SPM EEG Spike detection toolbox | BEAMS

SPM EEG Spike detection toolbox

Project information
Status: 
Running
Research unit: 
Micro-Bio-Mechatronics
BEAMS members
Head of project

SpikeDet, a SPM EEG Spike detection toolbox, allows to detect spikes in an EEG record by using a fully automated method described in [Nonclercq2012] and based on [Nonclercq2009]:

We propose a fully automated method of interictal spike detection that adapts to interpatient and intrapatient variation in spike morphology. The algorithm works in five steps. (1) Spikes are detected using parameters suitable for highly sensitive detection. (2) Detected spikes are separated into clusters. (3) The number of clusters is automatically adjusted. (4) Centroids are used as templates for more specific spike detections, therefore adapting to the types of spike morphology. (5) Detected spikes are summed.

Detected spikes are marked as “spike” event with a value corresponding to the electrode name where the spike has been detected. At the end of the detection process, it also computes and prints the spike index defined by the ratio between the number of one second window containing at least one spike and the total length of the EEG record in seconds.

This toolbox was codevelopped by Antoine Nonclercq and Rudy Ercek.
If you use this toolbox for a publication (in a journal, in a conference, etc.), please cite it by including as much information as possible from the following:

Antoine Nonclercq & Rudy Ercek, SpikeDet : a SPM EEG Spike detection toolbox, Université Libre de Bruxelles, http://beams.ulb.ac.be/research-projects/spm-eeg-spike-detection-toolbox, 2016.

Please also cite both related publications: [Nonclercq2012] and [Nonclercq2009].

[Nonclercq2009] Nonclercq, A., Foulon, M., Verheulpen, D., De Cock, C., Buzatu, M., Mathys, P., & Van Bogaert, P. (2009). Spike detection algorithm automatically adapted to individual patients applied to spike and wave percentage quantification.Neurophysiologie Clinique, 39, 123–131. doi:10.1016/j.neucli.2008.12.001

[Nonclercq2012] Nonclercq, A., Foulon, M., Verheulpen, D., De Cock, C., Buzatu, M., Mathys, P., & Van Bogaert, P. (2012). Cluster-based spike detection algorithm adapts to interpatient and intrapatient variation in spike morphology. Journal of Neuroscience Methods, 210(2), 259–265. doi:10.1016/j.jneumeth.2012.07.015
 
EEG Graphics view with detected spike events
EEG Graphics view with detected spike events
 

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