IEEE Newsletter

Vikram Shenoy Handiry wins the Best Student Paper

Award at the IEEE SMC International Conference in San

Diego

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

includes:

. 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

teaching.