An analysis of electroencephalogram (EEG) with machine learning
| dc.contributor.advisor | Cobzas, Dana | |
| dc.contributor.author | Jime, Isra | |
| dc.contributor.author | Emery, Jesse | |
| dc.contributor.author | Phan, Nhi | |
| dc.date.accessioned | 2024-06-25T20:15:48Z | |
| dc.date.available | 2024-06-25T20:15:48Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | 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. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14078/3640 | |
| dc.language.iso | en | |
| dc.relation.uri | https://hdl.handle.net/20.500.14078/3639 | |
| dc.rights | All Rights Reserved | |
| dc.subject | electroencephalogram (EEG) | |
| dc.subject | EEG events | |
| dc.subject | machine learning models | |
| dc.title | An analysis of electroencephalogram (EEG) with machine learning | en |
| dc.type | Student Report |
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