SUGISAKAMASANORI

Journal of Robotics, Networking and Artificial Life
Vol. 9(3); December (2022), pp. 245–254
ISSN (Online):2352-6386; ISSN (Print):2405-9021
https://alife-robotics.org/jrnal.html

Research Article

Deep Learning Based Imaginary Finger Control Detection
Algorithm
Suresh Gobee1, Norrima Mokhtar1, Hamzah Arof1, Noraisyah Md Shah1, Heshalini Rajagopal2, Wan Khairunizam3
Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Malaysia
Institute of Computer Science and Digital Innovation, UCSI University, 56000 Kuala Lumpur, Malaysia
3
Faculty of Electrical Engineering Technology, University Malaysia Perlis, 02600 Perlis, Malaysia
1
2

ARTICLE INFO
Article History
Received 11 November, 2021
Accepted 08 September 2022
Keywords
BCI
Imaginary finger movement
CNN
Log verification
EEG.

ABSTRACT

Conventionally, the brain signals were analysed manually by the neuroscientists on how the brain
signals reacts in proportion with the human body. However, this process is very time consuming
and unreliable. Therefore, we have proposed a brain signal detection system based on deeplearning algorithm in response to the output of EEG device on the imagery finger movements.
These fingers include thumb, index, middle, ring and little of right hand. In this study, 4
Convolutional Neural Network (CNN) classification models were developed. These 4 CNN
models are different in terms of the pre-processing requirements and the neural networkarchitecture. The best results for offline classification obtained in this project are 69.07% and82.83% respectively in terms of average accuracy from 6-class and 2-class tests. Moreover, thisproject has also developed a proof of concept for applying the trained models in online or realtime classification.
© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/)

only focus on electroencephalogram (EEG) based noninvasive BCI device. According to [2], EEG are mostly
used in other BCI research because of its reliability, easeof use and non-invasiveness. EEG functions by detecting
the brain signals in terms of voltage fluctuations from thehuman scalp through the electrodes. Besides, EEG is highin temporal resolution and relatively low in cost.
In 2018, Samson et al. has developed an EEG-based
Open BCI device for controlling home appliances for
impaired individuals [3]. The EEG signals generated byeye-blinking movement and concentration levels arecollected using four electrodes. The frontal and occipital
lobes, which are located at FP1, FP2, F8, and CP6, areused to insert electrodes. The Mu waves created when theuser blinks his eyes are read by the electrode implanted

1. INTRODUCTION
Brain-computer Interface (BCI) is a device used todetect brain signals [1]. The device enables human brain
activities to interact with the environment [1]. BCI isused widely in medical application, such as stroke
rehabilitation, controlling smart devices, communication
device for patients without motor abilities, mental diseasediagnosis and many more. Generally, there are three
types of BCI devices: invasive, partially invasive, andnon-invasive.
Due to the clinical risks and impracticality ofinvasive and partially invasive BCI, this research will

Corresponding author’s E-mail: sureshgobee@gmail.com, norrimamokhtar@um.edu.my, heshalini@ucsiuniversity.edu.my,khairunizam@unimap.edu.my245

on the frontal love. The electrode on the occipital lobe
reads the alpha impulses produced when the user placed
his teeth against each other at the same moment. Thesesignals will be utilised to operate home appliances as acommand. However, the project is not fully EEG-based
because it utilizes EMG signals as one of the inputs,
which is generated by eye-blinking.
According to [4], there are six classes of brain
signals: motor imagery (MI), sleep stage scoring,emotion recognition, mental workload, seizure detectionand event-related potential detection. Since that thisresearch is about imagery finger detection, therefore, it
falls under the category of MI. MI refers to the mental’s
thought on motor movements [5]. This phenomenon canbe detected by the EEG devices as the brain signals are
induced in motor cortex during imagination of motor
movements.
To classify these signals, deep learning approach areoften being used. Deep learning is a subset of machinelearning in which a computer model learns to perform
certain tasks based on a set of data [6]. The deep learning
architecture is inspired by the structure of human brain;
hence, they are often called as artificial neural networks.
When properly trained, deep learning models can achieve
high accuracy for tasks like classification, regression, andtext generation [7].
The research done by [8] [9] [10] do not implement
any feature extraction technique to pre-process thetraining data. The raw EEG signals were used directly as
the training data for the deep learning algorithm. Due tothe nature of Convolutional Neural Network (CNN)architecture, the learnt parameters in this algorithm form
layers of filters capable of producing the feature maps
from the raw EEG signals. Therefore, by stacking more
convolutional layers, the deep learning algorithm canproduce higher order of feature maps automatically,
increasing the classification accuracy. However, theincrease in number of learnable parameters causes morecomputational power required to execute the algorithm.
The Common Spatial Pattern (CSP) and Fast Fourier
Transform Energy Map (FFTEM) algorithms was usedby [11] for feature computation and feature selection,
respectively. The CSP algorithm works by applying
spatial filters to obtain the distinctive features between
two classes. The FFTEM algorithm then used thesefeatures to compute the energy maps. These energy maps
are used as the training data. This method can reduce thecomputational power needed for running the deeplearning algorithm as lesser neural network layers are
required. However, some of the features from the signals
might be discarded when applying the CSP algorithm
which leads to lower classification accuracy.
In the papers written by [12] [13], Short Time
Fourier Transform (STFT) was applied to convert the
EEG signals into 2D images. The resulting images

