are an i-th feature of the x and y ob-

jects, respectively.

In present paper, we consider the differences δL

µ,β

and δR

µ,β

described in previous subsection as features

of the MI. We introduced the pair (∆

, ∆

), a two-

dimensional space, objects in which describe MI type

of all subjects. Here, ∆

µ,β

are the wavelet energy dif-

ferences averaged over the hand type:

∆

δL

+ δR

, (9)

ICINCO 2019 - 16th International Conference on Informatics in Control, Automation and Robotics

190

(a)

(b)

-20-40

-20

-40

-60

∆β, %

∆μ,%

20 40-20

-40

-20

-40

-60

∆β, %

∆μ,%

Figure 2: Results of subject clustering to two groups according to the MI type using HCA.

∆

δL

+ δR

. (10)

Finally, due to the high-dimensionality of consid-

ering feature space (204 for each limb in case with

N = 102 MEG channels), we averaged ∆

µ,β

over the

channels:

∆

µ,β

∑

n=1

∆

µ,β

in order to reduce it.

2.4 Artiﬁcial Neural Network (ANN)

We used multilayer perceptron (MLP) as an artiﬁcial

intelligence algorithm to test classiﬁability of MEG

motor imagery pattern with different MI modes. MLP

employs feedforward structure with one input and one

output layers and several hidden layers. In our study,

we used MLP consisted of three hidden layers with

30, 15 and 5 neurons, one neuron on output layer and

the hyperbolic tangent sigmoid as an activation func-

tion. We applied the Levenberg-Marquardt algorithm

as an optimization method, which is a least-squares

estimation algorithm based on the idea of the maxi-

mum neighbourhood (

Ubeyli, 2009).

Before the data was fed to the MLP input, it under-

went a series of necessary pre-processing. First of all,

data was ﬁltered using a low-pass ﬁlter with various

cutoff frequency ranges. Then, we shufﬂed the data

and splitted it into the training and the test samples

that were 75% and 25% of the whole dataset, respec-

tively.

3 RESULTS

Fig. 1 represents the results of HCA, where each

dot represents the wavelet energy difference for each

MEG channel, each group of subjects has a different

colour and big dots are wavelet energy differences av-

eraged over all channels for each subject, represent-

ing individual wavelet characteristic of MI. Here, ∆

and ∆

are the wavelet coefﬁcients for correspond-

ing frequency bands µ and β, which reﬂect changes

in these frequency bands amplitudes associated with

motor imagery for each MEG-channel – in another

words, emergence of ERP. The vertical and horizon-

tal dashed lines corresponding to the zero values of

∆

and ∆

, separating ERD and ERS for both of the

frequency bands.

Thus, all dots placed above the vertical line and to

the right from vertical line are representing the MEG

channels, where ERS is observed in µ and β- fre-

quency bands, respectively. On the other hand, the

negative values of ∆

and ∆

are corresponding to

the decrease of ERP amplitude, or, in other words, to

ERD.

Fig. 2(a) shows the result of HCA clustering of in-

dividual MI characteristics of each subject (subjects

are represented as big numbered dots). Here we ob-

tained a quite good clustering, allowing us to distin-

guish two large groups of the subjects, corresponding

to the VI and KI, where three subjects belong to the

KI group (2, 3 and 6-th, red dots in Fig. 2(a)) and four

A MEG Study of Different Motor Imagery Modes in Untrained Subjects for BCI Applications

191

5-15 15-30 30-45 45-60

Accuracy, %

Cutoff frequency, Hz

5-15 15-30 30-45 45-60

Cutoff frequency, Hz

(a)

(b)

Figure 3: Results of MEG trials classiﬁcation for (a) KI and

(b) VI groups of subjects. Results are presented with the

standard deviation.

– to the VI(1, 4, 5 and 7-th, green dots in Fig. 2(a)).

As it can be seen from Fig. 2(a), the KI group can be

described by decrease of both β and µ rhythms – red

dots are mostly placed to the left from the zero value

of ∆

and below the zero value of ∆

. It is well-

known that ERD in these frequency bands is associ-

ated with motor activity, thus we can conclude that

subjects in this group were generating motor-related

activity during motor imagery.

