abstract: The human brain controls a person's actions and reactions. In this study, the main objective is to quantify reaction time towards a change of visual event and figuring out the inherent relationship between response time and corresponding brain activities. Furthermore, which parts of the human brain are responsible for the reaction time is also of interest. As electroencephalogram (EEG) signals are proportional to the change of brain functionalities with time, EEG signals from different locations of the brain are used as indicators of brain activities. As the different channels are from different parts of our brain, identifying most relevant channels can provide the idea of responsible brain locations. In this study, response time is estimated using EEG signal features from time, frequency and time-frequency domain. Regression-based estimation using the full data-set results in RMSE (Root Mean Square Error) of 99.5 milliseconds and a correlation value of 0.57. However, the addition of non-EEG features with the existing features gives RMSE of 101.7 ms and a correlation value of 0.58. Using the same analysis with a custom data-set provides RMSE of 135.7 milliseconds and a correlation value of 0.69. Classification-based estimation provides 79% & 72% of accuracy for binary and 3-class classication respectively. Classification of extremes (high-low) results in 95% of accuracy. Combining recursive feature elimination, tree-based feature importance, and mutual feature information method, important channels, and features are isolated based on the best result. As human response time is not solely dependent on brain activities, it requires additional information about the subject to improve the reaction time estimation. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2019
| Identifer | oai:union.ndltd.org:asu.edu/item:55526 |
| Date | January 2019 |
| Contributors | Chowdhury, Mohammad Samin Nur (Author), Bliss, Daniel W (Advisor), Papandreou-Suppappola, Antonia (Committee member), Brewer, Gene (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 77 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/ |
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