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Data clustering has been widely used in data analysis and classification. In the present work, a method based on dynamic quantum clustering is proposed for the segmentation and analysis of brain tumor MRI. The results open the possibility of applications to multi modality medical imaging.

Measuring human capabilities to synchronize in time, adapt to perturbations to timing sequences or reproduce time intervals often require experimental setups that allow recording response times with millisecond precision. Most setups present auditory stimuli using either MIDI devices or specialized hardware such as Arduino and are often expensive or require calibration and advanced programming skills. Here, we present in detail an experimental setup that only requires an external sound card and minor electronic skills, works on a conventional PC, is cheaper than alternatives and requires almost no programming skills. It is intended for presenting any auditory stimuli and recording tapping response times with within 2 milliseconds precision (up to -2ms lag). This paper shows why desired accuracy in recording response times against auditory stimuli is difficult to achieve in conventional computer setups, presents an experimental setup to overcome this and explains in detail how to set it up and use the provided code. Finally, the code for analyzing the recorded tapping responses was evaluated, showing that no spurious or missing events were found in 94% of the analyzed recordings.