Gaussian Process for a Single-channel EEG Decoder with Inconspicuous Stimuli and Eyeblinks

Abstract

A single-channel electroencephalography (EEG) device, despite being widely accepted due to convenience, ease of deployment and suitability for use in complex environments, typically poses a great challenge for reactive brain-computer interface (BCI) applications particularly when a continuous command from users is desired to run a motorized actuator with different speed profiles. In this study, a combination of an inconspicuous visual stimulus and voluntary eyeblinks along with a machine learning-based decoder is considered as a new reactive BCI paradigm to increase the degree of freedom and minimize mismatches between the intended dynamic command and transmitted control signal. The proposed decoder is constructed based on Gaussian Process model (GPM) which is a nonparametric Bayesian approach that has the advantages of being able to operate on small datasets and providing measurements of uncertainty on predictions. To evaluate the effectiveness of the proposed method, the GPM is compared against other competitive techniques which include k-Nearest Neighbors, linear discriminant analysis, support vector machine, ensemble learning and neural network. Results demonstrate that a significant improvement can be achieved via the GPM approach with average accuracy reaching over 96% and mean absolute error of no greater than 0.8 cm/s. In addition, the analysis reveals that while the performances of other existing methods deteriorate with a certain type of stimulus due to signal drifts resulting from the voluntary eyeblinks, the proposed GPM exhibits consistent performance across all stimuli considered, thereby manifesting its generalization capability and making it a more suitable option for dynamic commands with a single-channel EEG-controlled actuator.

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Computers, Materials & Continua
DOI: 10.32604/cmc.2022.025823
Article
Gaussian Process for a Single-channel EEG Decoder with Inconspicuous
Stimuli and Eyeblinks
Nur Syazreen Ahmad*, Jia Hui Teo and Patrick Goh
School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, 14300, Malaysia
*Corresponding Author: Nur Syazreen Ahmad. Email: syazreen@usm.my
Received: 06 December 2021; Accepted: 14 February 2022
Abstract: A single-channel electroencephalography (EEG) device, despite
being widely accepted due to convenience, ease of deployment and suitability
for use in complex environments, typically poses a great challenge for reactive
brain-computer interface (BCI) applications particularly when a continuous
command from users is desired to run a motorized actuator with different
speed profiles. In this study, a combination of an inconspicuous visual stimulus
and voluntary eyeblinks along with a machine learning-based decoder is
considered as a new reactive BCI paradigm to increase the degree of freedom
and minimize mismatches between the intended dynamic command and trans-
mitted control signal. The proposed decoder is constructed based on Gaussian
Process model (GPM) which is a nonparametric Bayesian approach that has
the advantages of being able to operate on small datasets and providing mea-
surements of uncertainty on predictions. To evaluate the effectiveness of the
proposed method, the GPM is compared against other competitive techniques
which include k-Nearest Neighbors, linear discriminant analysis, support
vector machine, ensemble learning and neural network. Results demonstrate
that a significant improvement can be achieved via the GPM approach with
average accuracy reaching over 96% and mean absolute error of no greater
than 0.8 cm/s. In addition, the analysis reveals that while the performances of
other existing methods deteriorate with a certain type of stimulus due to signal
drifts resulting from the voluntary eyeblinks, the proposed GPM exhibits
consistent performance across all stimuli considered, thereby manifesting its
generalization capability and making it a more suitable option for dynamic
commands with a single-channel EEG-controlled actuator.
Keywords: Brain-computer interface; dynamic command; electroence
phalography; gaussian process model; visual stimulus; voluntary eyeblinks
1Introduction
Electroencephalography (EEG) test is the standard approach for measuring oscillations caused
by brain activity in most brain-computer interface (BCI) technologies. The measurement was tradi-
tionally recorded using multiple wet electrodes (usually more than 32) attached to the scalp with high

