Recorded EEG data on Win10 with Muse S. When the file reaches 4 MB, the connection is lost. Repeatable, tried four times.

The process for CS students: http://cs.lth.se/examensarbete/hur-gaar-det-till/
General CS dep resource: http://cs.lth.se/examensarbete/
General LTH resource: http://www.student.lth.se/studieinformation/examensarbete/examensarbetsprocessen/

• Arbetstitel, inblandades namn och kontaktuppgifter samt preliminärt start- och slutdatum.
• Bakgrund/kontext och motiv för examensarbetet.
• Övergripande mål och problemställningar/forskningsfrågor.
• Angreppssätt/metodik och metoder.
• Vetenskaplig grund och beprövad erfarenhet som examensarbetet ska bygga vidare på. Detta kan t ex beskrivas i form av ett par nyckelreferenser till artiklar eller annat underlag.
• Hur förväntas examensarbetet bidra till kunskapsutvecklingen?
• Preliminär beskrivning av resurser som krävs för arbetets genomförande, t ex arbetsplats och utrustning, och hur dessa ordnas och finns tillgängliga.

Getting this error/warning intermittently.

Possible causes:

• Using Bluetooth speakers simultaneously
• Collecting PPG (known to have issues, see #9)

Not sure how fast/well it recovers. Need to investigate the raw data.

Not novel, but should be pretty easy to set up given the overlap with already existing code.

Edit: It does.

Using https://github.com/ActivityWatch/aw-watcher-input

I found some tricky issues and found that MNE has tools for just the thing.

After browsing the documentation a bit again, it seems like I've duplicated a lot of work by not using MNE when I probably should.

A partial rewrite is in order.

• See if MNE is useful for our arbitrary-length epochs and window-sliding approach.
• Complete restructure of dataset into BIDS (#10)

Not sure what's up with that, or how to deal with it.

From looking at the raw data, it looks like it's -1000 exactly every 5th row. Sometimes there are 2 in a row, and then it repeats every 5th row again.

Edit: Maybe this is just powerline noise? At the sampling freq of 250Hz the powerline peak would happen roughly every 4-5th sample. Why are TP9 and TP10 so much more sensitive though?

Example:

1603711387.314,-1000.000,-44.434,-38.574,-1000.000,0.000
1603711387.318,-609.375,-29.297,-27.344,-574.707,0.000
1603711387.322,787.109,-19.531,-23.926,814.941,0.000
1603711387.325,-852.051,-27.832,-22.461,-858.887,0.000
1603711387.329,184.082,-37.598,-23.926,189.941,0.000
1603711387.333,-1000.000,-45.410,-39.062,-1000.000,0.000
1603711387.337,-836.914,-34.668,-30.762,-804.688,0.000
1603711387.341,519.043,-18.555,-11.719,561.523,0.000
1603711387.345,-801.758,-18.555,-20.508,-808.105,0.000
1603711387.349,150.391,-23.438,-27.832,155.762,0.000
1603711387.353,-1000.000,-29.785,-26.367,-1000.000,0.000
1603711387.357,-1000.000,-30.762,-27.832,-1000.000,0.000
1603711387.361,178.711,-21.484,-20.508,231.934,0.000
1603711387.365,-764.648,-22.949,-21.484,-768.555,0.000
1603711387.368,222.168,-33.203,-31.250,198.242,0.000
1603711387.372,-1000.000,-39.551,-32.715,-1000.000,0.000
1603711387.376,-1000.000,-33.203,-31.250,-1000.000,0.000
1603711387.380,-36.133,-21.484,-26.367,-6.836,0.000
1603711387.384,-789.062,-16.113,-27.832,-781.738,0.000
1603711387.388,409.668,-18.066,-33.691,378.906,0.000
1603711387.392,-909.180,-28.320,-34.180,-925.293,0.000
1603711387.396,-1000.000,-26.855,-33.203,-1000.000,0.000
1603711387.400,-213.867,-16.113,-29.785,-186.523,0.000
1603711387.404,-873.535,-15.137,-23.438,-854.492,0.000
1603711387.407,650.879,-21.484,-28.320,603.516,0.000
1603711387.411,-738.281,-66.406,-38.086,-779.785,0.000
1603711387.415,-1000.000,-78.613,-34.180,-1000.000,0.000
1603711387.419,-204.102,-25.879,-19.531,-210.449,0.000
1603711387.423,-943.848,-7.812,-18.555,-930.664,0.000
1603711387.427,878.418,-9.277,-25.879,835.938,0.000
1603711387.431,-439.453,-28.320,-37.109,-500.977,0.000
1603711387.435,-1000.000,-36.621,-27.344,-1000.000,0.000
1603711387.439,-177.734,-18.066,-20.996,-181.641,0.000
1603711387.443,-991.699,-17.578,-37.598,-982.910,0.000
1603711387.447,-974.121,-29.297,-38.574,-996.094,0.000
1603711387.450,-139.160,-31.738,-42.969,-194.824,0.000
1603711387.454,-1000.000,-18.066,-48.340,-1000.000,0.000
1603711387.458,-303.223,-12.695,-33.691,-268.066,0.000

