Weekly MOABB meeting

• Thursday 3rd December

• Thursday 10th December

• Thursday 14th January

  1. Blocking issues:
  • update HDF5 requirement, https://docs.h5py.org/en/stable/whatsnew/3.0.html#breaking-changes-deprecations
  • Pinning scikit-learn requirement because of PyRiemann, temporary fix, should be updated soon.
  2. Enhancements
  • Adding SSVEP datasets: almost okay, 2 datasets ok. Need to update classes for MAMEM datasets (200+ electrodes)
  • Additional information in results (PR #127): almost ok, need review
  • Learning curve: evaluate accuracy while varying the number of trials. First draft.
  • Transfer learning: discussions in issue.
  3. Open discussions:
  • Google summer of code
  • Sebastien: run ML/autoML competitions, interest in transfer learning, few shot learning, cross-dataset, cross-hardware, cross-paradigm. Could be interesting to run a meta-learning challenge, in a RAMP like fashion maybe.
  • How to compare fairly different tasks, with different number of class, research questions close to meta-learning

• Thursday 21st January

  1. Open discussion:
     • Morgan Hough: asking financial support from a company to support cloud computing time for processing data. Also computational power available to run evaluations soon.
     • New techniques in ML: evaluation on EEG
     • BCI society meeting on ML, check for recording available online
     • Adding support to affective computing, and emotion recognition, as more and more databases are available such as DEAP datasets or GAMEEMO
  2. Code update:
     • Additional columns PR # 127: last review and merge
     • Learning curve on various trial number: cross validation first, select subset of trials (in percentage or in number of sample per classes), repeat multiple times (permutation)
  3. Issues
     • Explain MOABB philosophy, regarding the idea to restrict the number of parameters to ease the comparison of algorithm. For example, the number of k-fold validation is set to 5 to ensure reproducible and accurate results.
     • Divyesh: open a new discussion for cVEP paradigm, as there is data available