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sensitivity electronics in an attempt to boost the signal-to-noise ratio [1]. Participants involved in the
data collection using such a device are typically constrained to laboratory settings and require extensive
training in order to produce clean and reliable EEG data [2]. Nonetheless, the past decade has seen
the rapid development of wearable EEG-based BCIs such as Neurosky MindWave, Emotive EPOC
(+) and Mindo series which offer competitive performance with dry sensor technology and a smaller
number of electrodes that overcomes many of the aforesaid barriers. Apart from ease of deployment
and suitability for use in complex environments, they are also available at considerably lower prices
compared to the laboratory-restricted EEG devices, thus accelerating their adoption by the general
public [3–6].
EEG signals measured on the scalp from the BCI device will generate the so-called Event-Related
Potentials (ERPs) which refer to the small potential or voltage changes in the signals immediately after
the user’s attention is invoked by a stimulus. Human inhibitory control using ERP is relatively easier to
carry out as it only requires the user’s attention in a short duration for transmission of a command to
an external device. Examples include switching on/off the lights and stopping an ongoing motor action
[7]. However, in reactive BCI applications which require users to consciously generate brain signals for
continuous command transmission to an external device such as motorized actuators, the approach
via visual evoked potentials (VEPs) which are natural responses when the user’s brain is invoked only
by a visual stimulus tends to be relatively more prevalent [8].
To improve the quality of EEG data recording and decoding in the aforementioned BCI
paradigms, most wearable BCI devices have been equipped with machine learning (ML) algorithms
that allow them to safely extract relevant features from the EEG signals and classify them into several
states of mind such as relaxation and attention [9]. Linear discriminant analysis (LDA) for instance has
been preferred in many EEG classifications due to reduced computational cost which can minimize
the transmission delay between the brain and the target system [10]. However, for complex nonlinear
EEG data, support vector machine (SVM) can provide more desirable results as it uses a kernel-
based transformation to project data into a higher dimensional space where the relationships between
variables become linear [11]. The k-Nearest Neighbors (kNN) which is a machine learning (ML)
algorithm that identifies a testing sample’s class according to the majority class of k-nearest training
samples has demonstrated comparable performance in a recent study on EEG-based cognitive tasks
[12]. Another popular EEG classification is ensemble learning (EL) which generates multiple ML
models and then combines them to attain improved performance [13,14]. A more robust EEG
classification can be obtained using a deep convolutional neural network (CNN) using a large
number of electrodes with temporal and spatial filters to eliminate redundant information. For BCI
applications with a single-channel EEG device, the sequential CNN approach is available but is often
employed for passive BCI applications without the purpose of voluntary control such as cognitive
monitoring and sleep stage scoring [15,16]. Thus, for reactive BCI applications, applying the CNN
methods can be computationally taxing.
To treat issues on ocular artifacts, independent component analysis (ICA) is often employed
which utilizes the blind source separation method to detect and reject the contaminated EEG signals
[17]. Nonetheless, similar to the many CNN approaches, ICA usually requires EEG data recorded
from many channels owing to its intrinsic characteristics, which makes it extremely challenging to
eliminate independent components accurately including artifacts when only a few EEG channels are
available [18]. Alternatives to the ICA include multiscale principal component analysis [19], signal
decomposition methods [20,21], and general filtering methods such as wavelet transform, adaptive and
Wiener filters but most of them are frequently adopted for offline analysis due to high computational
cost. To minimize delays in real-time BCI application with an external actuator, infinite-impulse

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response, Kalman and Boxcar filters have been proposed as they can offer better solutions with less
demanding computational requirements [22].
Despite promising results in classifying and denoising EEG signals, most of the proposed
techniques are either only suitable for passive BCI applications or only applicable to multi-channel
EEG devices for optimal performance. A single-channel wearable EEG device, despite being widely
accepted due to low cost, convenience, and ease of applications especially in controlling robotic devices
in unconstrained environments [9,23], the accuracy and reliability of the transmitted signals are still
inconclusive and remain under debate as reported in several recent studies [24,25]. Plus, the use of such
a device will pose a great challenge particularly when both eyeblink detection and clean continuous
EEG signals are required to control an external actuator in reactive BCI applications [26].
In this work, the focus is on improving the BCI decoding strategy with a single-channel wearable
EEG device for reactive BCI applications where a continuous command from the user is transmitted to
actuate and drive a motorized actuator. To increase the degree of freedom of the BCI system, voluntary
eyeblinks with prespecified durations are leveraged to change the state of the recorded EEG data, thus
generating dynamic commands that can modify the speed of the motor while running. The proposed
decoding strategy is constructed based on Gaussian Process model (GPM) approach which to-date
remains underexplored for such a BCI paradigm. Unlike other ML approaches, a notable advantage
of the GPMs lies on their ability to operate on small datasets and provide measurements of uncertainty
on predictions. The effectiveness of the proposed approach is demonstrated via a comparative study
against other competitive classifiers which have been previously evaluated with a single-channel EEG
device in recent works such as multilayer perceptron NN [22], EL [27], LDA [28], kNN, and SVM [29].
In the light of [22] which proposes an alternative to motor imagery BCI that typically entails flickering
stimuli and extensive training [30], inconspicuous stationary visual stimuli are introduced in the BCI
paradigm to elevate the user’s attentiveness while controlling the actuator. The use of such a paradigm
is also in line with a recent review in [31] that highlights the significance of selecting suitable stimuli
to induce the user’s attention. Results demonstrate that a significant improvement can be achieved via
the GPM approach with average accuracy reaching over 96% and mean absolute error of no greater
than 0.8 cm/s. In addition, the analysis reveals that while the performances of other existing methods
deteriorate with a certain type of stimulus due to signal drifts resulting from the voluntary eyeblinks,
the proposed GPM exhibits consistent performance across all stimuli considered, thereby manifesting
its generalization capability and making it a more suitable option for such applications. The findings
of this study will not only increase the degree-of-freedom (DoF) of a single-channel EEG-controlled
actuator, but will also redound to the benefit of new BCI users or BCI illiterates who are unable to
sufficiently modulate their neuronal signals when controlling an external device.
2Methodology
2.1 Data Acquisition
NeuroSky®MindWave Mobile 2 Headset has been chosen in this study as it has gained widespread
acceptance due to its capability of providing a steady EEG recording over a long length of time. The
device consists of a single dry EEG channel placed on Fp1 as depicted in Fig. 1 according to the 10–20
system, which is a worldwide known system that establishes the relationship between the underlying
region of the cerebral cortex and the location of the electrodes. Another dry electrode is placed at the
A1 position using an ear clip to act as the ground reference.

