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Neuroscience Letters
journal homepage: www.elsevier.com/locate/neulet
Research article
EEG-based BCI system for decoding finger movements within
the same hand
Rami Alazraia,⁎, Hisham Alwannib, Mohammad I. Daouda
a Department of Computer Engineering, School of Electrical
Engineering and Information Technology, German Jordanian
University, Amman 11180, Jordan
b Faculty of Engineering, University of Freiburg, Freiburg
79098, Germany
A R T I C L E I N F O
Keywords:
Electroencephalography (EEG)
Brain–computer interfaces (BCIs)
Time-frequency distribution
Finger movements
Support vector machines
A B S T R A C T
Decoding the movements of different fingers within the same
hand can increase the control's dimensions of the

electroencephalography (EEG)-based brain–computer interface
(BCI) systems. This in turn enables the subjects
who are using assistive devices to better perform various
dexterous tasks. However, decoding the movements
performed by different fingers within the same hand by
analyzing the EEG signals is considered a challenging
task. In this paper, we present a new EEG-based BCI system for
decoding the movements of each finger within
the same hand based on analyzing the EEG signals using a
quadratic time-frequency distribution (QTFD), namely
the Choi–William distribution (CWD). In particular, the CWD is
employed to characterize the time-varying
spectral components of the EEG signals and extract features that
can capture movement-related information
encapsulated within the EEG signals. The extracted CWD-based
features are used to build a two-layer classifi-
cation framework that decodes finger movements within the
same hand. The performance of the proposed
system is evaluated by recording the EEG signals for eighteen
healthy subjects while performing twelve finger
movements using their right hands. The results demonstrate the
efficacy of the proposed system to decode finger
movements within the same hand of each subject.
1. Introduction
A brain–computer interface (BCI) is a system that decodes brain
activities to provide users with alternative ways to control
various
computer-based applications and assistive devices. Among the
various
neuroimaging modalities, the EEG is considered the most
commonly
used neuroimaging modality for designing BCI systems [1].
Over the past decade, researchers have developed EEG-based

BCI
systems to decode the actual and imagery motor tasks of large
body-
parts [2,3], including the hands, feet, and tongue, in an attempt
to
control various assistive devices, such as wheelchairs [4],
computer-
based applications [5], and prosthetic devices [6]. Nonetheless,
the fact
that the vast majority of the existing EEG-based BCI systems
can ana-
lyze brain activities and produce a limited number of control
signals,
usually less than five control signals, reduces the capability of
using
these systems to control more complicated assistive devices,
such as
prosthetic and robotic hands, that require a large number of
control
signals to perform various dexterous tasks [7].
Recently, few researchers have started to investigate the
possibility
of decoding the movements performed by fine body-parts, such
as the
movements of each finger within the same hand [7,8], wrist
movements
of the same hand [9,10], and grasp-related movements
performed by
the same hand [11,12], in order to increase the control's
dimensions of
the EEG-based BCI systems. In fact, decoding the movements
of each
finger within the same hand based on analyzing the EEG signals
is more

difficult than decoding the movements performed by different
large
body-parts, decoding the movements performed by a specific
finger in
the left hand from the movements of the matching finger in the
right
hand, or decoding the movements of the fingers from the
movements of
the wrist within the same hand [7,9,12]. This is due to the fact
that
finger movements within the same hand activate relatively small
and
close regions in the sensorimotor cortex area within the same
hemi-
sphere of the brain [7,9,13]. Therefore, the task of using a
neuroima-
ging modality that has a relatively low spatial resolution, such
as the
EEG, to decode the movements of each finger within the same
hand is
considered challenging due to the fact that various brain regions
are
activated during the movements of individual fingers [7]. In
addition,
the nonstationary nature of the EEG signals implies that the
spectral
components of the EEG signals vary as a function of time.
Therefore,
analyzing the EEG signals in the time-domain or the frequency-
domain
might not capture the spectral characteristics of the EEG
signals. In fact,
the nonstationary nature of the EEG signals imposes the
requirement of
representing the EEG signals in a joint time-frequency domain
that can

describe the spectral variations of the signals over time [14].
https://doi.org/10.1016/j.neulet.2018.12.045
Received 29 September 2018; Received in revised form 28
December 2018; Accepted 29 December 2018
⁎ Corresponding author.
E-mail address: [email protected] (R. Alazrai).
Neuroscience Letters 698 (2019) 113–120
Available online 08 January 2019
0304-3940/ © 2019 Elsevier B.V. All rights reserved.
T
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In this paper, we hypothesize that analyzing the EEG signals
using a
quadratic time-frequency distribution (QTFD), namely the
Choi–Williams distribution (CWD), can enable accurate
decoding of
fingers movements within the same hand. In particular, the
CWD is
employed to characterize the time-varying spectral components
of the
EEG signals and extract features that can capture movement-

