Welcome To UTPedia

We would like to introduce you, the new knowledge repository product called UTPedia. The UTP Electronic and Digital Intellectual Asset. It stores digitized version of thesis, dissertation, final year project reports and past year examination questions.

Browse content of UTPedia using Year, Subject, Department and Author and Search for required document using Searching facilities included in UTPedia. UTPedia with full text are accessible for all registered users, whereas only the physical information and metadata can be retrieved by public users. UTPedia collaborating and connecting peoples with university’s intellectual works from anywhere.

Disclaimer - Universiti Teknologi PETRONAS shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.Best viewed using Mozilla Firefox 3 or IE 7 with resolution 1024 x 768.

Classification of Grasp-and-Lift EEG using GoogLeNet

Ong , Zhong Yi (2019) Classification of Grasp-and-Lift EEG using GoogLeNet. IRC, Universiti Teknologi PETRONAS. (Submitted)

[img] PDF
Restricted to Registered users only

Download (1501Kb)

Abstract

Grasp-and-Lift (GAL) action is the hand movement of lifting an object for a few seconds and action complete with putting the object back to its original position. In fact, EEG signal is one of the common ways to understand the relationship between brain and GAL action. As the relationship between human brain activity and EEG signal is yet to be fully understood, more research work needs to be carried out. Realizing the lack of low cost and practical prosthetic device for patients suffering from neurological disease and the fact that low classification accuracy due to numerous events and low Signal to Noise ratio (SNR), GAL EEG signal processing will be giving huge impact to the development of prosthetic device by providing input to Brain-Computer Interface device. As such, this research presents a Convolutional Neural Network (CNN)-based deep learning method to classify EEG signals into 6 GAL classes. The main objective of this research is to develop EEG GAL events classification based on pretrained CNN followed by performance evaluation in term of accuracy, sensitivity and specificity. 6 electrodes corresponding to motor movement including electrode C3, CZ, C4, P3, PZ, P4 were selected during pre- processing phase. One-versus-rest scheme and two class Common Spatial Pattern (CSP) filter were used to maximize variance difference between two classes. Extracted CSP features from each electrode were converted into grayscale scalogram using sliding window method followed by concatenating 3 grayscale scalogram forming RGB scalogram. One classifier was trained per class. The classification accuracy can be computed by inputting test data into trained network. Based on result obtained, average testing accuracy, specificity and sensitivity among 6 classes are 93.85%,96.5% and 91% respectively.

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Pervasisve Systems - Microelectronics - Device Modelling
Subject: UNSPECIFIED
Divisions: Engineering > Electrical and Electronic
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 20 Dec 2019 16:12
Last Modified: 20 Dec 2019 16:12
URI: http://utpedia.utp.edu.my/id/eprint/20200

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...