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Figure 1: The EEG channel’s position with respect to the user’s head is placed at Fp1. Another dry
electrode in the form of an ear clip is placed at A1 to serve as the ground reference
Another significant characteristic of this device is its portability and lightweight, which allows the
user to move around freely without restriction. The MindWave Mobile 2 is equipped with an eSense
attention meter, which produces values on a scale of 1 to 100. If the reading falls below 40, the subject
is predicted to be in a neutral state. The range (140, 60] implies slightly elevated attention while the
range above 60 implies a normal to high attentiveness level.
2.2 Visual Stimuli and Dynamic Command to Actuator
Inspired by the work in [22] which adopts a brain training game-based stimuli to keep the
attentiveness high when transmitting signals via a BCI device, this work extends the capability of such a
paradigm by introducing voluntary eyeblinks to allow for multiple command changes to the actuator.
The proposed paradigm is depicted in Fig. 2 where the subject needs to transmit a continuous dynamic
speed command to the actuator (right subplot) while his/her attention is being elevated by the stimulus
(left subplot). In the light of [22], two stimuli are employed as shown in Fig. 3 where the first one
involves multiple hidden targets which requires the subject to spot differences between two adjacent
figures; while the second one involves one hidden target that needs to be localized in a cluttered scene.
For performance evaluation purposes, the speed command was designed with a mixture of a step
function to indicate a constant velocity and an increasing ramp function to represent acceleration
with prespecified durations as follows:
νd(t)=⎧
⎨
⎩
20 if t1<t≤t3
20t/(t5−t3)if t3<t≤t5
0otherwise
(1)
For consistency during the data acquisition, voluntary blinking will only take place at t=t2and
t=t4which serve as signals for state and speed changes (further details on this strategy are presented
in Section 2.3.3) that will take effect at t=t3and t=t5.
The left subplot of Fig. 2 depicts three major phases in the proposed paradigm; i.e., initial resting
state (IRS), attentive state (AS), and final resting state (FRS). During IRS, the subjects are requested
to rest and clear their minds before the experiment begins and a timer is displayed on the PC screen as
a guide. When the timer hits 10 s (i.e., at t=t1), they must instantly focus on the stimulus to actuate
the motor. At t=t2, they are required to blink twice at a rate of approximately 1 blink/second to
accelerate the motor, and then continue focusing on the stimulus until t=t4where they have to blink
thrice with a similar rate to stop the motor. The FRS phase begins when t=t5during which they need
to clear their mind to ensure the EEG signal is brought back to the normal state.

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Figure 2: The visual stimulus (left) is used to enhance the subject’s capability in controlling the EEG
signal to follow the targeted speed command (right) which is a mixture of ramp and step functions.
IRS, AS and FRS refer to initial, attentive and final resting stages respectively. The middle subfigure
depicts the timeline of the desired state transitions along with voluntary eyeblinks at t=t2(for b1)and
t=t4(for b2)
Figure 3: Two types of visual stimuli employed in this study [22]; Stimulus 1 (left) involves multiple
hidden targets, i.e., the subject needs to spot the differences between the two figures; Stimulus 2 (right)
involves one hidden target in a cluttered scene, i.e., the subject needs to find a character named Wally
hidden in the crowd
While the proposed paradigm is realistically attainable, it can be a significant challenge to
distinguish the elevated attention from the normal range during the voluntary blinking events due
to drifts and prominent deflections from the recorded EEG data. Such a scenario is illustrated by four
recorded trials in Fig. 4 where the blinking starts at t=33 s after the stimulus is displayed at t=20 s.
From the figure, a sudden drop in the EEG data, denoted as ν, and a duration of 2 to 5 s to drive the
meter reading back to the attention range is clearly seen within the blue strip. Thus, although the event
is instrumental for state or command changes, it can cause undesired delays and increase the chance
of misclassification, thereby lowering the BCI’s predictive capabilities.
To this purpose, this work proposes a robust decoding strategy that is based on GPM which is
a nonparametric Bayesian approach that has the advantages of being able to provide measurements
of prediction uncertainties, and a voluntary eyeblink detection that can also be embedded into the
motor’s control system as illustrated in Fig. 5. To ensure resilience against disturbances within the
motor system, the system is assumed to feature a pre-stabilized speed control loop that anticipates
a reference speed command rather than a pulse width modulation [32]. Hence, rather than visually
assessing the movement of the motor system (e.g., wheeled chair, robotic arm, or mobile robot), which
may be influenced by friction with the ground or disturbances within the hardware itself, we focus on