related
information encapsulated within the EEG signals. The extracted
CWD-
based features are used to build a two-layer classification
framework
that can simultaneously identify each moving finger within the
same
hand and decode the movements performed by each identified
finger.
2. Materials and methods
2.1. Subjects
Eighteen healthy subjects (6 females and 12 males, with an
average ± standard deviation age of 21.2 ± 3.0 years)
volunteered to
participate in this study. EEG signals were recorded for each
subject
while performing twelve finger movements using her/his right
hand,
including four thumb-related movements, namely the thumb
adduction,
thumb abduction, thumb flexion, and thumb extension
movements, and
the flexion and extension movements of the index, middle, ring,
and
little fingers. Before participating in the experiment, each
subject re-
ceived a thorough explanation of the experimental procedure
and
signed a consent form. The experimental procedure of this study
was
approved by the Research Ethics Committee at the German
Jordanian
University and was conducted in accordance with the

Declaration of
Helsinki.
2.2. Experimental protocol
At the beginning of the experiment, each subject was asked to
sit on
a chair and to relax her/his arms on a table located in front of
her/him.
A computer screen was placed on the table at a distance of
approxi-
mately 60 cm from the subject and employed to display various
visual
cues. In particular, each visual cue notifies the subject to
perform a full
flexion movement followed by a full extension movement using
a spe-
cific finger or a full adduction movement followed by a full
abduction
movement using the thumb.
For each trial, a visual cue was displayed for three seconds
followed
by a black screen that prompts the subject to start performing
the se-
quence of flexion and extension movements using a specific
finger or
the adduction and abduction movements using the thumb.
During the
recording of each trial, the experimenter carefully follows the
move-
ments of the subject's fingers and hits the button of an event
marker to
mark transitions from flexion to extension and from adduction
to ab-
duction. The total number of trials recorded for each finger

movement
is five trials per each subject. The average ± standard deviation
durations of the flexion and extension movements computed
over the
five fingers and all subjects are 4.1 ± 0.4s and 3.6 ± 0.1 s,
respec-
tively, while the average ± standard deviation durations of the
thumb
adduction and thumb abduction movements computed over all
subjects
are 4.7 ± 0.15 and 3.9 ± 0.14 s, respectively.
2.3. Data acquisition and preprocessing
The BioSemi ActiveTwo EEG system
(https://www.biosemi.com)
was used to record the EEG signals using 11 Ag/AgCl
electrodes at a
sampling rate of 2048 Hz. The utilized EEG electrodes are
arranged on
the scalp according to the 10–20 international electrode
placement
system at the following locations: F3, F4, Fz, C3, C4, Cz, P3,
P4, Pz, T7, and
T8, which are referenced to the common mode sense (CMS)/
driven
right leg (DRL) at the C1 and C2 locations. The recorded EEG
signals
were downsampled to 256 Hz and filtered by applying a
bandpass filter
with a bandwidth of 0.5–35 Hz. Moreover, the automatic artifact
re-
moval (AAR) toolbox [15] was employed to reduce the muscle
and
electrooculography (EOG) artifacts in the filtered EEG signals.

2.4. Time-frequency representation and feature extraction
In this study, we propose to analyze the EEG signals using a
quad-
ratic time-frequency distribution (QTFD), namely the Choi–
Williams
Distribution (CWD) [16]. The CWD can be viewed as a two-
dimensional
(2D) transformation that maps the original time-domain EEG
signals
into a joint time-frequency domain which has an excellent
resolution in
both the time and frequency domains [14,16]. Hence, the use of
the
CWD to analyze the EEG signals enables the construction of a
time-
frequency representation (TFR) of the EEG signals that can
quantify the
distribution of the energy encapsulated in the EEG signals over
the time
and frequency domains [14]. Specifically, to compute the CWD,
we
employed a sliding window that divides the EEG signal of each
elec-
trode into a set of overlapped segments, such that the size of
each
segment is 256 samples and the overlap between any two
consecutive
segments is 128 samples. The size and overlap of the sliding
window
were selected experimentally as described in Section 3.3. Then,
the
CWD is computed for an EEG segment, denoted as s(t), as
follows [14]:

