Predicting future brain signal is highly sought-after, yet difficult to achieve.
To predict the future phase of cortical activity at localized ECoG and MEG
recording sites, we exploit its predominant, large-scale, spatiotemporal
dynamics. The dynamics are extracted from the brain signal through Fourier
analysis and principal components analysis (PCA) only, and cast in a data model
that predicts future signal at each site and frequency of interest. The dominant
eigenvectors of the PCA that map the large-scale patterns of past cortical phase
to future ones take the form of smoothly propagating waves over the entire
measurement array. In ECoG data from 3 subjects and MEG data from 20 subjects
collected during a self-initiated motor task, mean phase prediction errors were
as low as 0.5 radians at local sites, surpassing state-of-the-art methods of
within-time-series or event-related models. Prediction accuracy was highest in
delta to beta bands, depending on the subject, was more accurate during episodes
of high global power, but was not strongly dependent on the time-course of the
task. Prediction results did not require past data from the to-be-predicted
site. Rather, best accuracy depended on the availability in the model of long
wavelength information. The utility of large-scale, low spatial frequency
traveling waves in predicting future phase activity at local sites allows
estimation of the error introduced by failing to account for irreducible
trajectories in the activity dynamics.