Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/149797
Title: Signal processing and machine learning for recognizing EEG signals of brain-computer interface
Authors: Yuan, Xinyu
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Yuan, X. (2021). Signal processing and machine learning for recognizing EEG signals of brain-computer interface. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149797
Project: P3041-192
Abstract: The human brain contains 86 billion nerve cells, the interaction activity of which makes human think and feel. Electroencephalography (EEG) is a physiological method to record brain-generated electrical activity through placing electrodes on the scalp surface. Brain-Computer interface, a device consists of electrodes, allow human to interact with computer by EEG measuring. Due to EEG signals high signal-to-noise ratio property, machine learning algorithm was applied for better features of interest extraction. This project aims to use machine learning approaches to achieve better EEG signal classification on human emotion with help of suitable feature extraction methods.
URI: https://hdl.handle.net/10356/149797
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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