The other picture is provided by the subjects from

VI group. As it was expected, VI is mostly associ-

ated with the slight or moderate enhancement of µ-

rhythm, which is a sign of visual processing or prepa-

ration for movement (Jones et al., 2010). The same

changes are observed in the β-band. Moreover, as

Fig. 2(b) shows, the subjects from the red group are

generally characterized by well-pronounced ERD in

both frequency bands, while the subjects from the

green group provided different type behaviour. This

close-to-ERS behaviour reﬂects the main difference

between two group of subjects – while the subjects

from the red group have a tendency to exercise, the

subjects from the green group are mostly prone to the

cognitive load.

The next stage of the research included classiﬁ-

cation of MEG trials with an artiﬁcial neural network.

Here, we compare the results of the recognition in two

groups of subjects selected above and apply low-pass

ﬁlters with different cutoffs. Fig. 3 shows the results

of classiﬁcation. We can see that using the same low-

pass ﬁlter results in opposite scenarios in these two

groups. For example, here we can see (Fig. 3(a)))

that ﬁltering in range 5 − 15 Hz has the most pos-

itive effect on the classiﬁcation accuracy in the VI

group. At the same time, the same ﬁlter causes signif-

icant decrease in the accuracy for the KI subjects (see

Fig. 3(b)). Other ﬁlter ranges cause sharp drop in clas-

siﬁcation quality, and then the accuracy does not rise

above 45% with increasing ﬁltering cutoffs. Fig. 4

represents the analysis of individual accuracy rates,

which evidences that minimal classiﬁcation accuracy

level is reached with [20,40] Hz ﬁltration. These re-

sults suggest that low-frequency MEG components

are most important in classiﬁcation of untrained sub-

jects MI, which a conclusion also previously made in

our earlier study (Maksimenko et al., 2018).

Fig. 3(b) provides another picture. As it was men-

tioned above, KI classiﬁcation results are diametri-

cally opposed to the VI results. Here we can see that

the best results are achieved when high-frequency ﬁl-

ters are applied. On the contrary, ﬁlter with cutoffs

5 − 15 Hz provides 44% accuracy with SD ±12%,

and the increase in the ﬁltration cutoffs results in ac-

curacy increasing up to 55% ± 8 SD. The individ-

ual accuracy for the KI group continues to increase

though, reaching it’s maximal value at cutoffs [40,60]

Hz (red circles in Fig.4). Unlike the VI subjects,

the KI group produces more MI-related information

in the frequency components > 15 Hz. This results

can be an evidence that the subjects that prone to KI

produce the MI activity similar to preparation to the

movement execution.

One of the most topical problem of BCI develop-

ment is the data reduction or optimization. MEG data

consisted of 102 channel recordings is a large dataset,

which requires high computing power for it’s process-

ing. With the aim of dataset optimization we reduce

number of MEG channels and study the changes in

ANN performance. We chose 14 channels located

above the motor cortex. Fig. 5 presents results of our

ICINCO 2019 - 16th International Conference on Informatics in Control, Automation and Robotics

192

Figure 4: Minimal and maximal accuracy in VI and KI, represented as squares and circles, respectively.

computations.

Accuracy, %

5-15 15-30 30-45 45-60

Cutoff frequency, Hz

(a)

(b)

5-15 15-30 30-45 45-60

all channels

optimized channels

Figure 5: Results of MEG trials classiﬁcation for optimized

and unoptimized dataset for (a) VI and (b) KI groups of

subjects. Results are presented with the standard deviation.

For the VI (see Fig. 5(a)) optimization played a

positive part in the context of classiﬁcation perfor-

mance: one can see that accuracy for cutoff frequency

15 − 30 Hz increased to the level of 5 − 15 Hz. In

general, for VI group the optimization of channel set

resulted in the increase or at least the absence of sig-

niﬁcant changes of the classiﬁcation accuracy. Re-

cent studies (Graimann et al., 2004) of time-frequency

structure of electroencephalography revealed that MI

can be pronounced not only in the motor-related cor-

tex, but also in several remote brain areas, which in

combination with our results can evidence that VI pat-

tern, which can be pronounced as various brain activ-

ity patterns, can emerge in different brain areas.

Returning to the KI group on Fig. 5(b), we can

see that optimization did not signiﬁcantly affect the

classiﬁcation accuracy, even resulting in decrease of

neural network performance.

