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SummaryThis project aims to build a system for dynamic and onsite brain state analysis using EEG data. The system will allow users to transit EEG data to an online database through mobile devices, interact with the web server through web interface, and get feedback from EEG data analysis programs on real time bases. The models perform self-adjusting based on the data sets available in the database. This method will be useful in quick-prototyping brain states and give users onsite feedback about their queries. High performance computing platform will provide computational power to ensure real time processing of requests.
Job DescriptionThe challenge of modeling brain waves is to design an inclusive system, which incorporates all the major brain waves and addresses the variables such as specific regions of the brain, inconsistency within samples, limitations of the recording machine, and integrating knowledge form neurobiology in terms of understanding certain brain functions. The method of separately analyzing the important known regions of the brain will provide better insight in the brain wave generation. Additionally, it imperative for brain wave modeling studies to contemplate the rigorous time series analysis of brain waves to decipher trend, irregularities, cycles, seasonality and other variations among waves during different states. Therefore, improvised and advanced machine learning modeling system such as neural network will be implemented on data collected from EEG device. The project aims to address the complexity of classification of brain waves data by modeling the major brain waves independently with clinically significant brain regions combined with the time-series analysis. This will achieve an efficient and predictable brain wave modeling system which has potential application in hospitality and clinical industry for self-controlled deep brain relaxation and early diagnosis of various brain abnormalities respectively.

Sishir Subedi has participated in this project since year 2014 and has acquired skills in using various data analysis and data mining tools to classify data and build models. He is interested in explore different kinds of EEG devices in data collection and compare the performance of different modeling algorithms on data sets with different properties. He will work with our research team in incorporating data analysis and modeling tools into our web based data storage and retrieve system, which is deployed on our cluster-centered grid computing lab. He will then investigate the potentials of applying these analytical tools in brain health problems.
Start Date06/01/2015
End Date05/31/2016
LocationDepartment of Computer Science and Engineering Technology, University of Houston-Downtown, Houston, Texas
Yuezhe Li