Systems, Man & Cybernetics - January 2015 - 35

Engineering of the RMCC as an assistant professor. His research interests
are mainly focused on autonomous
systems, especially the decentralized
control of multiple vehicles, learning
The Team
and adaptation of autonomous robots,
The Autonomous Robotics Research
and modeling of complex systems
Group includes researchers in three
with game theory.
institutions: 1) the
Howard M.
Royal Military ColSchwartz received
l e ge of C a n a d a
Vikram Shenoy,
his B.Eng. degree
(RMCC), 2) Carleton
from McGill UniverUniversity, and 3)
a Ph.D. student
sity, Montreal, QueQ u e e n's Un i v e rat Nanyang
bec, Canada, and his
sity. The main interTechnological
M.S. and Ph.D. deest of the group is
University in
grees from the Masmachine learning,
sachusetts Institute
especially reinforceSingapore, won
of Technology, Camment learning, and
the annual
bridge. His research
multiple robotics,
competition for
interests include
from ground robots
the best oral
adaptive and intellito unmanned aerial
gent control systems,
vehicles (UAVs) and
presentation and
robotics, system
autonomous underpaper.
modeling, and syswater vehicles. It
tem identification.
counts with stateHis most recent reof-the-art lab facilisearch is in multiagent learning with
ties located at RMCC with more than a
applications to teams of mobile robots.
dozen ground robots and ten UAVs.
a learning agent in an environment
with Poissonian-type stochastically
delayed rewards.

About the Authors
Jeffrey S. Campbell is a pilot in
the Royal Canadian Air Force with
the rank of second lieutenant. He
completed his undergraduate degree
in 2012 at the RMCC in Kingston,
Canada. While there, he specialized
in robotic control and worked on
quadrotor UAVs. In 2014, he received
his master's degree in electrical engineering from Carleton University
in Ottawa, Canada. His work there
focused on unsupervised machine
learning with applications in mobile
robotics. He is currently posted at
Defence Research and Development
Canada to work on over-the-horizon
radar projects.
Sidney N. Givigi received his
B.Sc. degree in computer science and
M.A.Sc. in electrical engineering from
the Federal University of EspĂ­rito
Santo, Brazil. He received his Ph.D.
degree in electrical and computer
engineering from Carleton University,
Canada. In 2009, he joined the Department of Electrical and Computer

The Best Student
Paper Award
Vikram Shenoy, a Ph.D. student at
Nanyang Technological University
in Singapore, won the annual competition for the best oral presentation and paper. The paper is "An
Iterative Optimization Technique for
Robust Channel Selection in Motor
Imager y-Ba sed Bra in- Computer
Interface" (with coauthor Vinod
Achutavarrier Prasad).
Shenoy's research is part of a program lead by Prof. Vinod A. Prasad,
Nanyang Technological University,
in collaboration with Dr, Guan Cuntai, Institute for Infocomm Research,
A*STAR, Singapore.
About the Paper
Brain-computer interface (BCI) is
an interesting research area that
opens up the avenue for decoding human intention, which has so
far been only in the realm of science fiction. An increasing number
of patients every year with stroke,
Ja nu a r y 2015

(From left) Larry Hall, michael
H. Smith, and Vikram Vinod.

neuromuscular disorders like amyotrophic lateral sclerosis, and cerebral
palsy require an effective neurorehabilitation strategy, and BCI could be
a viable option. BCI provides a direct
communication and control pathway
between the brain and a computer/
machine, bypassing the conventional pathway of nerves and muscles.
Elect roencepha log raphy (EEG)
is the most commonly used brain
signal acquisition technique in BCI
systems. The use of motor imagery
(imagination of movement of limbs)
patterns in EEG-based BCI has been
proven as an effective method to
translate the user's movement intention into commands for controlling
external devices like a robotic arm,
which assists in neurorehabilitation.
Conventional EEG headsets come
with a variable number of sensing
electrodes called channels (as sparse
as 16 channels to as dense as 256
channels). The use of fewer channels
results in computational efficiency
but reveals very limited information
about the brain activity. Meanwhile,
large numbers of channels uncover
more information about the brain
signal but result in increased computation and experimental preparation
time, which is not advisable in realtime BCI applications. To strike a balance between the two, it is necessary
to optimize the number of EEG channels used.
In the work presented in this paper, the authors use a priori information of the motor imagery task to propose an iterative method for selecting
the most relevant channels. The

IEEE SyStEmS, man, & CybErnEtICS magazInE

35


http://www.B.Sc http://M.A.Sc

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