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Statistical Parametric Mapping Workshop @KU Leuven, Belgium, 20-21 January 2022

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  • Statistical Parametric Mapping Workshop @KU Leuven, Belgium, 20-21 January 2022

    Statistical Parametric Mapping (SPM) is the analysis technique that allows the statistical analysis of typical biomechanical data e.g. 1D curves and vectors. It allows to avoid subjective analysis decisions. Actually, it works like the basic statistical analyses we all know, such as t-tests, ANOVA, and linear regression, but it extends these to one-dimensional profiles of forces or kinematics. Actually, pretty much anyone can use it with a little bit of training, which only requires one to learn the basic principles that underpin the technique, and then apply this through very basic tools.

    Workshop description
    In this workshop you will learn to understand the principles underpinning SPM. You will conduct SPM analyses in Matlab (or Python) AND learn how to include SPM in your scientific reporting. A certificate can be earned at the end of the two-day workshop, based on successfully conducting hypothesis tests on experimental and simulated data.

    Topics covered during the workshop
    • Principles of probability and random field theory
    • Temporal data registration and smoothing
    • Running a t-test using SPM
    • ANOVA using SPM
    • Linear regression using SPM
    • Writing up SPM results

    Registration and further info:

  • #2
    Hi Jos.
    Thanks for hosting the SPM online and face-to-face courses. I am looking at joining the online course but I was wondering if you or any other Biomech-L users could resolve an SPM question first.

    We are piloting using SPM analysis for biomechanical analysis research. Our research group has previously used SPM to compare acceleration signals from inertial measurement units (IMU) (

    We are comparing IMU acceleration signals to vertical loading rate. The signals have different measurement scales (m/s/s and BW/s). My question is whether it is appropriate to use SPM to compare different signals (assuming data registration) or whether you can potentially normalise data (0-100%)

    Thanks in advance.

    Eoin Doyle