Mar. 6

[rmarren1]

Monday Commitments

Status	Task	Deliverable
:panda_face:	Methods.md for other denoising techniques (other than PCA or RPCA which did not give good results)	file@rmarren1
🐼	Code to organize eye-tracking data into blinks, saccades, and fixations. Then organize the eye-tracking data with the EEG data according to trials and triggers.	@tsunwong123 EEGMethods.md EEGActualData Eyetrack.ipynb Code to Integrate into Pipeline
🐼	Finish comparison graphs	.ipynb with plots @nkumarcc
🐼	observe plots to figure out different methods that make sense for bad electrode detection and what to show to Nicolas	pairs plots @nkumarcc

Thursday Checkpoint

Status	Task	Deliverable
:panda_face: :	Measure discriminibility of the interpolations done by Nicolas, determine whether it helped. If so, attempt to re-create results with our data and interpolation method.	repo@rmarren1
:x:	refactor and then document benchmarking tools	@rmarren1 -- walk through of how one of the benchmark notebooks was produced using this utility.
:x:	Research some noise reduction papers and discuss with greg which look the most promising	@rmarren1
⏳	To show that it is necessary to label the trials of EEG data by showing an unaligned version.	.ipynb @tsunwong123
⏳	Make a few eye-tracking data plots	.ipynb @tsunwong123
⌛️	Make plots as specified by Greg's comment here. See if there's anything that stands out about the data.	.ipynb with the actual plots .ipynb for pairs plot @nkumarcc

[rmarren1]

@gkiar @tsunwong123 @nkumarcc

[gkiar]

@nkumarcc do you have any issues for the week? @tsunwong123 the definition of done for your Monday deliverable are plots showing me the raw (un- aligned/processed/whatever) data all side by side, and then the same plots showing me that data after your processing. This should importantly only be shown after you prove to me that your code works on simulated data, by doing a methods.md for it. @rmarren1 👌

[nkumarcc]

@gkiar: updated

[gkiar]

👌