contain 3 information: time, frequency, and intensity.
This technique can greatly increase the classification
accuracy of the algorithm since CNN architecture is very
effective on image recognition. However, performing
STFT for feature extraction increases the complexity of
the classification algorithm because higher dimensions ofdata is being used.
Sakhavi et al. has extracted characteristics from
EEG data using the Filter-Bank Common Spatial Pattern(FBCSP) approach [14]. The FBCSP algorithm is an
improved variant of the CSP algorithm. To discover thepatterns between each EEG channel, spatial filters areapplied. This technique yields discriminative featuresthat can be used to distinguish between two classes. The
complexity of the classification algorithm can be reduced
by using FBCSP to pre-process the EEG data, using less
CPU power. However, when compared to using a deeplearning approach to create trained filters for feature
extraction, the FBCSP technique may disregard somecharacteristics in the EEG signals.
The combinations of artefact removal with bandpass
filters, CSP feature extraction and random forest (RF)classifier to classify the imagery finger movements wasdone by [15]. This research has successfully obtained thebest accuracy of 54% with 5 classes (thumb, index,
middle, ring and little) of imagery finger movements.
The research done by [15] uses the combinations ofartefact removal with bandpass filters, ERD/ERS feature
extraction and Support Vector Machine (SVM) classifier.
This research obtains average accuracy of 62.5% inclassifying the 2 classes of imagery movements for left
and right index fingers.
2.

METHODS

3. Overall system

Fig 1: Block diagram for the system in training the deeplearning based classification algorithm.
The system shown in Fig 1 can be separated into 2
sections: data collection and signal pre-processing aswell as training and evaluation of the classification
246

2.1.2 Deep Learning Architecturealgorithm. The former prepares the MI dataset recorded
from the EEG device. In this stage, artefacts removal,
feature extraction and data conversion are performed at
the raw EEG signals. These pre-processed signals arebeing used as the training data for the deep learningmodel. The deep learning model in turn trains the neuralnetwork to classify the MI tasks based on the preprocessed training data. The deep learning model’s
performance will be evaluated in terms of accuracy andloss.

Summarizing the outcomes obtained from theliterature review, there are 3 convolutional layerarchitectures that can be applied in this research as shownin Table 2.
Table 2: Description of different convolutional layerarchitectures.
No.
1

2.1.1 Data Pre-processing Technique
Summarizing the outcomes obtained from literature
review, there are 5 feature extraction techniques that canbe applied in this research as shown in Table 1.
Table 1: Description of pre-processing techniques thatcan be applied in this research.
Pre-processing
Technique
No feature
extraction
EEG channels
selection
Short-time
Fouriertransform(STFT)
Commonspatial pattern(CSP)

Croppedtraining

2

Description
Raw EEG signals are used directlyas the input to the deep learningalgorithm.
Only certain EEG channels that
contains the most MI related
information are selected as the
input to the deep learningalgorithm.
This technique converts the timebased EEG signals into 2D images
as the input to the deep learningalgorithm.
CSP algorithm is applied on the
EEG signals to obtain the
distinctive features between eachclass. The extracted features areused as the input to the deeplearning algorithm.
Each MI trials are cropped with asliding window of smaller period.
This technique increases thenumber of training data andimproves the performance during
online classification.