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the precision of the command received by the system’s embedded controller, which also serves as the
motion controller in this work. The main decoding strategy is further detailed in the following section.
Figure 4: Illustration on signal deflections during voluntary blinking when the subject’s attention level
is within the elevated range (i.e., ν>40)
Figure 5: Illustration of the overall flow of the proposed paradigm. The embedded system which
consists of the decoder and a motorized actuator is simulated in the PC via MATLAB software.
Bluetooth was used for the wireless data transmission from the BCI headset
2.3 Decoding Strategy
Unlike neural network-based predictions which assume that the data distribution can be modeled
in terms of a set of a finite number of parameters, GPM works based on nonparametric Bayesian
statistics which predicts the target function value in the form of posterior distribution that can be
computed by combining the noise (likelihood) model and a prior distribution on the target function.
The trained GPM can be embedded into the motor’s motion control system in practice using GPML
[33], PyGPs [34], GPflow [35]orGPyTorch[36]. Applying the GPM alone, however, may not be
adequate if one is to change the speed of the motor when it is running. To treat this issue, voluntary
eyeblink detection is introduced since the EEG electrode that is placed at Fp1 will result in prominent
signal deflections during the blinking events. In order to construct a stronger prediction model, a
Hanning-based filtering stage is also integrated into the system. The overview of the proposed structure
for the decoder is presented in Fig. 6 where the green areas illustrate the filtering stage and voluntary

CMC, 2022, vol.73, no.1 617
eyeblink detection while the blue area represents the GPM with dynamic speed command decoder.
Details of each stage are discussed in the subsequent subsections.
Figure 6: Overview of the proposed decoding strategy which consists of a GPM in cascade with a
Hanning filter, and a voluntary eyeblink detection via ev.Bothyand evare required to decode the
signal into the desired speed command, vd
2.3.1 Hanning Filter
Hanning filters which are a type of finite impulse response filters with Hanning window are
frequently employed with random data as they typically have a moderate impact on the frequency
resolution. In this work, as the computation speed is equally important to avoid delay in the wireless
communication between the subject and the external device, we propose the Hanning filter as shown
in the top left of Fig. 6 with the gain values of a0=0.25, a1=0.5, and a2=0.25 which result in a
second-order polynomial as follows:
X(z)=1
4[V(z)+2z−1V(z)+z−2V(z)](2)
or, equivalently in time-domain,
xk=0.25 (υk+2υk−1+υk−2)(3)
This filter will have a total gain of unity to preserve the amplitude of the targeted command, and
the output that will be fed to the GPM later will only be a scaled average of three sequential inputs,
with the center point weighted twice as heavily as its two adjacent neighbours. The performance of
this filter will also be compared against the recursive Boxcar filter which has shown superiority over
IIR and Kalman filters with a single-channel EEG device in [22].
2.3.2 Gaussian Process Model
Gaussian Process (GP) has an advantage over other ML algorithms in approximating a target
function, (denoted as f(x)) since it can express complicated input and output interactions without
predefining a set of bases and forecast a target output with uncertainty quantification. For regressions,
GP is used as a prior to describe the distribution on the target function. As GP is a stochastic process,
the function values f(xi),i=1, ...,nare treated as random variables. GP describes the distribution
over an unknown function by its mean function m(x)=E[f(x)]and a kernel function k(x,x)which
approximates the covariance E[(f(x)−m(x))(
f(x)−m(x))]. The covariance function denotes a
geometrical distance measure assuming that the more closely located inputs would be more correlated

618 CMC, 2022, vol.73, no.1
in terms of their function values. That is, the prior on the function values is represented as:
f(x)∼GP (0, k(x,x)) (4)
which are from a zero-mean GP with covariance function k(x,x). Similar to SVM, there are several
kernel functions that can be used as covariance functions for GP. A widely employed form is the
squared exponential (SE) function which can be described as follows:
kxi,xj|θ=σ2
fexp −1
2lxi−xjTxi−xj(5)
where θ=l,σfis the set of hyperparameters. Consider an unknown target function y=f(x),and
given a training data set with n samples as defined below:
D={(xi,yi)|i=1, ...,n}(6)
where xi∈Rpdenotes the input vector and y∈Rdenotes the corresponding (possibly perturbed)
output observations. The aim of GP is to predict the real-valued output fnew =f(xnew )for unseen target
input xnew. However, rather than a point estimate, the prediction is given as a probability distribution
quantifying uncertainty in the target value (a more detailed description can be found in [37]). Thus,
the prior on the function values can be represented as P(f|X)∼N(f|μ,K)where X=[x1,...,xn],
f=[f(x1),...,f(xn)],μ=[m(x1),...,m(xn)]and Kij =kxi,xj.Xrefers to the observed data points,
mis the mean function, and krepresents a positive definite kernel function as defined in (5). In practical
situations, we do not have access to the true function values, but their noisy versions which can be
written as y=f(x)+ε. Assuming there is an additive independent and identically distributed Gaussian
noise with variance σ2
nin the outputs so that cov (y)=K+σ2
nI, and by deriving the conditional
distribution, the predictive equations for GP model become P(f∗|X,y,X∗)∼N¯
f∗,σ∗where ¯
f∗=
E[f∗|X,y,X∗]=KT
∗K+σ2
nI−1yand σ∗=K∗∗ −KT
∗K+σ2
nI−1K∗. To predict the function from a
new test data, the hyperparameters can be optimized using the log marginal likelihood as follows [37]:
θ∗=arg max
θ
log P(y|X,θ).(7)
Thus, with the optimized hyperparameters, a more general predictive equation for GP model can
be written as
P(f∗|X,y,X∗,θ∗)∼N¯
f∗,σ∗. (8)
In order to predict the dynamic command from the new EEG signal in the test dataset, the mean
function of the posterior distribution will be used along with voluntary eyeblinks as described below.
2.3.3 Voluntary Eyeblinks
EEG data from the BCI device would typically have minor fluctuations at all states including
normal eyeblink events. In order to identify the voluntary blinks from other events, a preliminary test
with ten trials is conducted where the BCI user had to perform voluntary blinking once, twice, and
thrice with a rate of approximately 1 Hz when the attention level falls within the elevated range. During
the test, the value of evwhich refers to the first derivative of νas depicted at the bottom left of Fig. 6 is
computed at each time instant. The magnitudes of evwhen ev<0 from voluntary blinks and normal
blinks/fluctuations are recorded and visualized in Fig. 7.

