This provides the MATLAB based source code for fMRI data processing on the auditory segregation experiment using SPM12 package. A critical aspect of auditory scene analysis is the ability to extract a sound of relevance (figure) from a background of competing sounds (ground) such as when we hear a speaker in a cafe. This is formally known as auditory figure-ground segregation. This is colloquially known as "cocktail party problem". To understand how the brain segregates overlapping sounds, I employed non-invasive functional magnetic resonance imaging (fMRI) and presented stochastic figure ground (SFG) artificial sounds to awake passively listening rhesus macaques (macaca mulatta) that were trained to perform visual fixation for fluid reward. EPI images were acquired using a sparse acquisition protocol on 4.7T upright Bruker scanner (TR/TA/TE = 10s/2.011s/21ms) while the animal performed a stimulus irrelevant visual fixation task. 360 volumes (135 each for figure & control) were acquired per session per animal. Analysis was carried out using SPM software (SPM12). Single subject inference was carried out by applying a generalized linear model (GLM). I showed that monkeys use similar regions of their auditory brain as humans to separate overlapping sounds. This has now paved the way for recording from single cells in the monkey brain which will enable us to understand how the brain solves the cocktail party problem. If you use this code please cite the following paper:  Felix Schneider, Pradeep Dheerendra, Fabien Balezeau, Michael Ortiz-Rios, Yukiko Kikuchi, Christopher I. Petkov, Alexander Thiele, and Timothy D. Griffiths. "Auditory figure-ground analysis in rostral belt and parabelt of the macaque monkey." Scientific reports 8, no. 1 (2018): 1-8. https://doi.org/10.1038/s41598-018-36903-1
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