mented based on different clas-
sification techniques such as
support vector machines, Naïve
Bayesian and Neural networks.
The theory behind the classifica-
tion techniques is fascinating. A
good example to describe the
theory of classification is a deci-
sion you take on seeing some-
thing. You identify someone by
their face, colour, skin and so
on. The same thing happens in
machine learning with classifica-
tion. The machine interface learns
and practices itself to identify and
differentiate the features it’s fed
with and form decision boundar-
ies with new data. So, the final
output is a machine interface that
can analyze brain signals of a
human and learn on its own to identify the actions of
the brain. The output can be used for many purposes, be
it medical or entertaining stuff. Automated wheelchairs,
mind controlled home environments and humanoid
robots are few examples for BCI applications.
The author is proud to mention the University of
Peradeniya, as an institute that has already carried
out research related with BCI. Currently, there are sev-
eral BCI related undergraduate projects ongoing at the
Department of Electrical and Electronic Engineering of
the University of Peradeniya. One such project is control-
ling a robotic arm with EEG signals. The undergraduates
are also carrying out a research to control a prosthetic
arm using EOG signals. The projects are supervised by
Dr. Janaka Wijeyakulasooriya and Dr. Ruwan Ranaweera.
They are carried out in collaboration with the Faculty of
Medicine of the University of Peradeniya. BCI interfaces
are a promising area in the scope of bio-medical engi-
neering. Dedication and hard work of many scientists and
engineers in the field of BCI research have paved way for
miracles such as humanoid robots. Scientists’ dreams
are becoming reality as BCIs are improving day by day
like the rest of science and engineering. So, let’s expect
miracles in engineering in the future with BCI.
Pasindu Perera
Final Year Undergraduate
Department of Electrical and Electronic Engineering
References
1. Jorge Baztarrica Ochoa, Prof Touradj Ebrahimi,
EEG Signal Classification for BrainComputer
Interface Applications, 2002
2. Fabien Lotte, Marco Congedo, Anatole L_
ecuyer, Fabrice Lamarche, Bruno Arnaldi. A
review of classification algorithms for EEG-
based brain computer interfaces. Journal of
Neural Engineering, IOP Publishing, 2007, 4,
pp.24.
3. Mostafa Mohammadpour, Mohammad Kazem
Ghorbanian, Saeed Mozaffari, Comparison of
EEG Signal Features and Ensemble Learning
Methods for Motor Imagery Classification,in
2016 Eighth International Conference on
Information and Knowledge Technology (IKT),
Hamedan, Iran
Gauge Magazine University of Peradeniya 45