Passive brain-computer interfaces are designed to use brain activity as an additional input, allowing the adaptation of the interface in real time according to the user's mental state. While most current brain computer interface research (BCI) is designed for direct use with disabled users, I focus my research on passive BCIs for healthy users. The goal of my dissertation is to employ functional near-infrared spectroscopy (fNIRS), a non-invasive brain measurement device, to augment an interface so it uses brain activity measures as an additional input channel. I have measured and classified brain signals that are interesting in HCI context, such as mental workload and difficulty level of a task. My future work will focus on creating an interface that responds to one of those measures by adapting the interface. By combining brain signal measured with an adaptive interface I expect to contribute a functional passive brain-computer interface that measures and adapts to the user's brain signal.

Additional Metadata
Keywords Brain-computer interface, FNIRS, Functional near-infrared spectroscopy, Human cognition, Task classification
Persistent URL dx.doi.org/10.1145/1520340.1520436
Conference 27th International Conference Extended Abstracts on Human Factors in Computing Systems, CHI 2009
Citation
Girouard, A. (2009). Adaptive brain-computer interface. In Conference on Human Factors in Computing Systems - Proceedings (pp. 3097–3100). doi:10.1145/1520340.1520436