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. The goal of the present study is to distinguish between different levels of game difficulty using non-invasive brain activity measurement with functional near-infrared spectroscopy (fNIRS). The study is designed to lead to adaptive interfaces that respond to the user's brain activity in real time. Nine subjects played two levels of the game Pacman while their brain activity was measured using fNIRS. Statistical analysis and machine learning classification results show that we can discriminate well between subjects playing or resting, and distinguish between the two levels of difficulty with some success. In contrast to most previous fNIRS studies which only distinguish brain activity from rest, we attempt to tell apart two levels of brain activity, and our results show potential for using fNIRS in an adaptive game or user interface.

Additional Metadata
Keywords Brain-computer interface, Difficulty level, FNIRS, Functional near-infrared spectroscopy, Game, Human cognition, Task classification
Persistent URL dx.doi.org/10.1007/978-3-642-03655-2_50
Series Lecture Notes in Computer Science
Citation
Girouard, A, Solovey, E.T. (Erin Treacy), Hirshfield, L.M. (Leanne M.), Chauncey, K. (Krysta), Sassaroli, A. (Angelo), Fantini, S. (Sergio), & Jacob, R.J.K. (Robert J. K.). (2009). Distinguishing difficulty levels with non-invasive brain activity measurements. In Lecture Notes in Computer Science. doi:10.1007/978-3-642-03655-2_50