Support Vector machines (SVM) for functional Magnetic Resonance Imaging (fMRI)

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive method to detect brain activity, and was used in a single-subject experiment to measure activity for acoustic stimulation. In the project, we investigated whether activity of human auditory cortex for different acoustic stimuli gives reliable information to discriminate the different stimuli. As stimuli, tones of four different frequencies were presented to the subject. To detect a possibly fine grained activity pattern specific for each frequency, we applied the multivariate machine learning algorithm SVM, that after a learning phase, predicts the frequency of a given observation (fMRI measurements during a frequency presentation). An above chance level detection rate of 65 % suggests a tonotopic organization of human auditory cortex.