Dynamic Neural Correlates of Perceiving and Imagining Speech

Recent studies have suggested that correlated neural signals generated by speech processing networks can be identified from brain electromagnetic recordings. We studied the dynamic property of 40-Hz gamma band steady-state auditory responses evoked by speech and non-speech stimuli, and found that these two categories of signals are processed differentially over the left and right auditory cortex. We applied an envelope-based Hilbert-Huang decomposition of the data, and extracted signals from a functional network that is only correlated with speech stimuli. We believe these are evidences that speech signals are preferentially processed at a longer (syllabic) time scales than that of non-speech (phonetic). In a separate study, the envelope correlated neural signals have been used to successfully classify the perceived and imagined syllabic rhythms from EEG. We then developed a new method of geometrically accurate spline surface Laplacian (SSL) to estimate the radial current source density of the signal. Numerical simulations and real EEG data have shown that the new method is more accurate than traditional spherical SS. In a third experiment we recorded EEG when subjects were asked to listen or imagine a small set of selected sentences. We apply the new SSL to estimate the anatomical origin of the signal that tracks the internal copy of imagined speech, and apply a fast wavelet transform technique to counter the lag and compression uncertainty of imagined speech.