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Browsing by Author "Phan, Nhi"

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    An analysis of electroencephalogram (EEG) with machine learning
    (2024) Emery, Jesse; Phan, Nhi; Jime, Isra; Cobzas, Dana; Hassall, Cameron D.
    Our capstone project was done in collaboration with Dr. Cameron Hassall from the Psychology department at MacEwan University. Our data was based on one of Dr. Hassall’s papers on “Task-level value affects trial-level reward processing” (Hassal, C, 2022), where he wanted to determine if the Anterior Cingulate Cortex was responsible or involved in decision making. To determine this, a task sequence was carried out 427 times using 12 participants over a 52 minute period. While the participants completed these tasks, brain activity was being measured using an electroencephalogram (EEG). For our project, the goal was to train a machine learning model to accurately classify an EEG event after training on past events. In greater detail, we focus on the brain signal when the participant hit the left or right button in response to the stimulus which are colored shapes.
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    An analysis of electroencephalogram (EEG) with machine learning
    (2024) Jime, Isra; Emery, Jesse; Phan, Nhi; Cobzas, Dana
    Our capstone project was done in collaboration with Dr. Cameron Hassall from the Psychology department at MacEwan University. Our data was based on one of Dr. Hassall’s papers on “Task-level value affects trial-level reward processing” (Hassal, C, 2022), where he wanted to determine if the Anterior Cingulate Cortex was responsible or involved in decision making. To determine this, a task sequence was carried out 427 times using 12 participants over a 52 minute period. While the participants completed these tasks, brain activity was being measured using an electroencephalogram (EEG). For our project, the goal was to train a machine learning model to accurately classify an EEG event after training on past events. In greater detail, we focus on the brain signal when the participant hit the left or right button in response to the stimulus which are colored shapes.
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    Generating functions related to the Fibonacci substitution
    (2023) Pouti, Aisling; Phan, Nhi
    In this paper, two generating function representations of the Fibonacci Substitution Tiling are derived and proven to converge on the interval -1
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    Leveraging machine learning to predict factors that drive successful basketball team formation
    (2025) El-Hajj, Mohamad; Kwon, Benjamin; Jethro Infante, Craeg; Steed, Jackson; Gore, Victor; Phan, Nhi; Elmorsy, Mohammed; Pang, Xiaodan
    This study delves deep into the key factors affecting the likelihood of NCAA basketball players getting drafted into the NBA. The study highlights the importance of offensive metrics such as points scored and offensive ratings in predicting an NCAA player’s chances of being drafted into the NBA by utilizing an unsupervised learning clustering model and a supervised decision tree model. This underscores the significance of offensive statistics in a player’s skill set and suggests that players and coaches should prioritize improving these metrics to enhance a player’s draft potential. The study found that defensive metrics like defensive ratings and blocks have less impact on overall draft potential than offensive metrics. A crucial point to note is that a team’s success often relies on having its top players actively participating on the court. This research enhances our understanding of the factors influencing the draft prospects of NCAA basketball players. It underscores the advancement of basketball analytics and paves the way for further research on player performance metrics and their influence on the scouting and selection of professional athletes.