Sharif Digital Repository

Blind source separation is an important field of study in signal processing, in which the goal is to estimate source signals by having mixed observations. There are some conventional methods in this field that aim to estimate source signals by considering certain assumptions on sources. One of the most popular assumptions is the non-Gaussianity of sources which is the basis of many popular blind source separation methods. These methods may fail to estimate sources when the distribution of two or more sources is Gaussian. Hence, this study aims to introduce a new approach in blind source separation for nonlinear and chaotic signals by using a dynamical similarity measure and relaxing... 

Background: While decoding visual and auditory stimuli using recorded EEG signals has enjoyed significant attention in the past decades, decoding olfactory sensory input from EEG data remains a novelty. Recent interest in the brain's mechanisms of processing olfactory stimuli partly stems from the association of the olfactory system and its deficit with neurodegenerative diseases. New Methods: An olfactory stimulus decoder using features that represent nonlinear behavior content in the recorded EEG data has been introduced for classifying 4 olfactory stimuli in 5 healthy male subjects. Results: We show that by using nonlinear and chaotic features, a subject-specific classifier can be...