3

Deep Learning Architecture
2 convolutional layers with one-dimensional
filters. These filters are applied across the timeand channel axis of EEG data, respectively.
This architecture allows the filter parameters tobe learnt separately and produce feature mapson the temporal and spatial information fromthe input signals.
2 convolutional layers with two-dimensional
filters. This architecture is applicable only
when the EEG signals are converted into 2D
images via Short Time Fourier Transform(STFT). This technique can greatly reduce thenumber of learnable parameters in the deeplearning algorithm, hence, reducing thecomputational power needed to run thealgorithm.
The Channel-wise CNN architecture. Thisarchitecture consists of 2 convolution layers
both with 1D filters applied across the time
axis of the EEG signals. Higher order offeature maps can be obtained by applying morethan one filters across the temporal axis of thesignals. Therefore, this can improve theclassification accuracy of the trained model.

In this research, each of CNN architectures will beapplied and compared to obtain the technique that canyield to the highest performance.
2.1.3 Preliminary Design for Data Acquisition
The first step for acquiring the EEG dataset is todetermine the timings where the subjects perform theimagery finger movement tasks. To ensure that thetimings used are consistent across each subject, videos
will be used as the guidance for collecting the dataset. To
achieve this, a timing diagram is developed as the videos
framework as shown in Fig 2.

In this research, combinations of each of these datapre-processing techniques will be applied. The results
are compared to obtain the classification algorithm withthe highest performance.

247

Fig 2: Timing diagram for data acquisition.
10 samples per class per session will be used in thisresearch. The cue time or the period of MI task is 4
seconds. The time interval between each MI is 3 seconds
to allow space for the subject in preparing the next MI
task. A beeping sound will be played 1 second before thecue start to notify the subject for the next MI task.
Therefore, combining the time required for initialcountdown, 76 seconds are needed to record each MI
class per subject.

Fig 1: Illustration of electrode locations and theirrespective channel.
Table 1: The list of electrode locations and theirrespective channel.

2.1.4 Preliminary Design for Artefact Removal

Location

ChannelSince that battery-based EEG device will be used inthis research, the use of notch filter (bandstop filter) toremove the line noise is not required. This is because thebattery power supply can provide perfect direct current(DC) to the device, hence, the line noise is not present in
the EEG signals. Besides, 8-30Hz bandpass filter will beapplied to the EEG signals to remove the noise in thesignals due to muscle movements. This frequency range
is chosen as it covers the mu (8-13Hz) and beta (1330Hz) frequency bands of brain signals, consisting of themost optimal signal-to-noise ratio for MI relatedclassification.

1

2

3

4

5

F
p
1

F
p
2

C C T
3 4 5

9

1
0

1
1

1
2

1
3

1
4

1
5

1
6

C O O Fz 1 2 7

F
8

F
3

F
4

T
3

T
4

P
3

P
4

6

7

8

Before the recording session, the subjects wereasked to ensure the cleanliness of their hair and scalp.
This is to prevent that the EEG signals obtained contains
noises due to the poor conductivity of the electrodes
caused by blockage of grease or dirt on the scalp. The
subjects were briefed about the details and procedures of
the recording session, while any enquiry from thesubjects were answered. After that, they were asked tofill in a consent form as an agreement to participate inthis research.
During the recording session, the subjects wereasked to sit on a comfortable chair in a quiet room, with
their right arm resting on a table and palm facing upwards.
This is to help the subjects on focusing during therecording session. Then, the subjects were asked to wear
the EEG headset. The electrodes were tuned such thatthey were touching the subjects’ scalp. A video wasplayed for each class and the subjects were asked toperform MI trial according to the instructions given in thevideo as shown in Fig 4. The subjects were requested not
to do any muscle movements such as blinking when
performing the MI trials. This is because the muscle

2.1.5 EEG Dataset Collection
In stage 1, the EEG headset is used to record the brain
signals of the subjects. There are 6 subjects who
participated in the EEG data collection for this project.
Two recording sessions were conducted for each subject,with 10 MI trials per class per session. Therefore, there
will be a total of 20 MI trials for each class per subject.
The EEG signals are sampled at the rate of 250Hz and 16
channels. The electrode locations (based on 10-20 EEGelectrode placement standard) and their respective
channels were illustrated in Fig 3 and summarized inTable 3.

