Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/138661
Title: APPLICATION OF DEEP LEARNING METHODS IN BRAIN-COMPUTER INTERFACE SYSTEMS
Authors: SIAVASH SAKHAVI
ORCID iD:   orcid.org/0000-0001-6401-141X
Keywords: Brain Computer Interface, Deep Learning, Machine Learning, EEG, Convolutional Neural Network, Deep Neural Networks
Issue Date: 16-Aug-2017
Citation: SIAVASH SAKHAVI (2017-08-16). APPLICATION OF DEEP LEARNING METHODS IN BRAIN-COMPUTER INTERFACE SYSTEMS. ScholarBank@NUS Repository.
Abstract: Deep learning, has produced many successful methods and architectures. Some of which are currently considered state-of-the-art in the areas of image classification and natural language processing. This thesis focuses on developing deep learning methods to the area of EEG classification. First, we propose a classification framework by introducing a new representation of the EEG data from extending the FBCSP method and utilizing a convolutional neural network. Our framework outperforms the state-of-the-art on a four-class motor-imagery dataset by a significant seven percent increase in accuracy. We have also analyzed and visualized the network for a more in-depth understanding. Second, we extend the application of deep learning to transfer learning in brain-computer interfaces by training a model on multiple subjects. The classification accuracy results produced in this thesis are stunningly higher relative to simple machine learning algorithms.
URI: http://scholarbank.nus.edu.sg/handle/10635/138661
Appears in Collections:Ph.D Theses (Open)

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