4 CONCLUSION

Classifying motor imagery in untrained subjects into

two groups – kinesthetic and visual imagery – is a

well-known practice in recent studies. To implement

an effective and robust system for classiﬁcation of

MI corresponding to the different limbs, one need to

study different scenarios of MI pattern emergence,

especially in untrained subjects. A widely known

strategy is to study an enhancement and decrease of

the sensorimotor rhythm, so-called event-related syn-

chronization and desynchronization – two phenomena

related to the motor activity, both real and imagery. In

present paper we studied MI of untrained subjects in

order to select two types of MI which causes different

sensorimotor rhythm pattern and provide a deeper in-

sight into the sensorimotor rhythm dynamics, related

to these two MI modes. We show that KI shows bet-

ter classiﬁcation results than VI (67% vs 56%) and

in general is more pronounced in the context of time-

frequency structure of the magnitoencephalographi-

cal signal.

At the ﬁrst stage of our study, we analysed the

amplitude changes of event-related potentials in sen-

sorimotor rhythm. Cluster analysis allowed us to di-

vide all subjects into two groups depending on the MI

mode. We revealed that the subjects from KI group

A MEG Study of Different Motor Imagery Modes in Untrained Subjects for BCI Applications

193

demonstrated event-related desynchronization in both

β and µ frequency bands, which is expected result

considering that the ”nature” of the KI pattern is more

related to the motor action (Neuper et al., 2005; Guil-

lot et al., 2009). On the contrary, the event-related

synchronization was found in the VI subjects.

Then, we enhanced our results using artiﬁcial neu-

ral network. First, we classiﬁed unoptimized magni-

toencephalographical dataset with 102 channels and

achieved up to 70% accuracy using the low-frequency

ﬁlter with cutoff below 15 Hz. The same level of ac-

curacy was achieved with KI by applying the high-

pass ﬁlter with cutoff above 30 Hz. Finally, in or-

der to see how data optimization affects the artiﬁcial

neural network performance, we selected 14 channels

over the motor-related area and revealed no signiﬁ-

cant changes for KI. On the other hand, the VI group

shown the possibility to enhance artiﬁcial neural net-

work performance with particular set of channels and

frequency cutoffs.

Thus, despite the fact that KI is easier to clas-

sify and the KI pattern is more pronounced in time-

frequency structure of the MEG signal, there is a

possibility to achieve comparable results with the

VI. Since the VI mode is more common for un-

trained subjects, we suggest that obtained results can

be useful for implementation of the artiﬁcial intelli-

gence systems for MI patterns classiﬁcation for brain-

computer interfaces, exoskeletons, wheelchairs and

other robotics devices.

ACKNOWLEDGEMENTS

This work has been supported by Russian Science

Foundation (Grant 17-72-30003).

REFERENCES

Arroyo, S., Lesser, R. P., Gordon, B., Uematsu, S., Jackson,

D., and Webber, R. (1993). Functional signiﬁcance of

the mu rhythm of human cortex: an electrophysiologic

study with subdural electrodes. Electroencephalogra-

phy and clinical neurophysiology, 87(3):76–87.

Callow, N., Jiang, D., Roberts, R., and Edwards, M. G.

(2017). Kinesthetic imagery provides additive beneﬁts

to internal visual imagery on slalom task performance.

Journal of Sport and Exercise Psychology, 39(1):81–

86.

Defays, D. (1977). An efﬁcient algorithm for a complete

link method. The Computer Journal, 20(4):364–366.

Duann, J.-R. and Chiou, J.-C. (2016). A Comparison

of Independent Event-Related Desynchronization Re-

sponses in Motor-Related Brain Areas to Movement

Execution, Movement Imagery, and Movement Ob-

servation. PLOS ONE, 11(9):1–16.

Ferreira, A., Celeste, W. C., Cheein, F. A., Bastos-Filho,

T. F., Sarcinelli-Filho, M., and Carelli, R. (2008).

Human-machine interfaces based on EMG and EEG

applied to robotic systems. Journal of NeuroEngineer-

ing and Rehabilitation, 5(1):10.

Filgueiras, A., Conde, E. F. Q., and Hall, C. R. (2018).