1. Compute the analytic signal of s(t), denoted as x(t), as
follows:
�= +x t s t j s t( ) ( ) { ( ) }, (1)
where � {·} is the Hilbert transform [17].
2. Compute the CWD of x(t), denoted as ρx(t, f), as follows
[16,18]:
∫ ∫= ∂ ∂
−∞
∞
−∞
∞
− +ρ t f χ μ ν κ μ ν e( , ) ( , ) ( , ) ,x x
j π fν tμ
ν μ
2 ( )
(2)
where χx(μ, ν) is the ambiguity function of x(t), and κ(μ, ν) is a
time-
frequency smoothing kernel. In particular, χs(μ, ν) represents
the
Fourier transform of the auto-correlation function of x(t). χs(μ,
ν)
can be computed as follows [16,18]:
∫= + − ∂
−∞

∞
χ μ ν x t
ν
x t
ν
e t( , ) (
2
) * (
2
) ,x
j πμt2
(3)
where x*(·) is the complex conjugate of x(·). The time-
frequency
smoothing kernel, κ(μ, ν), can be expressed as follows [16]:
⎜ ⎟= ⎛
⎝
− ⎞
⎠
κ μ ν
μ ν
α
( , ) exp ,
2 2
2 (4)

where α > 0 is a smoothing parameter that was experimentally
selected to be 0.5.
The dimensionality of the CWD-based TFR computed for each
EEG
segment is equal to H × L, where H = 256 represents the number
of
time samples within an EEG segment and L = 512 represents the
number of frequency samples. Such a high dimensionality can
increase
the complexity of the classification task. In order to reduce the
di-
mensionality of the obtained CWD-based TFR, we propose to
extend
two frequency-domain features, namely the normalized Renyi
entropy
and the energy concentration features, to the joint time-
frequency do-
main [11,19,14]. These two extended features aim to quantify
the
constructed CWD-based TFR of each EEG segment. In
particular, the
normalized Renyi entropy, F1, of the CWD quantifies the
regularity of
the distribution of the energy encapsulated within a specific
EEG seg-
ment. The F1 feature can be computed as follows [11,19,14]:
∑ ∑= −
⎛
⎝
⎜
⎜

⎛
⎝
⎜ ∑ ∑
⎞
⎠
⎟
⎞
⎠
⎟
⎟= = = =
F
ρ t f
ρ t f
(0.5) log
( , )
( , )
.
t
H
f
L
x
t

H
f
L
x
1 2
1 1 1 1
2
(5)
On the other hand, the energy concentration, F2, of the CWD
pro-
vides a measure that describes the spread of the energy
encapsulated
within a specific EEG segment. The F2 feature can be obtained
as fol-
lows [11,19,14]:
R. Alazrai et al. Neuroscience Letters 698 (2019) 113–120
114
https://www.biosemi.com
∑ ∑= ⎛
⎝
⎜
⎞
⎠

⎟
= =
F ρ t f| ( , ) | .
t
H
f
L
x2
1 1
1/2
2
(6)
Fig. 1 provides a graphical illustration of the feature extraction
process. In particular, at each position of the sliding window,
the fea-
tures F1 and F2 are computed from the constructed CWD-based
TFR of
each EEG segment. The total number of EEG segments at each
window
position is equal to 11 segments, where each segment represents
the
EEG signal of a particular EEG electrode within the current
window
position. The extracted features from the EEG segments at a
particular
window position are grouped to form a feature vector.

Therefore, the
total number of features comprised within each feature vector is
equal
to 22 features.
2.5. Classification framework
In this work, we propose a two-layer classification framework
(2LCF) to simultaneously identify each moving finger within
the same
hand and decode the movements performed by each identified
finger.
The proposed 2LCF converts the original complex classification
task
(i.e., classifying a feature vector into one of the twelve different
finger
movements described in Section 2.1) into a sequence of two
simpler
classification tasks that are performed at each layer. In
particular, the
first classification layer comprises one classifier, denoted as
C1,1, that
analyzes each input feature vector to identify the moving finger
within
the same hand, without specifying the movement performed by
the
identified moving finger. Explicitly, the C1,1 classifier assigns
each input
feature vector to one of the following five different movement
classes:
the thumb movement (M1), index movement (M2), middle
movement
(M3), ring movement (M4), and little movement (M5). In this
study, we
refer to the movements M1, M2, M3, M4, and M5 as movements

of dif-
ferent fingers within the same hand. The C1,1 classifier is
implemented
using a multi-class support vector machine (SVM) classifier
with radial
basis function (RBF) kernel [20]. After that, the input feature
vector is
passed to the second classification layer, which consists of five
different
SVM classifiers with RBF kernels. Each classifier in the second
classi-
fication layer is associated with a particular finger and is
designed to
decode movements performed by that particular finger.
Specifically, the
first classifier at the second classification layer, denoted as
C2,1, is a
multi-class SVM classifier that classifies an input feature vector
that is
identified at the first layer as M1 class into one of four thumb-
related
movements, namely the thumb adduction (M1,1), thumb
abduction
(M1,2), thumb flexion (M1,3), and thumb extension (M1,4)
movements.
The second classifier, denoted as C2,2, is a binary SVM
classifier that
classifies an input feature vector that is identified at the first
layer as M2
class into one of two index-related movements, namely the
index
flexion (M2,1) and index extension (M2,2) movements. The
third clas-
sifier, denoted as C2,3, is a binary SVM classifier that classifies
an input
feature vector that is identified at the first layer as M3 class