248

movements will decrease the quality of the EEG dataset,as it confuses the deep learning algorithm during thetraining session.

In this stage, the EEG signals data was pre-processed
before being used as the training and testing data for the
CNN classification models. The EEG signals werepassed through an 8-30Hz bandpass filter to remove theartefacts. After artefact removal, the EEG signals aretrimmed according to the MI task for each class and therest interval. Two recording sessions were conducted foreach subject, with 10 MI trials per class per session.
Therefore, there will be a total of 20 MI trials for eachclass per subject.
The EEG data was recorded for 1 minute for eachclass and the duration is for 20 minutes between the restand IM finger movement class. Out of these 20 MI trails,
two trials data set was set aside for testing and 18 trails
were used for testing. Each class trial consists of 16
channel recording of EEG data for 20 minutes.

Fig 4: Image montage of the cues used in the video forrecording EEG dataset.

The trimmed dataset is then separated into testing andtraining data with the ratio of 9:1. After that, cropped
training algorithm is applied on the dataset to further
increase the number of testing and training data.

After the first recording session was completed, thesubjects were given 5 minutes of break before continuingwith the second session. The total duration of recording
sessions is estimated to be around 15 to 20 minutes per
subject excluding the time taken for briefing.

After trimming, the EEG dataset are down sampledfrom 250Hz to 125Hz. During dataset collection, theEEG signal values were stored directly into the SD cardfrom the EEG headset. Using SD card as storage allows
more consistent and higher sampling frequency (250Hz)
as compared with streaming via Bluetooth connection
and storing the data in computer. However, during online
classification, the EEG signals are streamed directly into
the computer via Bluetooth connection at the sampling
frequency of 125Hz. Therefore, down sampling on the
EEG dataset is required so that the trained CNN
classification models can be used in online classification.
The flow is shown in Fig 6.

2.2 Classification Model Development
Variable that can be measure are the EEG signals we
get form the 16 electrodes from the BCI (Brain Computer
Interface) Headset. The 16 electrodes will give 16
channels of reading. The signal for each channel issampled at 250Hz. The BCI equipment is shown in Fig 5.

Fig 6: Block diagram of classification development
Feature extraction is applied to the EEG dataset
before training and testing the CNN classification model.
4 types of CNN classification model are developed in thisproject to classify the EEG signals. These models differ
with each other in terms of feature extraction requirement
and the neural network architecture as summarized inTable 4.

Fig 5: BCI Equipment.

249

Table 4: Summary of 4 types of CNN classification
model. The abbreviations used are power spectral density(PSD), short-time Fourier transform (STFT), frequency
band splitting (FBS), standard deviation (STD),
convolutional (Conv) and fully connected (Fc).
Model
A
B
C
D

Feature Extraction
PSD

STFT

FBS

Mean

STD

x

x

x

x
x

EEG signals. This allows the neural network to learn andextract the features on the temporal axis for each channelon the EEG signals. The feature maps produced by
Conv1 layer are fed into the second convolutional layer
Conv2. As shown in Fig 8, this layer consists of 24 onedimensional filters applied across the spatial axis on the
EEG signals. This allows the neural network to learn andextract the features related to the spatial information fromthe EEG signals. The fully connected layer Fc3 is added
to allow the neural network in recognizing more complex
features. The layers labelled as Fc4 act as theclassification layer of Model A, with the number of
output classes as the number of nodes (N).

Number of
CNN
LayersConv Fc
2
2
2
1
2
3
2
2

Each of the CNN classification model wastrained with 6 different sets of classes. The first set
contains 6 classes including thumb, index, middle, ring,
little and rest MI. The other 5 sets (2 classes) contain the
combination of thumb, index, middle, ring and little MIeach with respect to the rest MI. The data from 6 subject
will be averaged to get the accuracy and the standard
deviation of each model. Details about the CNN
classification models and the test results will be discussed
in section 3.
Fig 8: Illustration on the filter size with respect to EEG
signals for each convolutional layer in Model A.

2.2.1 Models
Model A
Model A uses the raw EEG signals, whichcontain the sampled voltage values per unit time for eachchannel, as the input to the CNN architecture. Therefore,feature extraction is not required in this model. Instead,these features are produced by the filters in the
convolutional layers during the training process as shownin Fig 7.

