An Event-driven AR-process Model for EEG-based BCIs with Rapid Trial Sequences.

Electroencephalography (EEG) is an effective noninvasive measurement method to infer user intent in braincomputer interface (BCI) systems for control and communication, however these systems often lack sufficient accuracy and speed due to low separability of class-conditional EEG feature distributions. Many factors impact system performance, including inadequate training datasets and models' ignorance of the temporal dependency of brain responses to serial stimuli. Here, we propose a signal model for event-related responses in EEG evoked with a rapid sequence of stimuli in BCI applications. The model describes EEG as a superposition of impulse responses time-locked to stimuli corrupted with an autoregressive noise process. The performance of the signal model is assessed in the context of RSVP Keyboard, a language-model-assisted EEGbased BCI for typing. EEG data obtained for model calibration from 10 healthy participants is used to fit and compare two models: the proposed sequence-based EEG model and the trialbased feature-class-conditional distribution model that ignores temporal dependencies, which has been used in previous work. Simulation studies indicate that the earlier model that ignores temporal dependencies may be causing drastic reductions in achievable information transfer rate (ITR). Furthermore, the proposed model, with better regularization, may achieve improved accuracy with fewer calibration data samples, potentially helping to reduce calibration time. Specifically, results show an average 8.6% increase in (cross-validated) calibration AUC for a single channel of EEG, and 54% increase in ITR in a typing task.