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Figure 7: Comparisons of signal deflection magnitude, |ev|, for normal blinks/fluctuations and
voluntary blinks. The “1×”, “2×”and“3×” notations refer to once, twice and thrice blinks with a rate
of approximately 1Hz. The left plot shows the histogram while the right plot shows the corresponding
box plot
From the left plot of Fig. 7, it can be observed that the |ev|is nearly normally distributed within
the (0, 23)range during the normal blink or fluctuations. A similar trend is also seen for voluntary
blinks that are performed once (1×) where the distribution spans between 19 and 23. On the other
hand, the distribution of |ev|when voluntary blinks are performed twice (2×) and thrice (3×)are
left-skewed with the highest frequency at |ev|=23 and |ev|=29 respectively. Interestingly, if the 1×
voluntary blink is removed, the remaining distributions do not heavily overlap with each other as can
be seen in the corresponding box plots on the left side of Fig. 7. Thus, from this observation, a two
DoF can be designed with voluntary blinks to change the EEG state when it is elevated, i.e., Voluntary
Blink 2×which can be detected when |ev|∈[23, 28], and Voluntary Blink 3×which can be detected
when |ev|≥29. For brevity, Voluntary Eyeblinks 2×and 3×will be henceforth renamed as b1and b2
respectively.
2.3.4 Generation of Prediction Models and Performance Metrics
Twenty healthy subjects (ten from each gender) aged between 24 and 29 years participated in the
EEG experiments conducted in this study. The subjects had no brain training session or any prior BCI
experience before the actual experiment was carried out. During the experiments, the EEG data from
the BCI device was captured and recorded in MATLAB software. To obtain consistent and accurate
results, descriptions of the experimental protocols and the recommended method for fitting the headset
were provided and demonstrated to each participant before the paradigm was carried out.
In order to provide an unbiased evaluation of the prediction model, the data were partitioned into
training and test sets where only the performance of the latter would be evaluated. The flowchart of the
prediction model generations is illustrated in Fig. 8 (left) where Set MTR and Set FTR which consist of
80% of the data from the male and female subjects respectively are used as training data to construct
the prior distribution on the target function and the likelihood model. To further observe whether
the gender-based training can enhance the generalization capability of the model, the training is also
conducted based on genders as depicted in the first section of the flowchart. This process will generate

620 CMC, 2022, vol.73, no.1
three types of models, namely Model G (Cg) which is trained based on both male and female data;
Model M (Cm) which is trained based on male-only data (i.e., Set MTR); and Model F (Cf) which is
trained based on female-only data (i.e., Set FTR ).
Figure 8: Flowchart of the prediction model generations (left) and the proposed algorithm for EEG
to dynamic speed decoder (right)
Algorithm 1 which is detailed on the right side of Fig. 8 presents the proposed EEG to dynamic
speed decoding strategy with b1and b2detections and heuristic method to reject and reconstruct the
EEG data to the desired values during the b1and b2events. The actual performance is then tested on
new datasets, i.e., Set MTS and Set FTS as defined in Fig. 8, which come from the remaining 20% of the
recorded EEG data. Similar to the training process, gender-based evaluations are also conducted to
analyse the generalization capability of the gender-based models.
In this study, accuracy which is a measure of correctly classified data is considered as the
performance metric for the classification of the states (i.e., A, B and C) as depicted in the middle
plot of Fig. 2. This metric can be computed as
Accuracy =TP +TN
TP +TN +FP +FN (9)
where TP, TN, FP and FN represent true positive, true negative, false positive and false negative
respectively. The ultimate goal is however to ensure the actual dynamic speed command, ˆνd,isdriven

