Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/74601
Title: EEG-based mental workload recognition using deep learning techniques
Authors: Koh, Charis Hwee Ying
Keywords: DRNTU::Engineering
DRNTU::Engineering::Electrical and electronic engineering
DRNTU::Social sciences::Psychology::Consciousness and cognition
Issue Date: 2018
Abstract: As EEG devices become more widely available in the market, they have seen increased usage in observing and tracking the brainwaves of operators. Furthermore, with their existing use in the healthcare sector, there is a need to improve the recognition of features for safety and wellbeing. This paper aims to study existing mental workload recognition techniques, as well as to demonstrate an implemented MATLAB code to execute transfer learning between two datasets and its related results. The paper also aims to compare the effectiveness of transfer learning against machine learning, and to propose suggestions for future development. In this study, results have found that while transfer learning can be applied to EEG data, much improvement has to be made before the algorithm can be used industrially or commercially.
URI: http://hdl.handle.net/10356/74601
Schools: School of Electrical and Electronic Engineering 
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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