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Online Near-infrared Spectroscopy Brain-computer Interfaces with Real-time Feedback

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.

Identiferoai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/30537
Date05 December 2011
CreatorsChan, Justin
ContributorsChau, Tom
Source SetsUniversity of Toronto
Languageen_ca
Detected LanguageEnglish
TypeThesis

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