Events

Brainlife App to extract events using MNE-Python mne.find_events.

Documentation

Input files are:

• a MEG file in .fif format,

Input parameters are:

• stim_channel: None | str | list of str, Name of the stim channel,
• consecutive: bool, If True, consider instances where the value of the events channel changes without first returning to zero as multiple events. If False, report only instances where the value of the events channel changes from/to zero. If ‘increasing’, report adjacent events only when the second event code is greater than the first.
• mask_type: The type of operation between the mask and the trigger.
• min_duration: float The minimum duration of a change in the events channel required to consider it as an event (in seconds).

Ouput files are:

• event.tsv file,
• a plot of the events

Authors

• Saeed ZAHRAN([email protected])

Contributors

• Saeed ZAHRAN
• Maximilien Chaumon

Funding Acknowledgement

brainlife.io is publicly funded and for the sustainability of the project it is helpful to Acknowledge the use of the platform. We kindly ask that you acknowledge the funding below in your code and publications. Copy and past the following lines into your repository when using this code.

Citations

1. Avesani, P., McPherson, B., Hayashi, S. et al. The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services. Sci Data 6, 69 (2019). https://doi.org/10.1038/s41597-019-0073-y
2. Taulu S. and Kajola M. Presentation of electromagnetic multichannel data: The signal space separation method. Journal of Applied Physics, 97 (2005). https://doi.org/10.1063/1.1935742
3. Taulu S. and Simola J. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Physics in Medicine and Biology, 51 (2006). https://doi.org/10.1088/0031-9155/51/7/008