Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/157285
Title: | EEG recognition for music generation | Authors: | Pham, Thien Tan | Keywords: | Engineering::Electrical and electronic engineering | Issue Date: | 2022 | Publisher: | Nanyang Technological University | Source: | Pham, T. T. (2022). EEG recognition for music generation. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157285 | Project: | A3273-211 | Abstract: | With the rise of the metaverse industry, research has been conducted to develop the user experience when playing games in augmented reality (AR) and virtual reality (VR). Many companies have attached Electroencephalography (EEG) sensors to AR/VR headsets to understand how users feel when playing games. The game content developers can understand the user experience and design a better gaming environment by processing and analyzing the EEG signals. Besides gaming content and lighting effect, the sound effect plays a significant role in inducing feelings for gamers. The user experience can be stimulated or changed to align with the game designer's intention by changing the music melody. This paper proposes a method of generating music that can induce the desired emotion based on the user's feelings. After understanding the user's emotion for a piece of a specific song, the system will generate and add more notes to that piece of music such that the modified music will sound happy and sad or neutral based on the user's feelings. | URI: | https://hdl.handle.net/10356/157285 | Fulltext Permission: | restricted | Fulltext Availability: | With Fulltext |
Appears in Collections: | EEE Student Reports (FYP/IA/PA/PI) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
PHAM THIEN TAN - FYP report.pdf Restricted Access | FYP | 2.04 MB | Adobe PDF | View/Open |
exciting_version.mp3 Restricted Access | exciting music version | 684.11 kB | Unknown | View/Open |
sad_version.mp3 Restricted Access | sad music version | 668.6 kB | Unknown | View/Open |
original_version.mp3 Restricted Access | original music | 680.85 kB | Unknown | View/Open |
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