├── .gitignore ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── JupyterNotebookGuideline.txt ├── README.md ├── Tutorials ├── .ipynb_checkpoints │ ├── About-checkpoint.ipynb │ ├── AcquiringData-checkpoint.ipynb │ ├── Applying Machine Learning Methods to Temporal Dimensions-checkpoint.ipynb │ ├── Applying Machine Learning Methods-1-checkpoint.ipynb │ ├── Applying Machine Learning Methods-2-checkpoint.ipynb │ ├── ApplyingMachineLearningMethods_1-checkpoint.ipynb │ ├── ApplyingMachineLearningMethods_2-checkpoint.ipynb │ ├── ApplyingMachineLearningMethodstoTemporalDimensions-checkpoint.ipynb │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_old_version-checkpoint.ipynb │ ├── BaselineCorrection-checkpoint.ipynb │ ├── ComplexNumbers-checkpoint.ipynb │ ├── Data Loading-checkpoint.ipynb │ ├── Data Visualization-checkpoint.ipynb │ ├── DataLoading-checkpoint.ipynb │ ├── DataVisualization-checkpoint.ipynb │ ├── Dataset Construction-checkpoint.ipynb │ ├── DatasetConstruction-checkpoint.ipynb │ ├── IntroductionToSignalProcessing-checkpoint.ipynb │ ├── Referencing for EEG data-checkpoint.ipynb │ ├── ReferencingforEEGdata-checkpoint.ipynb │ ├── Temporal_vs_Spectral -checkpoint.ipynb │ └── introToSP-checkpoint.ipynb ├── 817_1_PDDys_ODDBALL_Clean_curated-epo.fif ├── About.ipynb ├── AcquiringData.ipynb ├── ApplyingMachineLearningMethods_1.ipynb ├── ApplyingMachineLearningMethods_2.ipynb ├── ApplyingMachineLearningMethods_2_old_version.ipynb ├── ApplyingMachineLearningMethodstoTemporalDimensions.ipynb ├── ApplyingMachineLearningMethodstoTemporalDimensions_old_version.ipynb ├── ApplyingMachineLearningMethodstoTemporalDimensions_with_slidingEstimator.ipynb ├── BaselineCorrection.ipynb ├── ComplexNumbers.ipynb ├── DataLoading.ipynb ├── DataVisualization.ipynb ├── DatasetConstruction.ipynb ├── Hello.md ├── Introduction.ipynb ├── IntroductionToSignalProcessing.ipynb ├── Outline.md ├── ReferencingforEEGdata.ipynb ├── Temporal_vs_Spectral.ipynb ├── Tutorials.md ├── introToSP.ipynb ├── ml.md └── preprocess.md ├── _config.yml ├── datasets ├── 817_1_PDDys_ODDBALL_Clean_curated-epo.fif ├── 904_1_PDDys_ODDBALL_Clean_curated.fif └── MNE-eegbci-data │ └── physiobank │ └── database │ └── eegmmidb │ └── S001 │ ├── S001R06.edf │ ├── S001R10.edf │ └── S001R14.edf └── docs ├── Gemfile ├── Gemfile.lock ├── Guardfile ├── Makefile ├── _bibliography └── references.bib ├── _build ├── Tutorial_files │ ├── About.html │ ├── AcquiringData.html │ ├── ApplyingMachineLearningMethods_1.html │ ├── ApplyingMachineLearningMethods_2.html │ ├── ApplyingMachineLearningMethodstoTemporalDimensions.html │ ├── BaselineCorrection.html │ ├── ComplexNumbers_temp.html │ ├── DataLoading.html │ ├── DataVisualization.html │ ├── DatasetConstruction.html │ ├── Introduction.html │ ├── Outline.html │ ├── ReferencingforEEGdata.html │ ├── Temporal_vs_Spectral.html │ ├── introToSP.html │ ├── ml.html │ └── preprocess.html ├── features │ └── cool.jpg └── images │ ├── C-3PO_droid.png │ ├── Tutorial_files │ ├── ApplyingMachineLearningMethods_1_47_0.png │ ├── ApplyingMachineLearningMethods_1_48_0.png │ ├── ApplyingMachineLearningMethods_2_18_0.png │ ├── ApplyingMachineLearningMethods_2_26_0.png │ ├── ApplyingMachineLearningMethods_2_32_0.png │ ├── ApplyingMachineLearningMethods_2_48_0.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_1.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_11.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_13.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_15.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_17.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_19.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_21.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_23.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_25.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_27.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_29.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_3.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_31.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_33.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_35.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_37.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_39.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_41.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_43.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_45.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_47.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_49.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_5.