Similar method was applied for the other 3 models.
Model B, Model C and Model D as describe in Table 4.
2.2.2 Testing of the Proposed Design
There are 6 subjects participated in the EEG datacollection for this project. The test for online
classification requires the participation of the same
subject in separate session after CNN classification
models were trained. Therefore, this test could only be
conducted on one subject due to the limitation of time forthe subject’s participation.
4. RESULTS AND DISCUSSIONSAs shown in Table 5 and Fig 9, model A has the average
accuracies of 55.46% and 74.17% on the 6-class and 2class tests, respectively. Although the accuracy in 2-classtest is 18.71% higher than the 6-class test.

Fig 7: Block diagram of Model A.
The CNN architecture of Model A consists of 9
layers of neural network components as illustrated in Fig
7. The first convolutional layer Conv1 consists of 68 onedimensional filters applied across the temporal axis of the250

Table 5: Test results of offline classification for Model A.

1
2
3
4
5
6
Avg

2-Class Model

Accuracy (%)

Accuracy (%)

62.22
51.67
51.67
54.45
71.11
41.67
55.46

84.00
65.00
67.00
71.00
89.00
69.00
74.17

100

Accuracy

Subject

6-Class Model

Accuracy (%)

2
3
6-Class Model

452-Class Model6

Fig 10: Bar chart showing test results of offline
classification for Model B.
Model C has the average accuracies of 46.48% and
70% on the 6-class and 2-class tests respectively.
Although the accuracy in 2-class test is 23.52% higher
than the 6-class test as shown in Table 7 and Fig 11.

50

Table 7: Test results of offline classification for Model C.
1

2

3

4

5

6
Subject

Subject
6-Class Model

1
2
3
4
5
6
Avg

2-Class ModelFig 9: Bar chart showing test results of offline
classification for Model A.
Model B has the average accuracies of 55.64% and81.83% on the 6-class and 2-class tests respectively as
shown in Table 6 and Fig 10. This result indicates that
Model B is better in performance when classifying lower
number of classes.

Accuracy

6-Class Model

2-Class Model

Accuracy (%)

Accuracy (%)

73.33
52.22
53.33
54.45
53.89
46.60
55.64

98.00
77.00
83.00
79.00
83.00
71.00
81.83

6-Class Model

2-Class Model

Accuracy (%)

Accuracy (%)

61.11
30.00
34.45
50.56
66.67
36.11
46.48

86.00
61.00
63.00
70.00
87.00
53.00
70.00

Model C Test Accuracy

Table 6: Test results of offline classification for Model B
.

1
2
3
4
5
6
Avg

1

Subject

100

Subject

500Model A Test Accuracy0

Model B Test Accuracy

100
50
0

1

2

3

4

5

6

Subject
6-Class Model

2-Class Model

Fig 11: Bar chart showing test results of offline
classification for Model C.
Model D has the average accuracies of 69.07% and82.83% on the 6-class and 2-class tests respectively as
shown in Table 8 and Fig 12. The overall result showsthat Model D has the highest accuracy both 6-class and2-class tests. This indicates that the combinations offeature extraction techniques with frequency band
splitting, mean and standard deviation are most
optimized for MI related EEG classification by using
CNN architecture as shown in Table 9.

251

Table 8: Test results of offline classification for Model D.
6-Class
Model
Accuracy
(%)
78.33
56.11
69.45
71.67
73.89
65.00
69.07

Subject
1
2
3
4
5
6
Avg

The lower the value of standard deviation, the moreconsistent is the CNN model. This is because lower
standard deviation indicates that the difference inaccuracies across each subject is low. Therefore, Model
D has the highest consistency among the other CNN
classification models.