into one of
two middle-related movements, namely the middle flexion
(M3,1) and
middle extension (M3,2) movements. The fourth classifier,
denoted as
C2,4, is a binary SVM classifier that classifies an input feature
vector that
is identified at the first layer as M4 class into one of two ring-
related
movements, namely the ring flexion (M4,1) and ring extension
(M4,2)
movements. Finally, the fifth classifier, denoted as C2,5, is a
binary SVM
classifier that classifies an input feature vector that is identified
at the
first layer as M5 class into one of two little-related movements,
namely
the little flexion (M5,1) and little extension (M5,2) movements.
In this
study, we refer to the movements M1,1, M1,2, M1,3, M1,4,
M2,1, M2,2,
M3,1, M3,2, M4,1, M4,2, M5,1, and M5,2 as movements of the
same finger.
Fig. 1. Graphical illustration of the feature extraction procedure
employed at each window position.
115
Fig. 2 provides a structure diagram of the proposed 2LCF.
2.6. Performance evaluation procedures and metrics

For each subject, we construct the 2LCF by utilizing a ten-fold
cross-
validation procedure to train and test the SVM classifiers within
the first
and second layers of the proposed 2LCF [11,19]. The ten-fold
cross-
validation procedure is repeated for ten times and the average
classi-
fication performance for each subject is computed over the ten
repeti-
tions. The implementation of the multi-class SVM classifiers in
our
proposed 2LCF, namely the C1,1 and C2,1 classifiers, is carried
out using
the one-against-one scheme [20]. In addition, the regularization
para-
meter, C, and the RBF kernel parameter, γ, of each SVM
classifier are
tuned by performing a grid-search to find the values of C and γ
that
minimize the classification error [21].
To quantify the classification performance of each classifier in
the
constructed 2LCF, we employed two standard evaluation
metrics,
namely the classification accuracy (CA) and the F1-score, that
are
computed as follows [22]:
=
+
+ + +
×CA

(tp tn)
(tp tn fp fn)
100%,
(7)
− = ×
+
×F
P R
P R
score 2
( * )
( )
100%,1
(8)
where tp, tn, fp, and fn represent the number of true positive,
true ne-
gative, false positive, and false negative cases, respectively. In
addition,
P = tp/(tp + fp) and R = tp/(tp + fn) represent the precision and
recall,
respectively. In fact, the F1-score provides a weighted average
of the
precision and recall that takes into consideration the false
positive and
false negative rates.
3. Experimental results

3.1. Results of the first classification layer
In this section, we present the classification results of the first
classification layer, namely the C1,1 classifier. Table 1 shows
the F1-
scores obtained for the M1, M2, M3, M4, and M5 classes
computed for
each one of the eighteen subjects (S1 to S18). The average F1-
score va-
lues obtained for the M1, M2, M3, M4, and M5 classes are
86.7%, 87.1%,
84.1%, 79.0%, and 86.5%, respectively.
Fig. 3 shows the CA values and the corresponding standard
devia-
tions obtained by the first classification layer for each subject.
The re-
sults presented in Fig. 3 show that the mean ± standard
deviation CA
value of the first classification layer, which discriminates
between the
M1, M2, M3, M4, and M5 movement classes, computed over all
eighteen
subjects is equal to 85.85 ± 1.1 % Moreover, the mean CA
values
computed for the eighteen subjects are between 74.1%, which
was
obtained for subject 10 (S10), and 93.1%, which was obtained
for
subject 16 (S16).
In addition, we compare the significance of the CA values of
the C1,1
classifier that are computed for each subject with the random
classifi-

