Sample-based cross-frequency coupling analysis with CFAR detection

In this paper, we introduce a new approach for cross-frequency coupling analysis as applied to electroencephalograph (EEG) signals. Our approach consists of a low-complexity signal analysis block which is well-suited to implementation as an integrated circuit followed by constant false alarm rate (CFAR) detection - a strategy borrowed from the digital communications field. In addition to being very low in complexity, we demonstrate here that the proposed framework provides good detection performance while effectively rejecting false alarms. Compared to more conventional detection procedures that rely on the formation of surrogate distributions, the proposed approach is both lower in complexity and allows detection decisions to be accurately made using smaller time windows.