2-Class Model
Accuracy (%)
97.00
75.00
79.00
84.00
88.00
74.00
82.83

4. CONCLUSIONSThis project contributes to the creation of 4techniques to classify the imagery finger movements on
EEG signals by using the state-of-the-art deep learningtechnology. The best results achieved was 82.83% and69.07% in terms of accuracy on classifying the MI of 2classes and 6 classes respectively. In addition, this project
also contributes to the development of EEG classification
algorithm that is both lightweight and accurate. The
training, testing and online classification could be run
entirely on CPU without the need for discrete graphics.
Hence, the low computational power requirement
contributes to reducing the cost of BCI solutions andmaking it affordable to more people. Moreover, the low
computational power requirement also indicates that it ispossible to integrate the BCI device and classification
algorithm as a battery-based standalone system, which
can be useful when being used as the controller for otherdevices. This project has successfully achieved all the
research objectives. 4 types of CNN based deep learningalgorithm were developed for classifying the imagery
finger movement consisting of 6 classes and 2 classes.
The best results obtained are 69.07% and 82.83% inmodel D, respectively in terms of average accuracy for 6
classes and 2 classes. It is suggested deep learning
method with better features and classifiers will further
improve the accuracy of the results.

Accuracy (%)

Model D Test Accuracy
100
0

1

2

3

4

5

6

Subject
6-Class Model

2-Class Model

Fig 12: Bar chart showing test results of offline
classification for Model D.
Table 9: Summary of test results for all models in offline
classification.

Model
A
B
C
D

6-Class Model

2-Class Model

Accuracy (%)

Accuracy (%)

55.46
55.64
46.48
69.07

74.17
81.83
70.00
82.83

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The consistency of the CNN classification model to be
implemented across different individuals can beindicated by calculating the standard deviation on theclassification accuracy across each subject as shown in
Table 10.
Table 10: Standard deviations of classification accuracy
for each subject.
Model
A
B
C

D

Standard Deviation
2-Class
6-Class Model
Model

Average

10.11

9.89

10.00

11.29

18.44

14.86

15.25

13.89

14.57

7.75

8.75

8.25

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Dr. Norrima Mokhtar

Norrima Mokhtar received the
Bachelor of Engineering (B. Eng)
degree in Electrical Engineeringfrom University of Malaya in 2000.
After working two years with
International Telecommunication
Industry with attachment at Echo
Broadband GmbH, she managed tosecure Panasonic Scholarship which required intensive
screening at the national level in 2002. She finished her
Master of Engineering of Oita University, Japan underfinancial support received from Panasonic Scholarship in
2006. Consequently, she was appointed as a lecturer toserve the Department of Electrical Engineering, Universityof Malaya immediately after graduating with her Master ofEngineering. As part of her career development, she
received SLAB/SLAI scholarship to attain her Ph.D. in
2012. She is now serving Department of ElectricalEngineering, University of Malaya.
Professor Dr. Hamzah ArofHe received the B.Eng. degree from
Michigan University 1992 and Ph.D.
degree from University of Wales,
Cardiff. He is currently serving as
senior professor at the Department ofElectrical Engineering, University of
Malaya. His research interests areimage processing, machine learningand photonics.
Dr. Noraisyah Binti Mohamed Shah
She received the B.Eng. degree from
University of Malaya in 1999, Master
Degree from Oita Universitym,
Japan, and the Ph.D. degree from
George Mason University. She iscurrently a senior lecturer at the
Department
ofElectricalEngineering, University of Malaya.
Her research interests are image
processing, machine learning andsatellite communication.
Dr. Heshalini Rajagopal
She received her PhD and Master's
degree from the Department ofElectrical Engineering, University of
Malaya, Malaysia in 2021 and 2016,
respectively. She received the B.E(Electrical) in 2013 Currently, she isan Assistant Professor in UCSI
University, Kuala Lumpur, Malaysia.
Her research interest includes image
processing, artificial intelligence and machine learning.

Authors Introduction
Mr. Suresh Gobee

He received his Master's degreefrom the Department of MechanicalEngineering, University Science
Malaysia, in 2002. He received the
BTECH in 1998 form University
Science Malaysia Currently, he is alecturer in Asia Pacific University ofInnovation and Technology (APU),
KL, Malaysia. His research interest
includes image processing, artificial intelligence and BCI.

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Wan Khairunizam

Khairunizam WAN received his B.
Eng. degree in Electrical & ElectronicEng. from Yamaguchi University and
Ph.D. in Mechatronic Eng. from
Kagawa University, in 1999 and 2009
respectively. He is currently an Assoc.
Prof. at School Of Mechatronic
Engineering, University Malaysia Perlis. He is member ofBoard of Engineer and Institute of Engineer, Malaysia. His
research interest is in Human-Computer Interaction (HCI),
Intelligent Transportation System, Artificial Intelligenceand Robotics.

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