Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/118195
Title: Modelling and Classification of Motor Imagery EEG for BCI
Authors: LI XINYANG
Keywords: EEG, motor imagery, brain computer interface, feature extraction, signal processing, classification
Issue Date: 14-Aug-2014
Citation: LI XINYANG (2014-08-14). Modelling and Classification of Motor Imagery EEG for BCI. ScholarBank@NUS Repository.
Abstract: Discriminative analysis of EEG signals is crucial for the usability and reliability of brain computer interface (BCI) systems based on motor imagery EEG. This thesis has studied feature extraction for motor imagery EEG classification in BCI from the perspectives of model generalization and adaptation. In the presence of causal relationships in EEG signals, a computational model has been constructed for discriminative learning of propagation and spatial patterns. Ensemble learning of spatial filters has been proposed to improve the model generalization by considering the data-model mismatch. This mismatch has been investigated for model adaption to address the issue of significant signal nonstationarity. The relationship between the shift of discriminative subspaces and that in feature spaces has been analyzed. Experimental studies have been conducted to validate the proposed methods.
URI: http://scholarbank.nus.edu.sg/handle/10635/118195
Appears in Collections:Ph.D Theses (Open)

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