cation rate (RCR), which is defined as the reciprocal of the
number of
classes and has a value of 20%, by performing t-tests with a
significance
level of 0.01. The p values computed for all eighteen subjects
were less
than 0.01, which indicates that the CA obtained for the C1,1
classifier
associated with each subject is significantly higher than the
RCR (the
red dashed line in Fig. 3).
3.2. Results of the second classification layer
In this section, we present the classification results of each
classifier
in the second classification layer, namely the C2,1, C2,2, C2,3,
C2,4, and
C2,5 classifiers, computed for each of the eighteen subjects.
Table 2
presents the F1-scores computed for the C2,1, C2,2, C2,3, C2,4,
and C2,5
classifiers per each subject along with the mean F1-scores
computed for
each of the five classifiers over the eighteen subjects. In
particular, for
the C2,1 classifier, the average F1-score values obtained for
decoding the
four thumb movements, namely the M1,1, M1,2, M1,3, and
M1,4 move-
ments, are 67.3%, 53.5%, 67.4%, and 56.1%, respectively. For
the C2,2
classifier, the average F1-score values obtained for decoding the
flexion
and extension movements of the index finger, namely the M2,1
and M2,2

movements, are 72.1% and 67.5%, respectively. Moreover, for
the C2,3
flexion
Fig. 2. Structure diagram of the proposed 2LCF.
116
and extension movements of the middle finger, namely the M3,1
and
M3,2 movements, are 76.7% and 67.2%, respectively. For the
C2,4
flexion
and extension movements of the ring finger, namely the M4,1
and M4,2
movements, are 74.9% and 59.5%, respectively. Finally, for the
C2,5
flexion
and extension movements of the little finger, namely the M5,1
and M5,2
movements, are 70.7% and 63.0%, respectively.
Fig. 4 presents the CAs and corresponding standard deviations
ob-
tained for the C2,1, C2,2, C2,3, C2,4, and C2,5 classifiers
computed per
each subject. Furthermore, Fig. 4 provides the average CA
values
computed for each of the five classifiers over the eighteen

subjects. In
particular, the results presented in Fig. 4 indicate that the C2,1
classifier
was able to classify the M1,1, M1,2, M1,3, and M1,4
movements with an
average ± standard deviation CA of 64.6 ± 3.6 %. Moreover, the
C2,2
classifier was able to classify the M2,1 and M2,2 movements
with an
average ± standard deviation CA of 70.4 ± 5.5 %. For the C2,3
clas-
sifier, the average ± standard deviation CA value obtained in
dis-
criminating between the M3,1 and M3,2 movements was 73.4 ±
5.5 %.
In addition, the C2,4 classifier was able to classify the M4,1
and M4,2
movements with an average ± standard deviation CA of 70.4 ±
5.3 %.
Finally, for the C2,5 classifier, the average ± standard deviation
CA
value achieved in discriminating between the M5,1 and M5,2
movements
was 70.2 ± 3.9 %.
In addition, for each subject, we compare the significance of the
CA
values computed for each classifier in the second classification
layer,
namely the C2,1, C2,2, C2,3, C2,4, and C2,5 classifiers, with
the RCR value
associated with each of these classifiers by performing t-tests
with a
significance level of 0.01. In particular, the RCR of the C2,1,
which is
shown as a blue dashed line in Fig. 3, is equal to 25%, while the

RCR
associated with each of the other four classifiers in the second
classi-
fication layer, which is shown as a black dashed line in Fig. 3,
is equal
to 50%. For each classifier in the second classification layer,
the p va-
lues computed for all subject were less than 0.01, which
indicates that
the CA values computed for each subject per each classifier are
sig-
nificantly higher than the RCRs.
3.3. Analysis the effect of the sliding window size
Table 3 provides the average CA and F1-score values computed
for
the classifiers in our proposed 2LCF using different sizes and
overlaps of
the sliding window. In particular, the average CA and F1-score
values
presented in Table 3 are computed across all subjects using the
cross-
validation procedure described in Section 2.6. These results
indicate
that the best average CA and F1-score values were obtained
when the
size of the sliding window and overlap are 256 and 128,
respectively.
3.4. Comparison with the traditional multi-class SVM classier
(TMCC)
In this section, we compare the classification performance of
our

proposed 2LCF with the TMCC. In particular, the TMCC
consists of one
multi-class SVM classifier with RBF kernel that classifies
feature vectors
into one of the twelve finger movements described in subsection
2.1.
The performance of the TMCC was evaluated using the ten-fold
cross-
validation procedure described in Section 2.6. Table 4 presents
the
average CA and F1-score values computed over the ten
repetitions of the
cross-validation procedure across the twelve finger movements
per each
subject. The average CA and F1-score values computed for the
TMCC
over all subjects are 40.5% and 39.1%, respectively. On the
contrary,
the average CA and F1-score values computed for the second
classifi-
cation layer of our proposed 2LCF, which are presented in Fig.
4, are
69.8% and 67.4%, respectively. These results indicate that the
classi-
fication performance of our proposed 2LCF outperforms
significantly
the classification performance of the TMCC.
Table 1
The F1-scores (%) of the C1,1 classifier computed for each
subject.
Subject S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15
S16 S17 S18 Average
Movement M1 83.7 76.5 92.1 91.2 84.3 90.2 88.6 87.6 92.0 81.5

