Date Presented

Spring 5-1-2023

Document Type

Thesis

Department

Biomedical Science

First Advisor

David O. Freier, PhD

Second Advisor

Price Blair, PhD

Third Advisor

Cynthia Ramsey, DMA

Abstract

Music has many awe-inspiring characteristics. Some may refer to it as a “universal language” with the ability to transcend the barriers of speech, while others may describe its ability to evoke intense emotional experiences for the listener. Regardless of the description, it is a commonly held view that music can have many profound effects. Studies of music’s effects have found these beliefs to be more than pure conjecture, finding that music interacts with and changes our brains in physical and emotional ways. Music can even have clinical applications, such as music therapy. This type of therapy has been shown to be beneficial in many areas, ranging from stroke rehabilitation to mental health treatment. The mechanisms behind music’s therapeutic benefit has to do with neuroplastic effects; Being able to harness this benefit in a therapeutic setting could make treatments for mental disorders and brain injuries even more effective. This thesis aimed to discover whether musical thoughts could be interpreted using machine learning, potentially opening the door to the use of thought-based musical training for therapeutic benefit. For this study, EEG data was collected while people were thinking of 5 melodies, then machine learning models were trained on labeled datasets. The models were then tasked with categorizing unlabeled sets of EEG data - in other words, predicting which melody a subject was thinking of while the data was being recorded. The accuracy of the predictions ranged from 45% to 80%, which means that the programs were 2-4 times more accurate than random guessing. This shows that these programs could potentially be used to examine the effects of musical thinking on neuroplasticity. While this topic is still exploratory and requires more research, these results could lead to a promising future of development of music-based brain-computer interfaces.

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