<!-- TITLE: Software for doing EDA research --> # Processing the data in Python [scipy.signal.find_peaks_cwt](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks_cwt.html#scipy.signal.find_peaks_cwt) finds all local maxima (possibly the peak of an SCR) in each time series we can then filter out any peaks which did not occur within 2 seconds of another person's the remaining peaks are possible "meaningful moments" [sklearn.neighbors.kde.KernelDensity](https://stackoverflow.com/questions/35094454/how-would-one-use-kernel-density-estimation-as-a-1d-clustering-method-in-scikit/35151947#35151947) finds clusters of these possible synchronizations then [moviepy](https://zulko.github.io/moviepy/) to cut into the correct video clips # AcqKnowledge there is a [lab with some legit software](https://www.human.cornell.edu/hd/research/labs/eeg/facility) on Cornell's campus (256 HumanEcol) # Algorithms + [advanced deconvolution method](https://www.frontiersin.org/articles/10.3389/fnins.2019.00780/full) estimates times of stimuli, magnitude, and physiological characteristics even + I would likely have to ask them for the code, and try to modify it + sounds extremely complicated + [simpler deconvolution method](https://en.wikipedia.org/wiki/Wiener_deconvolution) probably doesn't work for me, because I don't know when the stimuli are + [surrogate data generation](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163103/)?? I'm dubious... + [why cross-correlation isn't good enough](https://link.springer.com/article/10.3758/s13428-015-0611-2) + [a non-cvxEDA method](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2904901/) for extracting tonic and phasic # Electrodermal activity in python + [EDA explorer](https://eda-explorer.media.mit.edu/) looks great, but only supports Q Sensor, E4, and Shimmer data. Need to reverse engineer these formats to use it... + [cvxEDA](https://github.com/lciti/cvxEDA) looks very well done, and exactly what I need + [NeuroKit](https://github.com/neuropsychology/NeuroKit.py) has analysis software for multiple types of neuro data, and uses cvxEDA under the hood for EDA ([EDA docs](https://neurokit.readthedocs.io/en/latest/documentation.html#eda-scr)) + I can at least get the data into NeuroKit + [Stimfit](https://www.frontiersin.org/articles/10.3389/fninf.2014.00016/full) ([software](https://neurodroid.github.io/stimfit/manual/getting_started.html#file-opening)) ([download](https://github.com/neurodroid/stimfit/releases)) + this looks good, but can't import CSVs! :( + [maybe SigViewer?](http://liinc.bme.columbia.edu/wp-content/uploads/SigViewer_GrazBCI.pdf?x99316) + [General Data Format](https://en.wikipedia.org/wiki/General_Data_Format_for_Biomedical_Signals) has no implementation in Python, but it does in MatLab + [MNE](https://mne.tools/stable/index.html) looks fabulous!! + also, I should find my old notes ([lol](http://wiki.alecmcgail.com/neuro/equip)) + FOUND THEM! but in a text file... here you go + HDF5 is apparently "non-proprietary" -- so there's a [converter](https://www.h5py.org/) # New notes on software for SCR analysis + [ephyviewer](https://github.com/NeuralEnsemble/ephyviewer) # Old notes on software for neuro in general + [Software for neuroscience computing](https://www.dartmouth.edu/~ccn/software/) + Where I found neuroDebian + [The Ultimate Neuroscience Software Platform](http://neuro.debian.net/) + GREAT! + [Neo - NeuralEnsemble](https://neuralensemble.org/projects/) + Electrophysiology analysis package + [Byron Yu: Software](https://users.ece.cmu.edu/~byronyu/software.shtml) + Amazing data visualization and analysis strategies for neural data + [DataHigh](https://users.ece.cmu.edu/~byronyu/software/DataHigh/datahigh.html) + One in particular I liked ^^^^^^^^ + [ConnectomeDB](https://db.humanconnectome.org/app/template/Login.vm;jsessionid=59CA62D4729A69473CD71A51FF95CC15) + HUGE database of neural stuff. + [THIS!](https://aws.amazon.com/marketplace/pp/B00AW0MBLO/ref=mkt_m3_nitrc) + NITRC Computational Environment + NITRC-CE is a virtual computing platform pre-configured with many neuroimaging data analysis applications. + I tried this out and it's STELLAR. + The VirtualMachine is just run IN BROWSER + automatically connected to tons of nice data. + needed to put "archive" in the sources.list -- look it up, it's just an old version of Debian