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_7.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_14_9.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_1.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_11.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_13.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_15.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_17.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_19.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_21.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_23.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_25.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_27.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_29.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_3.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_31.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_33.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_35.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_37.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_39.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_41.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_43.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_45.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_47.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_49.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_5.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_7.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_19_9.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_20_0.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_32_0.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_36_1.png │ ├── ApplyingMachineLearningMethodstoTemporalDimensions_39_1.png │ ├── BaselineCorrection_10_0.png │ ├── BaselineCorrection_10_1.png │ ├── BaselineCorrection_11_0.png │ ├── BaselineCorrection_11_1.png │ ├── BaselineCorrection_12_0.png │ ├── BaselineCorrection_12_1.png │ ├── BaselineCorrection_16_0.png │ ├── BaselineCorrection_16_1.png │ ├── BaselineCorrection_26_0.png │ ├── BaselineCorrection_26_1.png │ ├── BaselineCorrection_7_0.png │ ├── BaselineCorrection_7_1.png │ ├── ComplexNumbers_4_0.png │ ├── DataLoading_13_0.png │ ├── DataLoading_22_0.png │ ├── DataLoading_39_0.png │ ├── DataVisualization_12_0.png │ ├── DataVisualization_12_1.png │ ├── DataVisualization_14_0.png │ ├── DataVisualization_16_0.png │ ├── DataVisualization_16_1.png │ ├── DataVisualization_19_0.png │ ├── DataVisualization_19_1.png │ ├── DataVisualization_8_1.png │ ├── DataVisualization_8_2.png │ ├── DatasetConstruction_12_1.png │ ├── DatasetConstruction_12_2.png │ ├── DatasetConstruction_18_0.png │ ├── DatasetConstruction_18_1.png │ ├── DatasetConstruction_19_0.png │ ├── DatasetConstruction_19_1.png │ ├── DatasetConstruction_20_0.png │ ├── ReferencingforEEGdata_11_0.png │ ├── ReferencingforEEGdata_11_1.png │ ├── ReferencingforEEGdata_14_0.png │ ├── ReferencingforEEGdata_14_1.png │ ├── ReferencingforEEGdata_17_0.png │ ├── ReferencingforEEGdata_17_1.png │ ├── ReferencingforEEGdata_19_0.png │ ├── ReferencingforEEGdata_19_1.png │ ├── ReferencingforEEGdata_4_0.png │ ├── ReferencingforEEGdata_4_1.png │ ├── ReferencingforEEGdata_8_0.png │ ├── ReferencingforEEGdata_8_1.png │ ├── Temporal_vs_Spectral_11_0.png │ ├── Temporal_vs_Spectral_13_0.png │ ├── Temporal_vs_Spectral_15_0.png │ ├── Temporal_vs_Spectral_17_0.png │ ├── Temporal_vs_Spectral_2_0.png │ └── Temporal_vs_Spectral_9_0.png │ ├── logo.png │ └── logo │ ├── download.svg │ ├── favicon.ico │ ├── jupyter.png │ ├── logo.png │ └── logo.psd ├── _config.yml ├── _data └── toc.yml ├── _includes ├── buttons.html ├── buttons │ ├── binder.html │ ├── download.html │ ├── jupyterhub.html │ ├── nbinteract.html │ └── thebelab.html ├── css_entry.scss ├── fb_tags.html ├── footer.html ├── google_analytics.html ├── head.html ├── js │ ├── interact-update.html │ ├── nbinteract.html │ ├── print.html │ ├── thebelab-cell-button.html │ ├── thebelab-page-config.html │ └── thebelab.html ├── mathjax.html ├── metadata.json ├── page-nav.html ├── search │ └── lunr │ │ ├── lunr-en.js │ │ └── lunr-store.js ├── sidebar.html └── topbar.html ├── _layouts └── default.html ├── _sass ├── components │ ├── _components.book__layout.scss │ ├── _components.book__topbar.scss │ ├── _components.interact-button.scss │ ├── _components.page__footer.scss │ ├── _components.page__nav.scss │ ├── _components.page__onthispage.scss │ ├── _components.search.scss │ └── _components.thebelab.scss ├── hamburgers │ ├── _base.scss │ ├── hamburgers.scss │ └── types │ │ └── _arrowalt.scss ├── main.scss ├── objects │ └── _objects.thebelab-in-cell-button.scss └── page │ ├── components │ ├── _components.hidecells.scss │ ├── _components.page.scss │ └── _components.sidebar-right.scss │ ├── elements │ ├── _elements.links.scss │ ├── _elements.syntax-highlighting.scss │ ├── _elements.tables.scss │ ├── _elements.typography.scss │ └── _elements.variables.scss │ ├── generic │ └── _generic.phone-scrolling.scss │ ├── inuitcss │ ├── elements │ │ ├── _elements.headings.scss │ │ ├── _elements.images.scss │ │ ├── _elements.page.scss │ │ └── _elements.tables.scss │ ├── generic │ │ ├── _generic.box-sizing.scss │ │ ├── _generic.normalize.scss │ │ ├── _generic.reset.scss │ │ └── _generic.shared.scss │ ├── objects │ │ ├── _objects.block.scss │ │ ├── _objects.box.scss │ │ ├── _objects.crop.scss │ │ ├── _objects.flag.scss │ │ ├── _objects.layout.scss │ │ ├── _objects.list-bare.scss │ │ ├── _objects.list-inline.scss │ │ ├── _objects.media.scss │ │ ├── _objects.pack.scss │ │ ├── _objects.ratio.scss │ │ ├── _objects.table.scss │ │ └── _objects.wrapper.scss │ ├── settings │ │ ├── _example.settings.config.scss │ │ ├── _example.settings.global.scss │ │ └── _settings.core.scss │ ├── tools │ │ ├── _tools.clearfix.scss │ │ ├── _tools.font-size.scss │ │ ├── _tools.hidden.scss │ │ └── _tools.mq.scss │ └── utilities │ │ ├── _utilities.clearfix.scss │ │ ├── _utilities.headings.scss │ │ ├── _utilities.hide.scss │ │ ├── _utilities.print.scss │ │ ├── _utilities.responsive-spacings.scss │ │ ├── _utilities.spacings.scss │ │ └── _utilities.widths.scss │ ├── main.scss │ ├── objects │ ├── _objects.copy-button.scss │ └── _objects.tooltip.scss │ └── settings │ └── settings.global.scss ├── assets ├── css │ └── styles.scss ├── custom │ ├── custom.css │ └── custom.js ├── html │ ├── index.html │ └── search_form.html ├── images │ ├── download-solid.svg │ ├── edit-button.svg │ ├── list-solid.svg │ ├── logo_binder.svg │ ├── logo_jupyterhub.svg │ └── search-solid.svg └── js │ ├── page │ ├── anchors.js │ ├── copy-button.js │ ├── documentSelectors.js │ ├── dom-update.js │ ├── hide-cell.js │ └── tocbot.js │ └── scripts.js ├── content ├── LICENSE.md ├── Tutorial_files │ ├── About.ipynb │ ├── AcquiringData.ipynb │ ├── ApplyingMachineLearningMethods_1.ipynb │ ├── ApplyingMachineLearningMethods_2.ipynb │ ├── ApplyingMachineLearningMethodstoTemporalDimensions.ipynb │ ├── BaselineCorrection.ipynb │ ├── ComplexNumbers_temp.ipynb │ ├── DataLoading.ipynb │ ├── DataVisualization.ipynb │ ├── DatasetConstruction.ipynb │ ├── Introduction.ipynb │ ├── Outline.md │ ├── ReferencingforEEGdata.ipynb │ ├── Temporal_vs_Spectral.ipynb │ ├── introToSP.ipynb │ ├── ml.md │ └── preprocess.md ├── features │ ├── cool.jpg │ ├── features.md │ ├── markdown.md │ └── notebooks.ipynb ├── images │ ├── C-3PO_droid.png │ ├── logo.png │ └── logo │ │ ├── download.svg │ │ ├── favicon.ico │ │ ├── jupyter.png │ │ ├── logo.png │ │ └── logo.psd └── intro.md ├── logo.png ├── requirements.txt ├── runtime.txt └── scripts ├── __pycache__ └── clean.cpython-37.pyc └── clean.py /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | In the interest of fostering an open and welcoming environment, we as 6 | contributors and maintainers pledge to making participation in our project and 7 | our community a harassment-free experience for everyone, regardless of age, body 8 | size, disability, ethnicity, gender identity and expression, level of experience, 9 | nationality, personal appearance, race, religion, or sexual identity and 10 | orientation. 11 | 12 | ## Our Standards 13 | 14 | Examples of behavior that contributes to creating a positive environment 15 | include: 16 | 17 | * Using welcoming and inclusive language 18 | * Being respectful of differing viewpoints and experiences 19 | * Gracefully accepting constructive criticism 20 | * Focusing on what is best for the community 21 | * Showing empathy towards other community members 22 | 23 | Examples of unacceptable behavior by participants include: 24 | 25 | * The use of sexualized language or imagery and unwelcome sexual attention or 26 | advances 27 | * Trolling, insulting/derogatory comments, and personal or political attacks 28 | * Public or private harassment 29 | * Publishing others' private information, such as a physical or electronic 30 | address, without explicit permission 31 | * Other conduct which could reasonably be considered inappropriate in a 32 | professional setting 33 | 34 | ## Our Responsibilities 35 | 36 | Project maintainers are responsible for clarifying the standards of acceptable 37 | behavior and are expected to take appropriate and fair corrective action in 38 | response to any instances of unacceptable behavior. 