Finding consistencies in MEG responses to repeated natural speech

Abstract

The first steps in the attempts to unravel the perception of natural speech and to continuously follow the listener's brain activity, are to find and characterize the perception-related phenomena and the relevant features in measured signals. In this paper, the problem was tackled by searching for consistencies in single-trial magnetoencephalography (MEG) responses to repeated 49-s audiobook passage. The canonical correlation analysis (CCA) based modeling was applied to find the maximally correlating signal projections across the single-trial responses. Using the trained model and separate test trials, projected MEG time series showed consistent fluctuations in frequencies typically below 10 Hz, with cross-trial correlations up to 0.25 (median). These statistically significant correlations between test trial projections suggest that the proposed method can extract perception-related time series from long-lasting MEG responses to natural speech. © 2012 Springer-Verlag.