Main Page
Deanship
The Dean
Dean's Word
Curriculum Vitae
Contact the Dean
Vision and Mission
Organizational Structure
Vice- Deanship
Vice- Dean
KAU Graduate Studies
Research Services & Courses
Research Services Unit
Important Research for Society
Deanship's Services
FAQs
Research
Staff Directory
Files
Favorite Websites
Deanship Access Map
Graduate Studies Awards
Deanship's Staff
Staff Directory
Files
Researches
Contact us
عربي
English
About
Admission
Academic
Research and Innovations
University Life
E-Services
Search
Deanship of Graduate Studies
Document Details
Document Type
:
Thesis
Document Title
:
TRANSFER LEARNING ALGORITHM FOR LOW-COUNT TRAINING TRIALS WITH DEEP LEARNING IN ELECTROENCEPHALOGRAPHY
خوارزمية نقل التعلم لتجارب التدريب المنخفضة مع التعلم العميق على الانفعال الكهربائي للدماغ
Subject
:
faculty of Engineering
Document Language
:
Arabic
Abstract
:
The field of Artificial intelligence is rapidly evolving in daily basis and the fact of that brain computer interface (BCI) enables the computer to imitates the human brain processes. There are multiple types of algorithms but in this thesis will be concern about Neural Network and Transfer Learning. A new transfer learning methodology were proposed in the field of Electroencephalography (EEG) signal processing in order to study the performance. This thesis proposed approaches to transfer learning from four labels in which the source class is different from the target class in terms of valence, arousal, dominance and liking labels. This is achieved by two approaches, using Discrete Wavelet Transform (DWT) and the other way using Power Spectral Density (PSD) as feature extraction for EEG signals from Database for Emotion Analysis using Physiological Signals (DEAP). Transfer Learning used as a classifier which result with good performance accuracies are from 54.5% to 65.5% in case of using DWT and 53.6% to 62.4% in case of using PSD. Moreover, Cross-validation were used to ensure the validity of data by splitting in three categories train, validation and test data and by this there was no replications.
Supervisor
:
Dr. Hatem Rmili
Thesis Type
:
Master Thesis
Publishing Year
:
1442 AH
2020 AD
Co-Supervisor
:
Dr. Mohammed Abdulaal
Added Date
:
Thursday, August 27, 2020
Researchers
Researcher Name (Arabic)
Researcher Name (English)
Researcher Type
Dr Grade
Email
ماهر عبدالرحمن الجهني
Aljohani, Maher Abdulrahman
Researcher
Master
Files
File Name
Type
Description
46705.pdf
pdf
Back To Researches Page