Development of an Optical Brain-Computer Interface Using Dynamic Topographical Pattern Classification.

详细信息   

• 作者：Schudlo ; Larissa Christina.
• 学历：Master
• 年：2012
• 导师：Chau, Tom,eadvisor
• 毕业院校：University of Toronto
• ISBN：9780494930113
• CBH：MR93011
• Country：Canada
• 语种：English
• FileSize：5483930
• Pages：103

文摘

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.