The objective of the proposed research is to develop an improved "thought-guided" robotic control algorithm using electroencephalography (EEG) from the human brain for certain actions to be performed by a robotic device. One of the reasons of limited performance of existing brain machine interfaces (BMI) being developed to assist people with neurological disorders to conduct motor activities, I believe, is due to the fact that the dynamics of brain signals are too complex for the existing control algorithms to be efficient. To overcome this, I envision a paradigm shift in the neuronal dynamic model identification and control algorithm development that will help better understand the complex neuronal systems and provide improved performance to the control system. In the proposed project, I plan to investigate the hypothesis: 1) Model-based predictive approach and optimal control strategies will improve signal extraction and control performance in BMI substantially over existing non-model based and non-optimal approaches. I here propose a two part approach: 1) acquire non-invasive human scalp EEG in response to audio/visual cues, and compare and contrast EEG features using existing and proposed model-based techniques in order to develop a data driven, empirical model in state space format, and 2) design an MFC algorithm which, unlike the commonly used filtering and/or proportional feedback control algorithms, will predict the desired movement of the robotic device over a period of time in the future called the prediction horizon, will use an optimization algorithm to calculate the control move at the current sampling time, and will have the ability to be tuned online using a number of controller parameters in order to efficiently control the robotic device. The broader aim of this K25 mentored career award is to apply the candidate's control engineering background to neurological applications and develop an interface between these two research areas. The proposed career development plan includes, in addition to carrying out the proposed research in the Engineering Science and Mechanics Department at Penn State University, extensive training through courses, workshops, and other didactic means which will allow the candidate to build a strong foundation for an independent academic career in neurological problem solutions. Relevance to Public Health: Technology developed in the proposed research will advance the understanding of the complex neurobiological behavior by merging modern control engineering with neurobiology. It has implications that people with neurological disorders such as brain or spinal cord injuries will be better assisted through the development of smarter, more effective neural prosthetics.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Mentored Quantitative Research Career Development Award (K25)
Project #
5K25NS061001-05
Application #
8320183
Study Section
NST-2 Subcommittee (NST)
Program Officer
Ludwig, Kip A
Project Start
2008-09-30
Project End
2013-10-31
Budget Start
2012-09-01
Budget End
2013-10-31
Support Year
5
Fiscal Year
2012
Total Cost
$105,895
Indirect Cost
$7,844
Name
Pennsylvania State University
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
003403953
City
University Park
State
PA
Country
United States
Zip Code
16802
Kamrunnahar, M; Dias, N S; Schiff, S J (2011) Toward a model-based predictive controller design in brain-computer interfaces. Ann Biomed Eng 39:1482-92
Dias, N S; Kamrunnahar, M; Mendes, P M et al. (2010) Feature selection on movement imagery discrimination and attention detection. Med Biol Eng Comput 48:331-41
Dias, Nuno S; Kamrunnahar, Mst; Mendes, Paulo M et al. (2010) Variable Down-Selection for Brain-Computer Interfaces. Lect Notes Comput Sci 52:158-172
Kamrunnahar, M; Dias, N S; Schiff, S J (2009) Optimization of electrode channels in Brain Computer Interfaces. Conf Proc IEEE Eng Med Biol Soc 2009:6477-80
Kamrunnahar, M; Dias, N S; Schiff, S J et al. (2008) Model-based responses and features in Brain Computer Interfaces. Conf Proc IEEE Eng Med Biol Soc 2008:4482-5