Title: Bayesian inferences on neural activity in EEG-based brain-computer interface
Authors: Jian Kang - University of Michigan (United States) [presenting]
Abstract: A brain-computer interface (BCI) is a system that uses brain activity to control or communicate with technology. In particular, BCIs help people with disabilities use technology for communication. A common design for an electroencephalogram (EEG) BCI relies on the classification of the P300 event-related potential (ERP), which is a response elicited by the rare occurrence of target stimuli among common non-target stimuli. The existing studies have focused on constructing the ERP classifiers, but few provide insights into the underlying mechanism of the neural activity. To this end, we perform a novel Bayesian analysis of the probability distribution of the multi-channel EEG signals from real participants under the P300 ERP-BCI design. In contrast to classification, our goal is to identify relevant spatial-temporal differences of the neural activity in response to different external stimuli, which provides statistical evidence of P300 waveforms and facilitates designing user-specific profiles for efficient brain-computer communications. We also perform sensitivity and reproducibility analyses, make cross-participant comparisons, and design simulation studies to show the robustness of our analysis.