Tasks

• Split phase into issues/tasks
• Clean/align data
• Set up a pipeline using MNE-Python and scikit-learn
• Try different classifiers (one-vs-all and multiclass)
• Implement classifier for codeprose (#25)
  • More or less done
• More?
  • Get feedback from someone who knows their eeg

GQM

TODO (fetch from goaldoc)

Metrics

• Confusion matrix (which activities are hard to classify/discern?)

• Implement support for the Muse S
  • Done, but some dependency issues that make it awkward to use together with recent versions of pylsl, see alexandrebarachant/muse-lsl#136
• Implement support for OpenBCI Cyton (moved to #12)

• Query ActivityWatch for classified events (in ./scripts/query_aw.py)
• Construct labels and align with EEG data

Issue in eeg-notebooks: NeuroTechX/EEG-ExPy#70

• Replication files: https://github.com/collab-uniba/Replication_Package_ICPC
  • Stimuli images/tasks located in psycode.zip/psycode/resources/app/assets/questions
  • All images are in Italian (and all code is C), so will probably have to find or create tasks myself.
    • How can I do this in the fastest/best way? Find existing code puzzles/comprehension tasks?
• The original fMRI study replication files can be found here: https://web.eecs.umich.edu/~weimerw/fmri.html (direct link to .zip: https://web.eecs.umich.edu/~weimerw/fmri-resources/2016-materials.zip)
  • Will the fact that it's C code be a concern for the validity of the study? (given the rarity of C programmers in my peer group, that would serve as study participants)
    • Nah, turns out half my friends write C++ at work now lol

@JohnGriffiths was also interested in this.

• Write basic implementation (done in dfbe6db)
• Merge implementations for eegwatch check and the preprocessing step

• Adapt material from goaldoc
• Set up LaTeX formatting stuff (steal things from goaldoc)
• Ensure I've cited used software properly
  • mne
  • muse-lsl
  • brainflow
  • sklearn
  • numpy/scipy/matplotlib?
  • pytorch?
  • yasa?
• Ensure I've cited helpful datasets properly
• Write a short summary of Riemannian geometry in EEG.

Resources

• https://portal.research.lu.se/portal/sv/publications/writing-in-english-at-university(5bfb15e9-6809-4243-b840-0ded00251f76).html

Places to publicize thesis once done:

• Personal Twitter, Facebook, LinkedIn
• Any relevant subreddits?
• OpenBCI channels (their LinkedIn content gets decent engagement)
• NeuroTechX meetups/hacknights
• Conferences (Markus might know)
• Kaggle? (if parts of dataset can be made public)
• Journals
  • Journal of Open Source Software: https://joss.theoj.org/
    • Probably a good fit for ActivityWatch. Should probably consider publishing a paper on it there, eventually.

Hello Erik

Sorry to abuse your repo, but I've emailed you on 10/dec ("Muse pipeline wrapper"), but didn't get any reply. maybe it hit your spam folder? I'd be happy to get a ping back, to know if you're in to it.

