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Vikram Shenoy Handiry wins the Best Student Paper
Award at the IEEE SMC International Conference in San

Vikram Shenoy, a Ph. D. student at Nanyang Technological University in Singapore won the
annual competition for the best oral presentation and paper at the IEEE Systems, Man, and
Cybernetics Society Conference. The paper's title is "An Iterative Optimization Technique for
Robust Channel Selection in Motor Imagery based Brain Computer Interface" (co-author
Vinod Achutavarrier Prasad).

Brain-Computer Interface (BCI) is an interesting research area which opens up the avenue for
decoding the human intention, which was in the realm of science fiction so far. An increasing
number of patients every year with stroke, neuromuscular disorders like Amyotrophic Lateral
Sclerosis (ALS), cerebral palsy etc. requires an effective neurorehabilitation strategy for
which, BCI could be a viable option. BCI provides a direct communication and control
pathway between brain and computer/machine bypassing the conventional pathway of nerves
and muscles. Electroencephalography (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 robotic arm
which assists in neurorehabilitation.

Conventional EEG headsets come with 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 results in increased computation and experimental preparation time which is not
advisable in real-time BCI applications. To strike the balance between the two, it is necessary
to optimize the number of EEG channels being used. In the work presented in this paper,
authors use apriori information of the motor imagery task to propose an iterative method for
selecting the most relevant channels. The authors make use of publicly available BCI
competition datasets with 118 channels (dense) and 22 channels (sparse) to validate whether
the algorithm is invariant to number of channels being used. The proposed method results in
better accuracy of classifying the movement imagination between right hand and left hand
compared to state-of-the-art methods with a significant reduction in the number of channels.
The authors have looked into another interesting aspect of handling subject-variability, which
is hardly explored in the literature. Each individual has different head geometry but the EEG
headsets come with a standard size. This makes it little difficult to generalize the signal
information from different parts of the brain scalp. The proposed method addresses this
variability between different subjects based on frequently selected channels across subjects
thereby revealing the significance of different channels. This will aid in substantially reducing
the preparation time when performing multiple session BCI experiments for a larger pool of
subjects, especially when using high dense EEG headsets. Having demonstrated the good
classification accuracy with far lesser computation time, the authors believe that the
proposed method might prove beneficial in online motor imagery BCI experiments.

Vikram Shenoy's research is part of a program lead by Professor Vinod A Prasad, Nanyang
Technological University, in collaboration withand Dr Guan Cuntai, Institute for Infocomm
Research, A*STAR, Singapore. Their work focuses on Robust and accurate signal
processing techniques for robust and accurate Brain-Computer Interfaces (BCI.) This

. Robust and accurate motor imagery classification algorithms
. Decoding limb movement kinematics from Electroencephalogram-based BCI
. BCI-based neurofeedback games for enhancing cognition skills.

The group proposed robust and accurate signal processing algorithms to extract
discriminative brain activation patterns during hand, foot and tongue in an EEG based BCI
system. The group further investigated the intra subject and inter subject spectral variability of
discriminative motor patterns in EEG and have proposed algorithms to effectively track the
varying patterns. The non-stationary features of EEG signals were adaptively estimated in the
proposed algorithms and the results achieved indicate that the proposed approach
outperforms the state-of-art methods in terms of classification accuracy and highlights the
necessity of efficient frequency band selection techniques in real time MI-BCI applications.
They also contributed in another area of movement control BCIs that aims to decode hand
movement kinematics from non-invasive scalp recordings. Moreover, they introduced
algorithms for demonstrating the presence of movement parameter information in specific
space, frequency and time locations of EEG and have developed feature extraction tools to
detect them. Significant contributions were made in binary classification of hand movement
speed and direction. The proposed algorithms were modified further to achieve multiclass
classification of direction and continuous reconstruction of hand movement speed and
trajectory. They developed BCI-based Computer games to enhance attention (concentration)
and memory of children with attention-deficit hyperactivity disorder (ADHD). Such children
exhibit lack of attention, change in the behavioural mood, hyperactivity and impulsivity.
Worldwide, ADHD is common with an estimated prevalence rate of 5.3%. In Singapore,
ADHD ranks as the third highest cause of disease burden in youths below the age of 14.
Medications often cause significant side effects including poor appetite and physical growth
suppression, and have only limited impact on ADHD treatment. Alternative methodologies to
train children for enhancing their concentration (attention) skills need to be developed to
complement conventional pharmacological treatment. The Our research team developed a
brain wave (EEG) driven computer game that can be used by children suffering from ADHD to
boost their concentration abilities. Brain signal corresponding to concentration of subjects is
used to control the game, which in turn helped to improve the concentration abilities of ADHD
children by playing the game in a relaxed mindset, without the need of undergoing complex
behavioural treatment procedures. TheyWe developed a BCI system comprising of EEG data
acquisition, EEG signal processing methods and computer game which can be controlled
with the concentration data acquired from children.

Dr. Guan Cuntai of the Institute for Infocomm Research, A*STAR, Singapore collaborates with
the team whose members are Postdoctoral Fellows Dr. Kavitha P. Thomas, Dr. Smitha K. G,
and Research Staff, Ms. Neethu Robinson.  The team leader, Vinod A Prasad received his
Ph. D. degree from School of Computer Engineering, Nanyang Technological University
(NTU), Singapore, in 2004.

From September 2000 to September 2002, he was a Lecturer in Singapore Polytechnic,
Singapore. He joined NTU as a Lecturer in the School of Computer Engineering in September
2002 where he is currently a tenured Associate Professor. He has published over 180 papers
in refereed international journals and conferences, supervised and graduated 8 Ph. D.'s He is
a Senior Member of IEEE, Associate Editor of IEEE Transactions on Human-Machine
Systems, Associate Editor of Springer Journal Circuits, Systems, and Signal Processing
Journal (Springer), and Technical Committee Co-Chair of Brain-Machine Interface Systems of
IEEE Systems, Man & Cybernetics Society. He has won the Nanyang Award for Excellence
in Teaching in 2009, the highest recognition conferred by NTU to individual faculty for
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