An affective BCI system with music in an immersive environment

This study investigates the effectiveness of generated music in a Virtual reality (VR) immersive environment in inducing different emotional arousal states in the context of alleviating mood disorders. It details the collection and labelling of EEG data from 20 participants which is evaluated using...

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Main Author: Khendry, Nishka
Other Authors: Guan Cuntai
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/156478
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1564782022-04-17T12:07:28Z An affective BCI system with music in an immersive environment Khendry, Nishka Guan Cuntai School of Computer Science and Engineering CTGuan@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Computer science and engineering::Computer applications::Life and medical sciences Science::Medicine::Biomedical engineering This study investigates the effectiveness of generated music in a Virtual reality (VR) immersive environment in inducing different emotional arousal states in the context of alleviating mood disorders. It details the collection and labelling of EEG data from 20 participants which is evaluated using two state-of-the-art EEG emotion classification models – TSception and EEGNet. This report outlines the end-to-end implementation of a novel data recording system combining Python-based music generation, VR development with Unity, and EEG data streaming and labelling. It also highlights the overall system design considerations and experiment protocols administered. Given a fixed high valence value, the labelled EEG data recorded was used for offline model training to classify three emotional arousal states – low, high, and neutral. High classification accuracies were reported for Low-High arousal classification - 81.57% for TSception and 83.45% for EEGNet. Therefore, it can be concluded that the EEG data collected contained distinctive emotional states. This system, combining the effect of VR and music, is effective in inducing emotional arousal states and can be explored further in clinical trials as a potential tool for emotion modulation in alleviating mood disorders. Bachelor of Engineering (Computer Science) 2022-04-17T12:07:27Z 2022-04-17T12:07:27Z 2022 Final Year Project (FYP) Khendry, N. (2022). An affective BCI system with music in an immersive environment. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156478 https://hdl.handle.net/10356/156478 en SCSE21-0028 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Computer science and engineering::Computer applications::Life and medical sciences
Science::Medicine::Biomedical engineering
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Computer science and engineering::Computer applications::Life and medical sciences
Science::Medicine::Biomedical engineering
Khendry, Nishka
An affective BCI system with music in an immersive environment
description This study investigates the effectiveness of generated music in a Virtual reality (VR) immersive environment in inducing different emotional arousal states in the context of alleviating mood disorders. It details the collection and labelling of EEG data from 20 participants which is evaluated using two state-of-the-art EEG emotion classification models – TSception and EEGNet. This report outlines the end-to-end implementation of a novel data recording system combining Python-based music generation, VR development with Unity, and EEG data streaming and labelling. It also highlights the overall system design considerations and experiment protocols administered. Given a fixed high valence value, the labelled EEG data recorded was used for offline model training to classify three emotional arousal states – low, high, and neutral. High classification accuracies were reported for Low-High arousal classification - 81.57% for TSception and 83.45% for EEGNet. Therefore, it can be concluded that the EEG data collected contained distinctive emotional states. This system, combining the effect of VR and music, is effective in inducing emotional arousal states and can be explored further in clinical trials as a potential tool for emotion modulation in alleviating mood disorders.
author2 Guan Cuntai
author_facet Guan Cuntai
Khendry, Nishka
format Final Year Project
author Khendry, Nishka
author_sort Khendry, Nishka
title An affective BCI system with music in an immersive environment
title_short An affective BCI system with music in an immersive environment
title_full An affective BCI system with music in an immersive environment
title_fullStr An affective BCI system with music in an immersive environment
title_full_unstemmed An affective BCI system with music in an immersive environment
title_sort affective bci system with music in an immersive environment
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/156478
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