The neural basis of kinesthetic and visual imagery in

sports: an ale meta- analysis. Brain imaging and be-

havior, 12(5):1513–1523.

Fraley, C. and Raftery, A. E. (1998). How many clusters?

which clustering method? answers via model-based

cluster analysis. The computer journal, 41(8):578–

588.

Frolov, A. A., Mokienko, O., Lyukmanov, R., Biryukova,

E., Kotov, S., Turbina, L., Nadareyshvily, G., and

Bushkova, Y. (2017). Post-stroke rehabilitation train-

ing with a motor-imagery-based brain-computer inter-

face (BCI)-controlled hand exoskeleton: a random-

ized controlled multicenter trial. Frontiers in neuro-

science, 11:400.

Graimann, B., Huggins, J. E., Levine, S. P., and

Pfurtscheller, G. (2004). Toward a direct brain

interface based on human subdural recordings and

wavelet-packet analysis. IEEE Transactions on

Biomedical Engineering, 51(6):954–962.

Guillot, A., Collet, C., Nguyen, V. A., Malouin, F.,

Richards, C., and Doyon, J. (2009). Brain activity dur-

ing visual versus kinesthetic imagery: an fmri study.

Human brain mapping, 30(7):2157–2172.

Hanakawa, T. (2016). Organizing motor imageries. Neuro-

science research, 104:56–63.

Jones, S. R., Kerr, C. E., Wan, Q., Pritchett, D. L.,

ainen, M., and Moore, C. I. (2010). Cued spa-

tial attention drives functionally relevant modulation

of the mu rhythm in primary somatosensory cortex.

Journal of Neuroscience, 30(41):13760–13765.

ubler, A., Nijboer, F., Mellinger, J., Vaughan, T. M.,

Pawelzik, H., Schalk, G., McFarland, D. J., Bir-

baumer, N., and Wolpaw, J. R. (2005). Patients with

ALS can use sensorimotor rhythms to operate a brain-

computer interface. Neurology, 64(10):1775–1777.

La Touche, R., Grande-Alonso, M., Cuenca-Mart\’\inez,

F., G

onz

alez-Ferrero, L., Suso-Mart\’\i, L., and

Paris-Alemany, A. (2018). Diminished Kinesthetic

and Visual Motor Imagery Ability in Adults With

Chronic Low Back Pain. PM&R.

Maksimenko, V. A., Pavlov, A., Runnova, A. E., Nedaivo-

zov, V., Grubov, V., Koronovskii, A. A., Pchelintseva,

S. V., Pitsik, E., Pisarchik, A. N., and Hramov, A. E.

(2018). Nonlinear analysis of brain activity, associated

with motor action and motor imaginary in untrained

subjects. Nonlinear Dynamics, 91(4):2803–2817.

McFarland, D. J. and Wolpaw, J. R. (2005). Sensorimotor

rhythm-based brain-computer interface (BCI): feature

selection by regression improves performance. IEEE

Transactions on Neural Systems and Rehabilitation

Engineering, 13(3):372–379.

ICINCO 2019 - 16th International Conference on Informatics in Control, Automation and Robotics

194

Mehler, D. M. A., Williams, A. N., Krause, F., L

uhrs, M.,

Wise, R. G., Turner, D. L., Linden, D. E. J., and Whit-

taker, J. R. (2019a). The BOLD response in primary

motor cortex and supplementary motor area during

kinesthetic motor imagery based graded fMRI neuro-

feedback. Neuroimage, 184:36–44.

Mehler, D. M. A., Williams, A. N., Krause, F., Luhrs, M.,

Wise, R. G., Turner, D. L., Linden, D. E. J., and Whit-

taker, J. R. (2019b). The BOLD response in primary

motor cortex and supplementary motor area during

kinesthetic motor imagery based graded fMRI neuro-

feedback. NeuroImage, 184:36–44.

Meng, J., Zhang, S., Bekyo, A., Olsoe, J., Baxter, B., and

He, B. (2016). Noninvasive electroencephalogram

based control of a robotic arm for reach and grasp

tasks. Scientiﬁc Reports, 6:38565.

Mirza, I. A., Tripathy, A., Chopra, S., D’Sa, M., Ra-

jagopalan, K., D’Souza, A., and Sharma, N. (2015).

