Detection of periodic signals in brain echo-planar functional images
In this paper, we propose to use signal autocorrelation values at specially selected delays in order to construct brain periodic activity images. In comparison to the usual approaches, our method requires very little a priori information about the real signal. We applied this method to a well-known vision paradigm. The images thus obtained revealed that the autocorrelation peak method discriminates better expected activated areas from background than the classical method based on correlation with an artificial waveform which may induce false detection.