[Eeglabnews] EEGLAB 2019.0 released

[Arnaud Delorme]

Dear EEGLAB users,

A new EEGLAB version  (v2019.0) is now available (yes, we have changed the EEGLAB version numbering scheme). This is major update, long delayed by a few nagging problems, now addressed. Below we list some of the included enhancements:

- Single-trial processing in STUDY processing functions. This version includes a new STUDY framework compatible with LIMO (LInear MOdeling) applied to EEG data (Pernet et al., 2011). We also reworked STUDY-based computations (ERP, ERSP, ITC, mean spectra). You now only need to precompute these measures once, no matter how many statistical designs you want to run on the STUDY data. Note: While all existing EEGLAB STUDY sets can be processed using STUDY functions in v2019.0, to perform additional statistical testing on an existing STUDY (prior v2019.0), the STUDY functions will need to recompute the pre-computed measure files.

- There is a new plug-in manager with plugin rating capabilities (with now over 100 plug-ins available, this was much needed),

- Full Octave compatibility from the command line: The freely available open source app Octave will now run EEGLAB command line MATLAB scripts without requiring a MATLAB license. Note, however, that the EEGLAB graphic interface and menu are not available under Octave.

- New open source license: The open source license EEGLAB has been changed to BSD (from GNU) to allow commercial re-use of EEGLAB code (Note: each EEGLAB plug-in is released under its own license).

- See more detail about changes to EEGLAB since the last revision, including bug fixes and improvements, on this page https://sccn.ucsd.edu/wiki/EEGLAB_revision_history_version_2019

- Bug reporting: You may also directly use the Github version of EEGLAB - https://github.com/sccn/eeglab. Any EEGLAB bugs should now be submitted through the Github interface.

New plug-ins: We also recommend that you check out these new plug-ins:

-- IClabel - classifies independent components (ICs) of EEG data as brain or non-brain (recognizing 7 IC classes in all), returning a vector of weights for each class that can be treated as a probability vector:  https://sccn.ucsd.edu/wiki/ICLabel. See also the ICLabel tutorial for learning to classify ICs, and thereafter consider contributing to the crowd-sourced labeling effort which should keep enhancing ICLabel robustness as it is retrained on more and more contributed IC labels.

-- get_chanlocs - scan and measure 3D electrode positions using a relatively low cost iPad and attached Structure scanner, thereby saving valuable time of the participant for data recording:  This page<https://sccn.ucsd.edu/wiki/Get_chanlocs.> links to an illustrated User Guide.

Also, we are now planning a first quarterly EEGLAB News email newsletter. More on this soon ...

The EEGLAB workshop in June in Aspet, France is full. The next scheduled EEGLAB workshop will be in Poland in May 2020 and at UCSD in San Diego, California, mid-2020.

Thank you for continuing to use and contribute to the EEGLAB signal processing environment,

Arnaud Delorme
Ramon Martinez-Cancino
Scott Makeig
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