86.5 83.0 78.0 90.7 91.4 90.5 86.4 86.5 86.7
M2 89.8 77.8 83.5 89.6 78.9 90.5 94.6 86.8 89.2 78.4 82.3 76.4
89.3 94.3 87.7 94.9 97.5 87.5 87.2
M3 81.2 85.3 87.8 83.5 77.8 69.8 91.2 88.9 88.2 72.6 80.3 81.9
79.6 92.9 77.5 93.5 91.1 91.2 84.1
M4 58.9 77.3 89.1 85.2 65.5 74.3 79.9 94.2 96.5 71.3 90.5 67.9
80.9 74.4 86.6 72.2 91.8 65.5 79.0
M5 84.7 80.1 88.0 91.6 91.2 79.7 91.9 89.3 88.9 68.1 90.4 84.2
83.5 94.6 85.3 96.7 85.7 83.4 86.5
Fig. 3. The CA values of the C1,1 classifier computed for each
subject. The red vertical lines represent the standard deviations
in the CA values and the red dashed line
represents the RCR. (For interpretation of the references to
color in text/this figure legend, the reader is referred to the web
version of the article.)
117
4. Discussion
The main focus of the current study is to investigate the
capability of
using the CWD-based features along with the proposed 2LCF to
analyze
the EEG signals and decode finger movements within the same
hand.
The results obtained for the first and second classification
layers de-
monstrate the capability of our proposed approach to
successfully

decode twelve finger movements within the same hand for
eighteen
able-bodied subjects.
4.1. Movements of different fingers within the same hand
The results obtained for the first classification layer, which are
provided in Table 1 and Fig. 3, indicate that the extracted
CWD-based
Table 2
The F1-scores (%) of the C2,1, C2,2, C2,3, C2,4, and C2,5
classifiers within the second classification layer computed for
each subject and across all subjects.
Subject C2,1 C2,2 C2,3 C2,4 C2,5
M1,1 M1,2 M1,3 M1,4 M2,1 M2,2 M3,1 M3,2 M4,1 M4,2 M5,1
M5,2
S1 70.9 41.4 62.6 54.4 58.7 71.8 71.4 66.2 81.2 83.9 63.6 68.7
S2 79.0 66.7 63.5 48.6 69.8 58.9 72.1 66.5 67.9 41.1 74.2 52.4
S3 47.8 57.9 66.4 36.2 74.7 54.0 85.9 63.9 70.3 60.5 81.5 56.7
S4 79.8 55.4 69.5 46.5 76.3 62.3 69.2 62.7 82.3 59.6 63.0 57.4
S5 72.2 67.9 64.8 69.0 62.6 62.6 88.1 69.6 72.0 55.6 69.0 54.0
S6 65.1 51.1 67.4 59.4 80.1 76.5 83.5 67.0 80.8 75.8 80.1 55.6
S7 70.5 72.3 54.9 63.8 79.5 70.8 67.4 54.4 67.6 51.5 65.6 69.6
S8 47.2 40.3 76.0 52.6 75.2 64.5 87.4 62.4 81.1 43.3 71.5 60.4
S9 63.5 46.2 66.3 69.0 53.9 66.0 56.6 70.6 50.9 56.1 73.9 82.8
S10 64.3 62.6 60.0 46.1 73.5 77.1 78.5 65.3 75.1 56.5 78.0 53.3
S11 74.8 34.8 78.1 35.5 80.3 64.4 85.3 76.7 82.3 76.9 80.7 76.7
S12 54.7 33.3 61.9 53.2 84.9 70.7 80.9 69.7 76.1 57.9 64.5 68.5
S13 64.2 65.0 64.8 56.2 62.5 65.0 51.7 64.8 67.5 54.4 75.4 58.1
S14 74.5 41.1 77.6 65.9 71.4 55.2 81.2 58.9 73.1 50.6 78.9 65.0
S15 85.6 62.2 76.7 55.8 73.9 59.0 78.3 62.4 67.9 39.0 68.7 52.6
S16 67.8 61.2 58.9 84.1 61.3 79.4 81.2 85.3 84.1 89.6 51.7 79.2

