Poster Session

Title

19. Analysis of Electroencephalogram (EEG) Signals through Data Science and High Performance Computing (HPC) and their relevance to human brain functioning

Presenter Information

Hannah McSwainFollow

Faculty Mentor(s)

Dr. Luis Cueva Parra

Campus

Dahlonega

Proposal Type

Poster

Subject Area

Computer Science

Location

Nesbitt 3110

Start Date

13-3-2020 12:00 PM

End Date

13-3-2020 1:30 PM

Description/Abstract

Analyzing Electroencephalogram (EEG) signals and ERP event-related potentials (ERP) can lead to insight on the human brain functioning. Mathematical, computational and data science tools can be used to analyze the patterns of the EEG waves produced by the brain. Multichannel EEG signals are analyzed considering its spatial configuration by using mathematical transformations and machine learning techniques. To speed up the computations during the analysis high performance computing tools are employed. Based on the analysis, EEG signal’s patterns will be identified and correlated with specific brain functions. The resulting information (model) could be in the prediction of what a human subject is trying to communicate just by the patterns in its EEG brain signals. Also, these results can be used to contribute to a larger discussion of how these signals relate to mental illnesses such as dementia, schizophrenia, and Alzheimer’s disease. With more information regarding this brain activity, progress can be made in the diagnosis and treatment in these medical fields.

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Mar 13th, 12:00 PM Mar 13th, 1:30 PM

19. Analysis of Electroencephalogram (EEG) Signals through Data Science and High Performance Computing (HPC) and their relevance to human brain functioning

Nesbitt 3110

Analyzing Electroencephalogram (EEG) signals and ERP event-related potentials (ERP) can lead to insight on the human brain functioning. Mathematical, computational and data science tools can be used to analyze the patterns of the EEG waves produced by the brain. Multichannel EEG signals are analyzed considering its spatial configuration by using mathematical transformations and machine learning techniques. To speed up the computations during the analysis high performance computing tools are employed. Based on the analysis, EEG signal’s patterns will be identified and correlated with specific brain functions. The resulting information (model) could be in the prediction of what a human subject is trying to communicate just by the patterns in its EEG brain signals. Also, these results can be used to contribute to a larger discussion of how these signals relate to mental illnesses such as dementia, schizophrenia, and Alzheimer’s disease. With more information regarding this brain activity, progress can be made in the diagnosis and treatment in these medical fields.