Near-infrared spectroscopy (NIRS) in an imaging technique that has gained much attention in brain-computer interfaces (BCIs). Previous NIRS-BCI studies have primarily employed temporal features, derived from the time course of hemodynamic activity, despite potential value contained in the spatial attributes of a response. In an initial offline study, we investigated the value of using joint spatial-temporal pattern classification with dynamic NIR topograms to differentiate intentional cortical activation from rest. With the inclusion of spatiotemporal features, we demonstrated a significant increase in achievable classification accuracies from those obtained using temporal features alone (p < 10-4). In a second study, we evaluated the feasibility of implementing joint spatial-temporal pattern classification in an online system. We developed an online system-paced NIRS-BCI, and were able to differentiate two cortical states with high accuracy (77.4±10.5%). Collectively, these findings demonstrate the value of including spatiotemporal features in the classification of functional NIRS data for BCI applications.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/33521 |
| Date | 26 November 2012 |
| Creators | Schudlo, Larissa Christina |
| Contributors | Chau, Tom |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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