39 | 40 | Project maintainers have the right and responsibility to remove, edit, or 41 | reject comments, commits, code, wiki edits, issues, and other contributions 42 | that are not aligned to this Code of Conduct, or to ban temporarily or 43 | permanently any contributor for other behaviors that they deem inappropriate, 44 | threatening, offensive, or harmful. 45 | 46 | ## Scope 47 | 48 | This Code of Conduct applies both within project spaces and in public spaces 49 | when an individual is representing the project or its community. Examples of 50 | representing a project or community include using an official project e-mail 51 | address, posting via an official social media account, or acting as an appointed 52 | representative at an online or offline event. Representation of a project may be 53 | further defined and clarified by project maintainers. 54 | 55 | ## Enforcement 56 | 57 | Instances of abusive, harassing, or otherwise unacceptable behavior may be 58 | reported by contacting the project team at athina.tz@gmail.com or elpiniki.apostolaki@gmail.com. All 59 | complaints will be reviewed and investigated and will result in a response that 60 | is deemed necessary and appropriate to the circumstances. The project team is 61 | obligated to maintain confidentiality with regard to the reporter of an incident. 62 | Further details of specific enforcement policies may be posted separately. 63 | 64 | Project maintainers who do not follow or enforce the Code of Conduct in good 65 | faith may face temporary or permanent repercussions as determined by other 66 | members of the project's leadership. 67 | 68 | ## Attribution 69 | 70 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, 71 | available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html 72 | 73 | [homepage]: https://www.contributor-covenant.org 74 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing to "Open Resources for Algorithmic Decision-Making in Neuroscience" 2 | 3 | Thank you for your interest in this project! 👩🏼‍🔬 🧠 :tada: 4 | 5 | We aim to raise awareness about algorithmic decision-making in the field of neuroscience. We welcome new contributors and will be delighted to have your input! Bellow is a list of guidelines for helping in this project. 6 | Please keep in mind that these are just guidelines, not rules! We particularly welcome new ideas or different perspectives! 7 | 8 | # Table of Contents 9 | - [Getting started](#getting-started) 10 | - [How to contribute](#how-to-contribute) 11 | - [Get in touch](#get-in-touch) 12 | 13 | 14 | # Getting started 15 | * Before starting, please take a moment to check our [Code of Conduct](https://github.com/aath0/AlgorithmsNeuroscience/blob/master/CODE_OF_CONDUCT.md). 16 | * A list of open issues and future directions can be found in our [Roadmap](https://github.com/aath0/AlgorithmsNeuroscience/issues/1). 17 | 18 | # How to contribute 19 | We do have a [roadmap](https://github.com/aath0/AlgorithmsNeuroscience/issues/1) file, with a list of things we want to achieve in the next few months. 20 | Additionally, on the [issues](https://github.com/aath0/AlgorithmsNeuroscience/issues) page, you can find some topics where you could contribute. Some of the things we are currently working on are: 21 | 22 | * Create some tutorials for pre-processing electroencephalography (EEG) data 23 | * Expand on tutorials for applying machine learning algorithms in neuroscience 24 | * Work on some tutorials with simulated data, to give a better understanding of algorithmic techniques 25 | * Deciding which other topics we should pursue 26 | * Bringing everything together with JupyterBook 27 | 28 | # Get in touch 29 | You can always [open issues](https://github.com/aath0/AlgorithmsNeuroscience/issues) for any problems or new ideas that you may have. Also, feel free to email athina.tz@gmail.com if we can help with something! 30 | 31 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Open Resources for Algorithmic Decision-Making in Neuroscience 👩🏼‍🔬 🧠 2 | 3 | ### Welcome! :confetti_ball: 4 | 5 | Thank you for visiting this repository! Bellow you can find some information about this project: 6 | 7 | * [What is this the purpose of this project?](#what-is-the-purpose-of-this-project) 8 | * [Project summary](#project-summary) 9 | * [Who are we?](#who-are-we) 10 | * [How can you be involved?](#how-can-you-be-involved) 11 | 12 | ## What is the purpose of this project? 