Estimating neural sources from each time-frequency component of magnetoencephalographic data

We have developed a method that incorporates the time-frequency characteris tics of neural sources into magnetoencephalographic (MEG) source estimation . This method, referred to as the time-frequency multiple-signal-classifica tion algorithm, allows the locations of neural sources to be estimated from any time-frequency region of interest. In this paper, we formulate the met hod based on the most general form of the quadratic time-frequency represen tations. We then apply it to two kinds of nonstationary MEG data: gamma-ban d (frequency range between 30-100 Hz) auditory activity data and spontaneou s MEG data. Our method successfully detected the gamma-band source slightly medial to the Nlm source location. The method was able to selectively loca lize sources for alpha-rhythm bursts at different locations. It also detect ed the mu-rhythm source from the alpha-rhythm-dominant MEG data that was me asured with the subject's eyes closed. The results of these applications va lidate the effectiveness of the time-frequency MUSIC algorithm for selectiv ely localizing sources having different time-frequency signatures.