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Mst (Kamrun) Kamrunnahar

I like to go by **Kamrun Nahar**, which is easier to pronounce, of course!


W-309 Millennium Science Complex
Center for Neural Engineering
Dept. of Engineering Science and Mechanics

Penn State University
University Park, PA 16802

Email: Kamrun Nahar
Phone: (814)865-6951

Current Research:

The overall goal of my current research is to develop an improved, robust, and high performance “thought-guided” robotic control algorithm where electroencephalography (EEG) from the human brain is interpreted for certain actions to be performed by a robotic device. My long-term vision is to assist people with neurological disorders (for example, due to strokes or accidental damage to brain or spinal cord) to conduct motor activities, i.e. move objects by “thought” alone without moving (or being unable to move) body parts. A brain computer interface (BCI) is an alternative communication pathway between the brain (human or animal) and an external device. One motivation of BCI research is to give greater ability to severely disabled patients to interact with their surrounding environments. In BCI development, neuronal signals are translated into commands to build a direct interface between the brain and a device. Although invasive techniques have shown recent promise in the application of BCI, non-invasive scalp EEG based methods may be useful and more easily applied. One of the reasons for the limited performance of currently developed brain BCI systems is, I believe, due to the fact that neural dynamics is too complex for the currently applied proportional feedback control algorithms or filters to be efficient. To address this limitation, I envision a paradigm shift in the neuronal dynamic model identification and control algorithm development in the design and implementation of BCI which will improve the performance of scalp EEG based BCI significantly compared to other existing non-model based approaches using EEG signals. This research has implications for the disabled in our society through the development of smarter, more effective neural prosthetics.

This research is sponsored by a K25 Career Development Award from the National Institute of Neurological Disorders and Stroke (NINDS) at the National Institute of Health (NIH).




(i) Working on: (a) Feature extraction and model identification using non-invasive EEG data from human brain in order to develop control algorithms for Brain Computer Interface systems, (b) Optimization of electroencephalographic (EEG) data acquisition techniques, (ii) co-advised a senior honors thesis, (iii) offered graduate seminar courses (E Sc 597E, 597I) on Brain Computer Interfaces (BCI) in 2009, 2010, 2011 (iv) Contributed significantly to the development of a Laboratory system for human EEG data acquisition and analysis for teaching and research purposes, (v) Led study groups at the Center for Neural Engineering (CNE) by giving weekly seminars on Neural Engineering, (vi) Career Development Award, National Institute of Health (NIH), on “Brain-Machine Interface: A Robust, High Performance Predictive Control Algorithm”. Role: PI.

Worked on: (i) Data Mining and Model development to study corrosion properties of metals and alloys using Neural Networks, statistical methods, and Bioinformatics tools (ii) Trained a WISEr (Women in Science and Engineering) student on data mining methodologies and tools.

Worked on: (i) Modeling Fundamental Role of Nano-scale Oxide Films in the Oxidation/Reduction Reactions on Noble Metal Electrocatalysts, specifically applicable to fuel cells. (ii) Development of Figure of Merit for the Quality of a ZrO2 Coating on Boiling Water Reactor Surfaces.

Worked on: (i) ‘Parameter sensitivity analysis of pit initiation at single sulfide inclusions in stainless steel’. (ii) ‘Portal development for the parameter sensitivity analysis of Copper electrodeposition for making interconnects on microelectronic devices’. (iii) ‘Modeling of Copper electrodeposition in the presence of additive systems’.

Worked on System Identification, Model-based Predictive Controller (MPC) design and analysis.

Assisted with a course on Numerical Methods.

Designed and taught undergraduate courses, developed and instructed laboratory experiments, mentored undergraduate students, and served on the committee for undergraduate student development.



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Penn State Live, ScienceDaily, Science Codex,, ScienceBlog, MyScience, NACE Corrosion Press.

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