Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/72775
Title: Visual target selection using brain signals acquired using Electroencephalography (EEG)
Authors: Oh, Yoke Chew
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2017
Abstract: Past studies had shown promising results of Electroencephalography(EEG) based BCI as tool for communication and control. Several channels of BCI such as motor imagery, steady state visual evoked potential and visual spatial attention had been proposed. In this project, the goal was to demonstrate EEG-based control to a robotic device (SPHERO) using a covert visual spatial attention task. Specifically, it used the alpha and beta band neural oscillations observed during the task as input features for training of model. Band power of those neural oscillation were used to train a Linear Discriminant Analysis model. Average validation accuracy of 72.5% and average test accuracy of 60.9% were obtained from five subjects. A decreased in accuracy was attributed to feature shift due to non-stationarity of features and increasing fatigue level. Future designs for BCI could explore ways to minimised fatigue induced during the course of experiment and adapt the classifier to account for feature shifts
URI: http://hdl.handle.net/10356/72775
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 
Oh_Yoke_Chew_FYP_Report.pdf
  Restricted Access
1.27 MBAdobe PDFView/Open

Page view(s)

126
Updated on Dec 5, 2020

Download(s)

28
Updated on Dec 5, 2020

Google ScholarTM

Check

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