CMC, 2022, vol.73, no.1 621
as close as possible to the target command, νd. Thus, to penalize the mismatch between the two, the
mean absolute error (MAE) is computed as follows:
MAE =1
n
n

i=1
νd,i−ˆνd,i
(10)
where nis the total sampled data for each test. This metric will be a more accurate representation of
the overall performance since it takes into account the effectiveness of the voluntary blink detection
that affects the state transitions. Results from these performance evaluations are presented in the next
section.
3Results
To demonstrate the effectiveness of the proposed GPM in decoding the single-channel EEG data
into the desired dynamic commands, the performance is compared against other competitive classifiers
using 5-fold cross validations as well as the conventional method (Conv) which relies solely on the
eSense meter and the proposed voluntary blink detection for state transitions. The classifiers evaluated
in this study are LDA, SVM, kNN, EL and NN which have previously been employed for classification
with a single-channel EEG device in recent studies [22,29]. In addition, the results with and without
the filters are also recorded for further analyses.
Tab. 1 compares the overall performance of the proposed GPM and other methods from the test
conducted on Set FTS +MTS with Stimulus 1 using Model G. “No F”, “B” and “H”denote “No Filter”,
“Boxcar filter” and “Hanning filter” respectively. In general, the GPM considerably outperforms the
rest in terms of accuracy and MAE, both with and without filters. The highest accuracy and lowest
MAE obtained are 96.5% and 0.7 cm/s respectively with Hanning filter.
Table 1: Overall performance evaluations of the proposed GPM and other ML classifiers from the test
conducted on Set FTS +MTS with Stimulus 1 using Model G
Model G (Tested on set FTS +MTS)
Accuracy MAE (cm/s)
No F B H No F B H
Conv 0.540 - - 7.695 - -
LDA 0.670 0.820 0.850 4.050 2.845 2.245
kNN 0.775 0.915 0.850 2.800 1.700 2.245
SVM 0.670 0.915 0.850 4.050 2.845 2.245
EL 0.665 0.820 0.850 4.350 2.845 2.245
NN 0.775 0.820 0.850 2.800 2.845 2.245
GPM (prop.) 0.880 0.925 0.965 1.645 1.500 0.700
Tabs. 2 and 3illustrate the difference in the performance of Model G and Model F/M when
evaluated based on genders. Via the proposed GPM, no performance differences can be seen between
the generic and gender-based models, and both result in the best performance with 96% accuracy
and 0.8cm/s MAE with Hanning filter. With regard to male-based evaluations which are presented in

622 CMC, 2022, vol.73, no.1
Tab. 3, a quite similar trend is seen from the DA, EL and NN classifiers except for kNN and SVM
where their generic models outperform their male-based counterparts with MAE of 1.4 cm/s.
Table 2: Performance evaluations of the proposed GPM and other ML classifiers from the test
conducted on set FTS with Stimulus 1 using model G and model F
Model G (Tested on set FTS) Model F (Tested on set FTS)
Accuracy MAE (cm/s) Accuracy MAE (cm/s)
No F B H No F B H No F B H No F B H
Conv 0.520 - - 0.930 - - 0.520 - - 9.390 - -
LDA 0.760 0.900 0.930 3.100 2.000 2.245 0.760 0.910 0.930 3.100 1.800 1.400
kNN 0.760 0.900 0.930 3.100 2.000 2.245 0.550 0.900 0.750 6.000 2.000 3.670
SVM 0.760 0.900 0.930 3.100 2.000 2.245 0.760 0.900 0.930 3.100 1.800 1.400
EL 0.760 0.900 0.930 3.300 2.000 2.245 0.760 0.910 0.740 3.300 1.800 3.870
NN 0.760 0.900 0.930 3.100 2.000 2.245 0.750 0.910 0.930 3.500 1.800 1.400
GPM (prop.) 0.780 0.920 0.960 2.890 1.600 0.800 0.780 0.920 0.960 2.890 1.600 0.800
Table 3: Performance evaluations of the proposed GPM and other ML classifiers from the test
conducted on set MTS with Stimulus 1 using model G and model M
Model G (Tested on set MTS) Model F (Tested on set MTS)
Accuracy MAE (cm/s) Accuracy MAE (cm/s)
No F B H No F B H No F B H No F B H
Conv 0.560 - - 6.000 - - 0.560 - - 6.000 - -
LDA 0.580 0.740 0.770 5.000 3.690 3.090 0.640 0.870 0.770 4.880 2.540 3.600
kNN 0.790 0.930 0.770 2.500 1.400 3.090 0.790 0.860 0.780 3.010 2.740 3.400
SVM 0.580 0.930 0.770 5.000 1.400 3.090 0.790 0.860 0.770 4.880 2.540 3.600
EL 0.570 0.740 0.770 5.400 3.690 3.090 0.620 0.870 0.770 5.310 2.540 3.600
NN 0.790 0.740 0.770 2.500 3.690 3.090 0.790 0.860 0.780 3.010 2.740 3.400
GPM (prop.) 0.980 0.930 0.970 0.400 1.400 0.600 0.980 0.930 0.970 0.400 1.400 0.600
The same evaluations for Stimulus 2 are presented in Tab. 4 for the overall performance, and
Tabs. 5 and 6for the gender-based performances. In contrast to Stimulus 1, the best performance
when Stimulus 2 is employed is achieved via the proposed GPM without filter, which results in 92.5%
accuracy and 1.5 cm/s MAE. What stands out in Tab. 4 is the big gap in performance between the
proposed model and other classifiers where the highest accuracy achieved is only 69.5% via DA and
EL, which is considerably lower than that resulted from GPM. Moreover, the resulting gender-based
models from the DA, kNN, SVM, EL and NN classifications do not improve the predictive ability as
can be observed in Tabs. 5 and 6where the differences with their generic counterparts are negligibly
small. On the contrary, a slight difference in performance is seen between Model G and Model F/M
with GPM; i.e., for the female-based evaluations, Model F resulted in a better performance with 91%

