Grants Biomedical signal processing

Abstract
Human beings are a source of many signals that encode information related to the health status of the person, its intentions, the status of diverse biological process in his body, the information he is trying to communicate, etc. In general these signals are of nonlinear and nonstationary nature, and the information is hidden and coded in a complex way which is not clearly exposed in the raw signal. In many cases, the information conveyed in those signals can be interpreted rather easily by other human beings. For example, a listener can understand the meaning of a speech sentence and the emotional state of the speaker. As another example, an expert can determine the presence or absence of apnea events by watching a polysomnogram of a sleeping patient. Nevertheless, when one tries to replicate this characteristic in a computer, the nonlinear and nostationary characteristics pose a serious problem for an automatic algorithm.
Moreover, there are signals that carry interesting information that at the present day cannot be interpreted by other humans. For example, the electroencephalographic record obtaining at the same time a person is imagining to pronounce a word, carry information regarding the word that was imagined, but it is not possible for a human observer to extract this information.
In the last years several advanced signal processing methods have been developed which try to improve the capabilities of information extraction, and can be used to improve the behaviour of systems dedicated to detect its presence or absence in a specific signal. Some of these techniques are sparse representation, nonnegative matrix factorization, blind source separation, independent component analysis, regularization and inverse problems . In this line, these (and other) advanced signal processing method are developed and extended to address the problems found in specific signals applied to problems of speech recognition, speech denoising and dereverberation, detection of apneas and hypopneas, prediction of the presence of the sleep apnea-hypopneas syndrome, imagined speech recognition, brain -computer interfaces and other related applications.

Principal researchers:
Leandro Di Persia
Leonardo Rufiner

Current funding:
PIP 2014 CONICET
DST 2011 MINCyT

Past funding:
PIP 2012 CONICET
CAID 2011 UNL
PICT 2010 ANPCyT
CAID 2009 UNL
PAE-PID 2007 ANPCyT
PAE-PICT 2007 ANPCyT