  Human brain connectivity can be mapped by single pulse electrical stimulation
during intracranial EEG measurements. The raw cortico-cortical evoked
potentials (CCEP) are often contaminated by noise. Common average referencing
(CAR) removes common noise and preserves response shapes but can introduce bias
from responsive channels. We address this issue with an adjusted, adaptive CAR
algorithm termed "CAR by Least Anticorrelation (CARLA)".
  CARLA was tested on simulated CCEP data and real CCEP data collected from
four human participants. In CARLA, the channels are ordered by increasing mean
cross-trial covariance, and iteratively added to the common average until
anticorrelation between any single channel and all re-referenced channels
reaches a minimum, as a measure of shared noise.
  We simulated CCEP data with true responses in 0 to 45 of 50 total channels.
We quantified CARLA's error and found that it erroneously included 0 (median)
truly responsive channels in the common average with less than or equal to 42
responsive channels, and erroneously excluded less than or equal to 2.5
(median) unresponsive channels at all responsiveness levels. On real CCEP data,
signal quality was quantified with the mean R-squared between all pairs of
channels, which represents inter-channel dependency and is low for
well-referenced data. CARLA re-referencing produced significantly lower mean
R-squared than standard CAR, CAR using a fixed bottom quartile of channels by
covariance, and no re-referencing.
  CARLA minimizes bias in re-referenced CCEP data by adaptively selecting the
optimal subset of non-responsive channels. It showed high specificity and
sensitivity on simulated CCEP data and lowered inter-channel dependency
compared to CAR on real CCEP data.