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accuracy and 1.8 cm/s MAE, and for male-based evaluations, Model G with Hanning filter beats
Model M with 98% accuracy and 0.4 cm/s MAE.
Table 4: Overall performance evaluations of the proposed GPM and other ML classifiers from the test
conducted on set FTS +MTS with Stimulus 2 using model G
Model G (Tested on set FTS +MTS)
Accuracy MAE (cm/s)
No F B H No F B H
Conv 0.605 - - 6.450 - -
LDA 0.500 0.660 0.695 6.800 5.670 4.890
kNN 0.500 0.655 0.585 6.900 5.780 6.345
SVM 0.500 0.535 0.585 6.800 5.670 4.890
EL 0.500 0.660 0.695 6.800 5.670 4.890
NN 0.500 0.655 0.580 6.900 5.770 6.445
GPM (prop.) 0.925 0.870 0.915 1.500 2.600 1.700
Table 5: Performance evaluations of the proposed GPM and other ML classifiers from the test
conducted on set FTS with Stimulus 2 using model G and model F
Model G (Tested on set FTS) Model F (Tested on set FTS)
Accuracy MAE (cm/s) Accuracy MAE (cm/s)
No F B H No F B H No F B H No F B H
Conv 0.690 - - 4.500 - - 0.690 - - 4.500 - -
LDA 0.500 0.670 0.710 6.800 5.270 4.490 0.500 0.670 0.710 6.800 5.270 4.490
kNN 0.510 0.660 0.700 6.600 5.490 4.700 0.510 0.660 0.700 6.600 5.490 4.900
SVM 0.510 0.640 0.700 6.600 6.700 4.700 0.500 0.650 0.710 6.800 5.270 4.490
EL 0.500 0.670 0.710 6.800 5.270 4.490 0.500 0.670 0.710 6.800 5.270 4.490
NN 0.510 0.670 0.700 6.600 5.270 4.700 0.500 0.640 0.700 6.800 5.870 4.700
GPM (prop.) 0.890 0.820 0.850 2.200 3.600 3.000 0.910 0.820 0.860 1.800 3.600 2.800
Table 6: Performance evaluations of the proposed GPM and other ML classifiers from the test
conducted on set MTS with Stimulus 2 using model G and model M
Model G (Tested on set MTS) Model F (Tested on set MTS )
Accuracy MAE (cm/s) Accuracy MAE (cm/s)
No F B H No F B H No F B H No F B H
Conv 0.520 - - 8.400 - - 0.520 - - 8.400 - -
LDA 0.500 0.650 0.680 6.800 6.070 5.290 0.500 0.650 0.680 6.800 6.070 5.290
(Continued)

624 CMC, 2022, vol.73, no.1
Table 6: Continued
Model G (Tested on set MTS) Model F (Tested on set MTS)
Accuracy MAE (cm/s) Accuracy MAE (cm/s)
No F B H No F B H No F B H No F B H
kNN 0.490 0.650 0.470 7.200 6.070 7.990 0.490 0.650 0.660 7.200 6.070 5.700
SVM 0.490 0.430 0.470 7.200 9.000 7.990 0.490 0.640 0.460 6.800 6.070 5.290
EL 0.500 0.650 0.680 6.800 6.070 5.290 0.500 0.650 0.680 6.800 6.070 5.290
NN 0.490 0.640 0.460 7.200 6.270 8.190 0.490 0.630 0.470 7.200 6.470 7.990
GPM (prop.) 0.960 0.920 0.980 0.800 1.600 0.400 0.950 0.920 0.930 1.000 1.600 1.400
For clarity and brevity, the performance of the proposed GPMs against other best-performing
models based on gender and stimulus is summarized in Tab. 7. From the table, it can be generally
concluded that while other methods perform substantially worse with Stimulus 2, the GPM approach
demonstrates consistent performance across both stimuli with accuracy above 91% and a maximum
MAE of 1.80 cm/s. Nonetheless, for such a BCI application, Stimulus 1 with GPM is likely to form
a better prediction model since the resulting accuracy reaches 96% with an MAE of no greater than
0.8 cm/s, which is significantly lower than that resulted from Stimulus 2.
Table 7: Summary of the GPM methods against other best performing models based on gender and
stimulus. The corresponding dynamic speed commands are illustrated in Fig. 9 for Stimulus 1 and
Fig. 10 for Stimulus 2
Accuracy MAE (cm/s)
Stimulus 1 Stimulus 2 Stimulus 1 Stimulus 2
Female Male Female Male Female Male Female Male
GPM - 0.98 0.91 - - 0.40 1.80 -
GPM +H 0.96 - - 0.98 0.80 - - 0.40
SVM +H 0.93 - 0.71 - 1.40 - 4.49 -
SVM +B - 0.93 - - - 1.40 - -
EL +H - - - 0.68 - - - 5.29
The corresponding dynamic speed commands are visualized in the upper plots of Figs. 9 and 10
along with the derivatives of the EEG data, ev, in the bottom plots. The target speed command, υd,is
represented by the dashed black lines, while the voluntary blink events which serve as signals for state
transition are denoted by the vertical lines, b1and b2. Comparing Figs. 9 and 10, it can be clearly seen
that Stimulus 2 resulted in a relatively longer delay during the transition from νd=0toνd=20 cm/s,
which accounted for the deteriorating performance when compared to the results from Stimulus 1 in
Tab. 7.