Mind-controlled wheelchair using an EEG headset

and arduino microcontroller. In Technologies for Sus-

tainable Development (ICTSD), 2015 International

Conference on, pages 1–5. IEEE.

Morlet, D., Peyrin, F., Desseigne, P., Touboul, P., and

Rubel, P. (1993). Wavelet analysis of high-resolution

signal-averaged ECGs in postinfarction patients. Jour-

nal of Electrocardiology, 26(4):311–320.

Murphy, D. P., Bai, O., Gorgey, A. S., Fox, J., Lovegreen,

W. T., Burkhardt, B. W., Atri, R., Marquez, J. S.,

Li, Q., and Fei, D.-Y. (2017). electroencephalogram-

Based Brain–computer interface and Lower-Limb

Prosthesis control: A case study. Frontiers in neu-

rology, 8:696.

Neuper, C., Scherer, R., Reiner, M., and Pfurtscheller, G.

(2005). Imagery of motor actions: Differential effects

of kinesthetic and visual–motor mode of imagery in

single-trial eeg. Cognitive brain research, 25(3):668–

677.

Nijboer, F., Morin, F. O., Carmien, S. P., Koene, R. A.,

Leon, E., and Hoffmann, U. (2009). Affective brain-

computer interfaces: Psychophysiological markers of

emotion in healthy persons and in persons with amy-

otrophic lateral sclerosis. In 2009 3rd International

Conference on Affective Computing and Intelligent In-

teraction and Workshops, pages 1–11. IEEE.

Norman, S. L., McFarland, D. J., Miner, A., Cramer, S. C.,

Wolbrecht, E. T., Wolpaw, J. R., and Reinkensmeyer,

D. J. (2018). Controlling pre-movement sensorimo-

tor rhythm can improve ﬁnger extension after stroke.

Journal of neural engineering, 15(5):56026.

Ozt

urk, N. and Yilmaz, B. (2018). Discrimination of Rest,

Motor Imagery and Movement for Brain-Computer

Interface Applications. In 2018 Medical Technologies

National Congress (TIPTEKNO), pages 1–4. IEEE.

Penaloza, C. I., Alimardani, M., and Nishio, S. (2018). An-

droid feedback-based training modulates sensorimo-

tor rhythms during motor imagery. IEEE Transac-

tions on Neural Systems and Rehabilitation Engineer-

ing, 26(3):666–674.

Ranky, G. N. and Adamovich, S. (2010). Analysis of a com-

mercial EEG device for the control of a robot arm.

In Proceedings of the 2010 IEEE 36th Annual North-

east Bioengineering Conference (NEBEC), pages 1–2.

IEEE.

Tacchino, G., Gandolla, M., Coelli, S., Barbieri, R., Pedroc-

chi, A., and Bianchi, A. M. (2017). EEG Analysis

during active and assisted repetitive movements: ev-

idence for differences in neural engagement. IEEE

Transactions on Neural Systems and Rehabilitation

Engineering, 25(6):761–771.

Ubeyli, E. D. (2009). Combined neural network model em-

ploying wavelet coefﬁcients for eeg signals classiﬁca-

tion. Digital Signal Processing, 19(2):297–308.

Victorino, J., Noirhomme, Q., Lul

e, D., Kleih, S. C.,

Chatelle, C., Halder, S., Demertzi, A., Bruno, M.-

A., Gosseries, O., Vanhaudenhuyse, A., and Others

(2015). Improving EEG-BCI analysis for low cer-

tainty subjects by using dictionary learning. In Signal

Processing, Images and Computer Vision (STSIVA),

2015 20th Symposium on, pages 1–7. IEEE.

Yi, W., Qiu, S., Wang, K., Qi, H., He, F., Zhou, P., Zhang,

L., and Ming, D. (2016). EEG oscillatory patterns

and classiﬁcation of sequential compound limb motor

imagery. Journal of neuroengineering and rehabilita-

tion, 13(1):11.

Yu, T., Xiao, J., Wang, F., Zhang, R., Gu, Z., Cichocki, A.,

and Li, Y. (2015). Enhanced motor imagery training

using a hybrid BCI with feedback. IEEE Transactions

on Biomedical Engineering, 62(7):1706–1717.

A MEG Study of Different Motor Imagery Modes in Untrained Subjects for BCI Applications

195