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|Title:||Predicting sensorimotor responses to rhythmic events : an EEG study of neuronal entrainment & beat perception (cognizant through harmonics)||Authors:||Darmalingam, Kumaresh||Keywords:||Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Social sciences::Psychology::Consciousness and cognition
|Issue Date:||2021||Publisher:||Nanyang Technological University||Source:||Darmalingam, K. (2021). Predicting sensorimotor responses to rhythmic events : an EEG study of neuronal entrainment & beat perception (cognizant through harmonics). Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/151269||Abstract:||This research is motivated by developments in low cost BCIs and to inspire practical implementations. Such as, single electrode mobile EEGs leveraged in live settings by music listeners and performers. This research aims to provide guidance, to isolating electrode locations and frequency range selections for the broader non-academic community. I analysed the publicly available NMED-T dataset of EEG recordings and rhythmic tapping, respectively during music listening. By drawing from Neural Resonance Theory, Dynamic Attending Theory, Neural Entrainment, Beat Perception and MIR methodologies, I deployed a series of predictive models to identify the most competitive single electrode and frequency combination that links to future listening and rhythmic behaviour. The most competitive model used electrode measuring the premotor cortex in the sub-harmonic range of the beat frequency based on the music stimuli, to predict tapping rates of future listening to the same song (Precision=0.74, s=0.19). This suggests that future sensorimotor responses like tapping behaviour can be reliably predicted using responses to neural entrainment. The learnings will hopefully lower the entry barrier for developments within the music community. Findings also highlight that the most informative features were extracted at the sub-harmonics, encouraging research aiming to use entrainment to consider neural harmonics too.||URI:||https://hdl.handle.net/10356/151269||DOI:||10.32657/10356/151269||Rights:||This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Theses|
Updated on Oct 18, 2021
Updated on Oct 18, 2021
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