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Figure 9: Illustrations on dynamic speed commands based on GPM approach against conventional
method and other best performing classifiers as recorded in Tab. 7 for Stimulus 1
Figure 10: Illustrations on dynamic speed commands based on GPM approach against conventional
method and other best performing classifiers recorded in Tab. 7 for Stimulus 2
Referring to the responses of evon the bottom plots of Figs. 9 and 10,theb2(b1) events result in
the largest (second largest) magnitude when ev<0 in each test. With the proposed voluntary blink
detections, the transmitted speed commands have been successfully driven to the desired values for
both paradigms as can be seen from the responses of GPM and other classifiers which are represented
by the orange and blue lines respectively. On the contrary, the conventional method performs the
worst due to the nature of the eSense meter reading which has a greater tendency of misclassification
during the voluntary eyeblink events as conjectured in Section 2.2. Another striking observation is
that SVM and EL result in significant delays and mismatches between νdand ˆνdparticularly during

626 CMC, 2022, vol.73, no.1
the state transitions at t=10 s and t=40 s compared to the GPM approach which only causes small
delays during the transition at t=10 s. In practice, such a scenario is undesirable since it will lead to
performance deterioration of the closed-loop motor system and eventually instability. By contrast, the
GPM approach particularly with Stimulus 1 demonstrates considerably smaller errors between νdand
ˆνdwhich only occur when the motor is initially actuated. This is inherently due to the representation
flexibility of the trained models that also provide uncertainty measures over predictions.
4Conclusion and Future Work
Conclusion: In this work, a new BCI decoding strategy via the GPM approach for dynamic
speed commands with a single-channel EEG-controlled actuator has been proposed. The experimental
outcome has demonstrated the superiority of the GPM approach over other existing classifiers in the
literature which include LDA, SVM, kNN, EL and NN. Additionally, further analysis reveals that
while the error performance of other existing methods deteriorates with Stimulus 2 due to signal drifts
resulting from voluntary eyeblinks, the proposed GPM exhibits consistent performance.
Implications of the study: The current study has proposed an improved BCI decoding strategy
based on GPM that can be readily embedded in many affordable off-the-shelf microcomputers. Plus,
the combination of an inconspicuous visual stimulus and voluntary eyeblinks has not just increased the
DoF of a single-channel EEG-controlled actuator, but also eliminated the need of extensive training
that is typically required in most motor-imagery based BCIs. Such an approach will greatly benefit
new BCI users as well as BCI illiterates who are unable to sufficiently modulate their neuronal signals
when controlling an external device.
Limitations and future work: Despite the significant improvements, the proposed method has only
been evaluated with a BCI paradigm that lasted no longer than 50 s. A greater focus on modifying
the stimuli to prolong the attention span could produce interesting findings that account more for
higher DoF EEG-controlled actuators particularly those used in mobile robots. Thus, future work
will encompass the aforementioned research field as well as deployment to robotic platforms which
may necessitate some modifications to address unanticipated issues during real-time implementations.
For instance, when the actuator is subject to external disturbances and diverts away from the targeted
position, a new function to detect such a scenario needs to be embedded in the decoder’s algorithm
to avoid user distraction that can consequently affect the accuracy of the transmitted EEG signal. In
addition, different sizes of datasets may be required to evaluate and further enhance the generalization
capability of the GPM-based decoder.
Acknowledgement: The authors would like to thank all volunteers who have participated in this
experimental study and the Human Research Ethics Committee for approving the protocol which
was conducted in accordance to the ethical principles outlined by the Declaration of Helsinki.
Funding Statement: This work was supported by the Ministry of Higher Education Malaysia for
Fundamental Research Grant Scheme with Project Code: FRGS/1/2021/TK0/USM/02/18.
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the
present study.

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