S17 74.7 45.7 76.5 53.0 85.9 76.9 84.7 75.6 85.7 60.6 76.9 53.0
S18 55.0 58.8 67.1 59.5 72.3 79.7 77.0 68.0 82.8 58.4 56.4 69.4
Average 67.3 53.6 67.4 56.0 72.0 67.5 76.7 67.2 74.9 59.5 70.8
63.0
Fig. 4. The CA values of the C2,1, C2,2, C2,3,
C2,4, and C2,5 classifiers computed for each
subject. The red vertical lines represent the
standard deviations in the CAs. The blue da-
shed line represents the RCR of the C2,2 clas-
sifier, while the black dashed line represents
the RCR of the C2,2, C2,3, C2,4, and C2,5 classi-
fiers. (For interpretation of the references to
color in text/this figure legend, the reader is
referred to the web version of the article.)
Table 3
The average CA (%) and F1-scores (%) computed for the
classifiers of our proposed 2LCF using different sizes of the
sliding window.
Sliding window size Overlap size First classification layer
Second classification layer
C1,1 C2,1 C2,2 C2,3 C2,4 C2,5
CA F1-score CA F1-score CA F1-score CA F1-score CA F1-
score CA F1-score
64 32 74.7 74.1 60.5 56.4 65.9 63.7 64.4 62.2 65.8 63.2 67.6
64.7
128 64 80.5 80.0 63.6 59.3 69.6 67.3 67.1 65.2 68.4 66.9 68.5
64.9
256 128 85.8 84.7 64.6 61.1 70.4 69.8 73.4 71.9 70.4 67.2 70.2
66.9

512 256 83.2 81.9 62.9 46.8 68.1 59.9 71.1 56.9 69.8 59.3 69.6
52.9
1024 512 70.3 66.4 49.0 26.2 63.7 45.7 59.9 44.4 73.1 43.3 67.2
37.6
118
features were able to obtain accurate identification of the
moving fin-
gers within the same hand. In particular, Table 1 indicates that
the
average F1-scores obtained for the identification of the moving
fingers
are above 79%, where the lowest F1-score of 79.0% was
computed for
the ring finger. Moreover, the results of the first classification
layer
suggest that the EEG signals encapsulate sufficient information
to de-
code the movements of fine body-parts, such as the finger
movements,
which complies with the findings reported in previous studies,
such as
[7,8].
4.2. Movements of the same finger within the same hand
The results obtained for the second classification layer, which
are
provided in Table 2 and Fig. 4, indicate that the discrimination
between
the movements performed by a specific finger is more

challenging than
decoding the movements performed by different fingers within
the
same hand. For example, the first classification layer was able
to
identify that the moving finger is the thumb finger with an
average F1-
score of 86.7%, while the second classification layer was able
to dis-
criminate between the four thumb-related movements with an
average
F1-score of 61.1%. Moreover, the results presented in Fig. 4
indicate
that increasing the number of movements performed by the same
finger
can drastically decrease the ability to differentiate between the
move-
ments performed by the same finger. In particular, Fig. 4 shows
that the
mean CA value of the C2,1 classifier, which discriminates
between four
thumb-related movements, is significantly lower than the CAs
obtained
for each of the other four classifiers in the second classification
layer
that discriminates between only two movements of each finger.
This
reduction in the classification performance can be attributed to
the
following factors: (1) Finger movements within the same hand
activate
relatively close regions in the sensorimotor cortex area within
the same
hemisphere of the brain [7,9,13]. (2) The limited spatial
resolution and
low signal-to-noise ratio (SNR) of the EEG modality reduces

the cap-
ability of capturing brain activities within the activated regions
of the
brain during finger movements [7]. (3) As a consequence of the
two
factors mentioned above, increasing the number of different
move-
ments that are performed by each finger can significantly
increase the
difficulty of dividing the feature space into separable decision
regions,
where each region comprises feature vectors that belong to a
particular
movement. In fact, several previous EEG-related studies have
indicated
that the CAs obtained for multi-class classification problems,
which
involve more than two classes, were significantly lower than the
CAs
obtained for binary classification problems that involve two
classes
[7,19,23].
4.3. Comparison with other approaches
Recently, few studies have investigated the possibility of
decoding
finger movements within the same hand based on EEG signals.
For
example, Liao et al. [7] recorded EEG signals using 128
electrodes for
eleven healthy subjects while performing flexion and extension
move-
ments using each of the fingers in their right hands. The
recorded EEG
signals were analyzed using principal component analysis and a

set of
power spectrum-based features. For each subject, the extracted
features
were used to build ten binary SVM classifiers, where each
classifier is
associated with a pair of fingers. The average CA computed for
all pairs
of fingers across all subjects was 77.1%. In another study,
Quandt et al.
[8] recorded the EEG signals using 32 electrodes for thirteen
healthy
subjects while pressing a button using the thumb, index, middle,
and
little fingers. For each subject, the recorded EEG signals were
utilized to
construct four binary SVM classifiers, where each classifier is
associated
with one of the four fingers. In particular, the first, second,
third, and
fourth classifiers aim to identify whether the moving finger is
the
thumb, index, middle, and little, respectively. The average CA
value
computed over the four fingers across all subjects was 43.5%.
The current study provides five improvements over the
approaches
presented in [7,8]: Firstly, the first classification layer of our
proposed
2LCF utilizes a multi-class SVM classifier to discriminate
between the
movement of all fingers within the same hand rather than using
binary
SVM classifiers to discriminate between the movements of each
pair of

