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Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/30537

Title: Online Near-infrared Spectroscopy Brain-computer Interfaces with Real-time Feedback
Authors: Chan, Justin
Advisor: Chau, Tom
Department: Electrical and Computer Engineering
Keywords: near-infrared spectroscopy
brain-computer interface
neurofeedback
Issue Date: 5-Dec-2011
Abstract: Near-infrared spectroscopy (NIRS) is an emerging non-invasive brain-computer interface (BCI) modality that measures changes in hemoglobin concentrations in neurocortical tissue. Previous NIRS studies have not employed real-time feedback with online classification, a combination which would allow users to alter their mental strategy on the fly. This thesis reports the results of two online studies. The first study contrasted online classification of prefrontal hemodynamics using an artificial neural network (ANN) and a hidden Markov model-based (HMM) classifier. The second study measured the accuracy of an online linear discriminant classifier. In study 1, only the ANN classifier facilitated online classification rates greater than chance (p=0.0289). In study 2, a new feedback system and experimental protocol led to improved classification rates over those of the first study (p=5.1*10^(-5)). While control over instantaneously generated feedback in online NIRS-BCIs has been demonstrated, factors such as user frustration, mental fatigue, and restrictions on ambient lighting may compromise performance.
URI: http://hdl.handle.net/1807/30537
Appears in Collections:Master

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