A Parametric EEG Signal Model for BCIs with Rapid-Trial Sequences

Electroencephalogram (EEG) signals have been shown very effective for inferring user intents in brain-computer interface (BCI) applications. However, existing EEG-based BCIs, in many cases, lack sufficient performance due to utilizing classifiers that operate on EEG signals induced by individual trials. While many factors influence the classification performance, an important aspect that is often ignored is the temporal dependency of these trial-EEG signals, in some cases impacted by interference of brain responses to consecutive target and non-target trials. In this study, the EEG signals are analyzed in a parametric sequence-based fashion, which considers all trials that induce brain responses in a rapid-sequence fashion, including a mixture of consecutive target and non-target trials. EEG signals are described as a linear combination of time-shifted cortical source activities plus measurement noise. Using a superposition of time invariant with an auto-regressive (AR) process, EEG signals are treated as a linear combination of a stationary Gaussian process and time-locked impulse responses to the stimulus (input events) onsets. The model performance is assessed in the framework of a rapid serial visualization presentation (RSVP) based typing task for three healthy subjects across two sessions. Signal modeling in this fashion yields promising performance outcomes considering a single EEG channel to estimate the user intent.