Please use this identifier to cite or link to this item:
Title: EEG-based emotion recognition using machine learning techniques
Authors: Lan, Zirui
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Lan, Z. (2018). EEG-based emotion recognition using machine learning techniques. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: Electroencephalography (EEG)-based emotion recognition attempts to detect the affective states of humans directly via spontaneous EEG signals, bypassing the peripheral nervous system. In this thesis, we explore various machine learning techniques for EEG-based emotion recognition, and focus on the three research gaps outlined as follows. 1. Stable feature selection for recalibration-less affective Brain-Computer Interfaces. 2. Cross-subject transfer learning for calibration-less affective Brain-Computer Interfaces. 3. Unsupervised feature learning for affective Brain-Computer Interfaces. We propose several novel methods in this thesis to address the three research gaps and validate our proposed methods by experiments. Extensive comparisons between our methods and other existing methods justify the advantages of our methods.
DOI: 10.32657/10220/46340
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
LanZirui_PhD_Thesis.pdfPhD Thesis6.6 MBAdobe PDFThumbnail

Google ScholarTM




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