fingers as in [7,8]. In this regard, Liao et al. [7] indicated that
dis-
criminating between the movements of individual fingers within
the
same hand using a multi-class classifier is more difficult than
dis-
criminating between the movements of each pair of fingers
within the
same hand. In fact, the use of multi-class classification
approaches to
decode finger movements within the same hand can facilitate
the de-
velopment of EEG-based BCI systems with higher control's
dimensions,
which can enhance the functionality of various dexterous
assistive de-
vices. Secondly, the current study considered both identifying
move-
ments of different fingers within the same hand and decoding
the
movements of the moving finger, while the approaches
presented in
[7,8] considered only the identification of the moving finger
without
decoding the movement performed by the moving finger. Hence,
our
proposed approach can increase the control's dimensions of the
devel-
oped EEG-based BCI systems. Thirdly, the results reported in
the cur-
rent study are based on utilizing eleven EEG electrodes
compared with
the results reported in the studies [7,8] which are based on
utilizing
128 and 32 electrodes, respectively. This demonstrates the
capability of

the extracted CWD-based features to capture movement-related
in-
formation that are encapsulated within the EEG signals to
achieve high
CA values. Fourthly, to the best of our knowledge, this is the
first study
that investigates the possibility of decoding twelve different
movements
performed by different fingers within the same hand. Such a
large
number of movements makes the classification task more
challenging
compared with the approaches [7,8] that considered decoding
only five
and four movements, respectively, of individual fingers within
the same
hand. Fifthly, the classification architectures employed in the
ap-
proaches [7,8] are based on constructing a set of binary SVM
classifiers,
where each classifier produces one decision. These decisions
were not
combined to produce one final decision that specifies the
moving finger
within the same hand. Such classification architecture can
highly suffer
from the false positive error, in which a feature vector can be
collec-
tively misclassified by multiple binary classifiers and assigned
to in-
correct fingers. In contrast, our proposed 2LCF produces one
decision at
the first classification layer that identifies the moving finger,
and one
decision at the second classification layer that specifies the
movement

performed by the identified moving finger.
5. Conclusions
In the present study, we have demonstrated the possibility of
de-
coding movements performed by each finger within the same
hand
Table 4
The average CA (%) and F1-score (%) values computed for each
subject using the TMCC.
Evaluation metric Subject Average across all subjects
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17
S18
CA 42.8 35.7 41.3 39.6 41.8 36.0 44.2 43.3 41.4 36.2 43.9 37.6
34.4 42.5 45.8 44.8 39.0 37.8 40.5
F1-score 44.3 36.0 39.2 39.6 41.8 36.4 44.4 43.4 37.4 35.3 36.6
39.2 35.5 40.2 44.4 38.0 35.3 37.0 39.1
119
using EEG signals. The results presented in this study suggest
the fea-
sibility of using the CWD to analyze the EEG signals and
extract
quantitative features that are capable of discriminating between
dif-
ferent finger movements. In the future, we plan to investigate

the fol-
lowing research directions: (1) Studying the problem of
decoding si-
multaneous movements performed by multiple fingers within
the same
hand to improve the control mechanisms of prosthetic hands. (2)
Extending our experiments by including subjects with an
extended age
range and balanced gender distribution. (3) Studying the
potential of
using a higher number of EEG channels that achieve high
resolution
coverage of the motor cortex region to enhance the accuracy of
clas-
sifying the movements performed by the same finger.
Acknowledgements
This work is supported by the Scientific Research Support Fund
of
Jordan (grant no. ENG/1/9/2015).
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120
http://refhub.elsevier.com/S0304-3940(18)30902-9/sbref0005

http://refhub.elsevier.com/S0304-3940(18)30902-
9/sbref0115EEG-based BCI system for decoding finger
movements within the same handIntroductionMaterials and
methodsSubjectsExperimental protocolData acquisition and
preprocessingTime-frequency representation and feature
extractionClassification frameworkPerformance evaluation
procedures and metricsExperimental resultsResults of the first
classification layerResults of the second classification
layerAnalysis the effect of the sliding window sizeComparison
with the traditional multi-class SVM classier
(TMCC)DiscussionMovements of different fingers within the
same handMovements of the same finger within the same

handComparison with other
approachesConclusionsAcknowledgementsReferences
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