  The electromyogram (EMG) is an important tool for assessing the activity of a
muscle and thus also a valuable measure for the diagnosis and control of
respiratory support. In this article we propose convolutive blind source
separation (BSS) as an effective tool to pre-process surface electromyogram
(sEMG) data of the human respiratory muscles. Specifically, the problem of
discriminating between inspiratory, expiratory and cardiac muscle activity is
addressed, which currently poses a major obstacle for the clinical use of sEMG
for adaptive ventilation control. It is shown that using the investigated
broadband algorithm, a clear separation of these components can be achieved.
The algorithm is based on a generic framework for BSS that utilizes multiple
statistical signal characteristics. Apart from a four-channel FIR structure,
there are no further restrictive assumptions on the demixing system.