Thanks, and again - sorry for this repo-lution.
Oori

• Basic support
  • Basic support implemented in 9a4b89a
  • Might have some issues though, see alexandrebarachant/muse-lsl#110
• Test resilience during longer recordings
  • There seems to be some issues, see #11.
• Convert PPG data to actionable features (such as HR, HRV).
  • Unclear how to do this from the PPG1, PPG2, PPG3 columns in CSV.

Tasks

• Decide on electrode placement
  • Decided for me by the Muse S
  • For the OpenBCI Cyton might go with the default Ultracortex placements, since they have decent variation across the brain.
• Build an EEG watcher that can run in the background (#8)
• Collect basic dataset (during organic device activity)
  • Collect a fair bit of data using the Muse
  • Construct labels from ActivityWatch data (#7)
• Work on a basic pipeline
  • Get data into FIF/BIDS (#10)
  • Example pipelines
    • https://github.com/NeuroTechX/eeg-notebooks
    • https://github.com/NeuroTechX/moabb
    • https://github.com/mne-tools/mne-study-template
    • https://github.com/braindecode/braindecode
  • Apparently Riemannian geometry is all the hype: https://github.com/alexandrebarachant/pyRiemann

GQM

TODO (fetch from goaldoc)

Tasks

• Split phase into issues/tasks
• Design experiment
  • Which activities?
  • Electrode placement?
  • Duration?
• Enlist volunteers
• Collect data (#27)

GQM

TODO (fetch from goaldoc)

@alexandrebarachant, who authored pyRiemann, has won several competitions with Riemannian approaches:

• https://github.com/alexandrebarachant/Grasp-and-lift-EEG-challenge
• https://github.com/alexandrebarachant/decoding-brain-challenge-2016
• https://github.com/alexandrebarachant/bci-challenge-ner-2015

We'll investigate previous approaches to classify EEG data that is similar to our task at hand.

See also #17.

Classifying tasks

Likely the most similar type classification.

Synchronized Brainwave Dataset (2015)

Dataset on Kaggle: https://www.kaggle.com/berkeley-biosense/synchronized-brainwave-dataset

Stimuli:

• Video 1: https://www.youtube.com/watch?v=zkGoPdpRvaU
• Video 2: https://www.youtube.com/watch?v=sxqlOoBBjvc

They both follow the same process:

• Blinking
• Relax (closed eyes, focus on breathing)
• Arithmetic
• Relax with music
• Video clip
• Come up with examples from category
• Count squares of chosen color

A popular subset of the stimuli is relaxation vs math:

• With a SVM: https://www.kaggle.com/elsehow/classifying-relaxation-versus-doing-math
• With a NN: https://www.kaggle.com/wpncrh/classifying-tasks-using-eeg-data-w-tensorflow-nn

Reading prose vs code (Fucci et al)

No publicly available dataset. Ask Fucci?

Classifying sleep stages

Shares some similarities (long recordings, "organic" data).

See the excellent YASA: https://github.com/raphaelvallat/yasa

Classifying emotion

Might be somewhat similar. Often uses 1min clips of happy/sad movie scenes as stimuli.

Sometimes split into arousal/valence.

Classifying mental states (focus etc)

Classifying things like focus is sometimes considered a simpler task where acceptable classification can be achieved with a simple power band ratio.

EEG data for Mental Attention State Detection (focused, unfocused, drowsy)

Dataset on Kaggle (MATLAB files): https://www.kaggle.com/inancigdem/eeg-data-for-mental-attention-state-detection

BIDS spec & common principles: https://bids-specification.readthedocs.io/en/stable/02-common-principles.html

• Convert CSV to FIF
• Convert FIF to BIDS
  • mne-study-template seems to err on the produced BIDS, so everything might not be correct.
• Run BIDS validator: https://bids-standard.github.io/bids-validator/
• Use EDF instead of FIF?
  • Nah: mne-tools/mne-python#5302 (comment)
• Restructure existing data folder to use BIDS exclusively (use sourcedata for unprocessed/unconverted files!)
• Test with other devices (Neurosity Crown)

Might differ for different devices. Only Muse S has been tested so far.

• Implement support in eegwatch
• Convert raw data to FIF/BIDS (#10)