We are running a PsPM workshop a the Meeting of the German Psychological Society/Section Biological Psychology (Psychologie & Gehirn). Information and registration can be found here.
PsPM 4.0.1 contains minor bug fixes.
PsPM 4.0 released – including startle eye blink EMG modelling and pre-processing, and many improvements for data import and pre-processing. Thanks to everybody involved, and in particular Tobias Moser!
PsPM 3.1 is released. This is the first version that truly deserves the name “Psychophysiological Modelling” and comprises model-based methods for analysis of heart rate data, respiration data, and pupil size. Furthermore, it includes convenience functions for these data and for startle eye-blink EMG analysis. Many minor improvements, including more generous handling of missing values, support for Philips Scanphyslog files, and for bioread-converted AcqKnowledge files. Tell us whether you like it!
PsPM 3.0.2 contains minor bug fixes.
PsPM 3.0.1 is released. It provides platform-independent import of WDQ-files, EDF import, convenience functions for respiration channels, z-scoring first-level statistics for contrasts, and many minor bug fixes. Stay tuned for the next version which will support new data modalities.
PsPM 3.0 is released. It offers an entirely novel GUI, building on the Matlab Batch Editor well known to SPM users. We worked > 1 year on streamlining the code, testing basic functions, fixing bugs, and enhancing usability. A tutorial now offers a smooth introduction into usage of the software – it is contained in the manual. Tell us whether you like it!
Matching pursuit is a machine-learning algorithm that allows for fast model inversion. In a new paper, Bach & Staib use this algorithm to infer tonic arousal from skin conductance recordings – by estimating the number of sudomotor bursts. This estimation is about 100 times faster than the currently available DCM inversion for this purpose, and not less precise in terms of predictive validity: Bach DR & Staib M 2015, Psychophysiology, in press.
A direct comparison of SCRalyze and Ledalab reveals consistently higher sensitivity for the GLM approach in SCRalyze than for all Ledalab methods. SCRalyze is also far more sensitive than a standard peak scoring approach: Bach DR 2014, Biological Psychology, 103, 63-88.