EuroMov, European Research Center on Human Movement +33 434 432 630 contact@euromov.eu EuroMov is a research centre of Université de Montpellier Université de Montpellier

Axe

Biological Signal Processing

Transversal axis: Biomedical Signal Processing

 

Members of the axis:

Julien Lagarde (N&R group), Simone Dalla Bella (R&S group), Sofiane Ramdani (C&A group, responsable).

 

Objectives of the transversal axis:

The three M2H groups are involved in research projects for which several types of data are recorded and analyzed. These data include kinematic or dynamic human movement data signals (e.g., posture, locomotion, bimanual and interpersonal coordination) as well as neurophysiological signals (e.g., EEG, EMG, NIRS).

 

The main objectives of the Biomedical Signal Processing transversal axis are to:

  • Provide tools and assistance to solve methodological issues related to the M2H research projects.
  • Validate the methods used to address new problems.
  • Develop new approaches and measures adapted to the scientific questions addressed in the groups.

The main lines of this methodological axis are:

 

  1. Multivariate analysis:

Nowadays, various multivariate time series analyses are very commonly performed to investigate neurophysiologic signals. Classical linear tools such as principal component analysis can be used for dimension reduction and detection of correlations between sources. For some neurophysiological signals (EEG, MEG), the independent component analysis can also be a powerful technique, which is able to extract sources from a set of signals. Other important aspects of multivariate analysis include the study of synchronization and linear or nonlinear interdependence (and its directionality) between simultaneously recorded sources. These different methods are appropriate for the investigation of data recorded in the lab such as multichannel NIRS and EEG data as well as biomechanical signals (posture and locomotion).

 

  1. Multiscale analysis:

Another feature of some of the data collected in the lab is related to their multiscale characteristics. Beside the classical time-frequency approaches, the empirical mode decomposition (EMD) is an interesting adaptive (data-driven) alternative, which can be applied to nonstationary signals generated by nonlinear systems.

 

  1. Data mining:

The significant increase of the amount of data recorded within our research projects (e.g., large amount of clinical and movement data in pathological populations) will naturally lead us to exploit data mining techniques. The main objective of data mining is to provide automatic assistance in transforming database information into useful knowledge. These approaches will be specifically useful to explore the data provided by the clinical research projects of the lab. For instance, clustering techniques can provide interesting insights into the classification of subjects according to their behavior or response to experimental manipulations. Another issue that is worth of interest is related to pattern recognition to unravel and interpret movement and physiological recordings. The three groups of the lab have an interest in such data analysis approaches. For this category, the axis will collaborate with experts from the engineering school Ecole des Mines d’Ales.

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