We investigated an emerging brain-computer interface (BCI) modality, namely, transcranial Doppler ultrasonography (TCD), which measures cerebral blood flow velocity.
We hypothesized that a bilateral TCD-driven online BCI would be able to dichotomously classify a user’s intentions with at least 70% accuracy. To test this hypothesis, we had three objectives: (1) to develop a signal classifier that yielded high (>80%) offline accuracies; (2) to develop an online TCD-BCI system with an onscreen keyboard; and, (3) to determine the achievable online accuracy with able-bodied participants.
With a weighted, forward feature selection and a Naïve Bayes classifier, sensitivity and specificity of 81.44 ± 8.35% and 82.30 ± 7.39%, respectively, were achieved in the
online differentiation of two mental tasks. The average information transfer rate and throughput of the system were 0.87 bits/min and 0.35 ± 0.18 characters/min, respectively. These promising online results encourage future testing of TCD-BCI systems with the target population.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/43093 |
| Date | 05 December 2013 |
| Creators | Lu, Jie |
| Contributors | Chau, Tom |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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