Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/72792
Title: Real-time classification and recognition of EEG/EMG signals for BCI
Authors: Lee, Vic Son
Keywords: DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences
Issue Date: 2017
Abstract: Development in Brain-Computer Interfaces (BCI) has evolved tremendously in recent years due to the improvement in EEG techniques and the improvement of EEG-capturing technology. The availability of low-cost boards such as the OpenBCI Ganglion vastly increases the amount of people who can develop new systems based on BCI technologies, as well as the people who benefit from such technologies – such as the physically impaired. The aim of this project is to develop a portable, flexible and practical system to distinguish between various EEG and EMG signals in real-time, with reasonably high accuracies. The input signals chosen for the system include motor imagery, physical motor movements, eye blinks, and jaw clenches. A Python GUI application was created to perform various functionalities required by the system, such as: data capture, loading and saving of data, the processing and feature extraction of the signal, prediction of input, and a maze game to demonstrate the prediction outputs. The data is processed via baseline removal, Fast Fourier Transform (FFT), and signal power of frequency bands. The machine learning algorithm used is XGBoost, a tree ensemble algorithm. The performance of the system using the various signal processing operations were discussed, and the results have been presented to show the system’s accuracy.
URI: http://hdl.handle.net/10356/72792
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP Report.pdf
  Restricted Access
FYP Report1.17 MBAdobe PDFView/Open

Page view(s) 50

154
checked on Oct 20, 2020

Download(s) 50

27
checked on Oct 20, 2020

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.