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.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/30537 |
| Date | 05 December 2011 |
| Creators | Chan, Justin |
| Contributors | Chau, Tom |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0018 seconds