13 | 14 | We aim to raise awareness about algorithmic decision-making in the field of neuroscience by building an open curriculum 15 | 16 | ## Project summary 17 | 18 | As the amount and complexity of data collected in neuroscience increases, advanced algorithmic techniques have become imperative for analysing them. In particular, machine learning (ML) is used to infer intentions based on neural activity, in an attempt to control external devices. Algorithmic decisions can majorly improve the lives of patients suffering from neurological disorders, e.g. through neural implants that automatically detect and stop epileptic seizures before they occur, or by enabling communication in individuals who cannot control their bodies. 19 | 20 | While the use of algorithms has an enormous potential for bringing positive change, this comes with a huge cost, as algorithms are often treated as a black box. 21 | 22 | In interdisciplinary fields such as neuroscience, there is often a gap in expertise, as researchers come from very diverse fields as psychology vs. engineering. As a result, researchers from less computational fields might be using algorithms without fully understanding their assumptions, while researchers who are developing algorithms may not be fully aware of the social and ethical implications of their use (i.e. privacy, stigma). 23 | 24 | The main goal of this proposal is to raise awareness about the use of algorithmic decision-making in the field of neuroscience by building an open resource. This will consist of material and tutorials on the appropriate use of ML and signal-processing techniques for analysing neuroscientific data. The resource will comprise of a mixture of theory and hands-on exercises, aiming at making computational techniques accessible to all academic and citizen scientists. 25 | 26 | ## Who are we? 27 | 28 | Behind this project there is an interdisciplinary team of people working in neuroscience, electrical engineering and computer engineering. Some of the main contributors are: 29 | * [Athina Tzovara](https://ccneuro.github.io/): I am a neuroscientist, working at the University of Bern. In my research I am using computational and electrophysiological techniques to study human cognition, while advocating open science and open education. 30 | * [Elpiniki](): 31 | * [Pinar](): 32 | * [Samuel Gerster](): 33 | 34 | ## How can you be involved? 35 | We welcome new contributions and ideas! [Here](https://github.com/aath0/AlgorithmsNeuroscience/issues/1) you can find the roadmap of this project and [here](https://github.com/aath0/AlgorithmsNeuroscience/issues) the issues we're currently working on. 36 | 37 | You're very welcome to join our [slack channel](https://ccneurogroup.slack.com) for any questions or new ideas! 38 | -------------------------------------------------------------------------------- /Tutorials/.ipynb_checkpoints/About-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## About us\n", 8 | "\n", 9 | "### Pinar Goektepe\n", 10 | "After completing my Bachelor's degree in computer engineering, I started my Master's degree study at University of Bern in 2018 and currently I am working as a research asistant at Computational Cognitive Neuroscience Lab at University of Bern. My research interests include macnine learning, deep learning and their applications in neuroscience. \n", 11 | "\n", 12 | "### Athina Tzovara\n", 13 | "\n", 14 | "My research uses computational techniques to study human cognition. During my PhD, I developed machine learning techniques to predict the outcome of coma patients based on their neural responses to sounds (https://www.ncbi.nlm.nih.gov/pubmed/23148350). Moreover, I have been using machine learning and computational modeling techniques to model physiological data, in relation to [decision-making](https://www.sciencedirect.com/science/article/pii/S1053811912001632?via%3Dihub), or [associative learning](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006243). Importantly, throughout my research, I am advocating open and inclusive science practices, promoting diversity and inclusivity. \n", 15 | "\n", 16 | "### Elpiniki Apostolaki Iosifidou\n", 17 | "under construction \n" 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": {}, 23 | "source": [ 24 | "## Contributing\n", 25 | "\n", 26 | "We welcome new contributions and ideas! [Here](https://github.com/aath0/AlgorithmsNeuroscience/issues/1) you can find the roadmap of this project and [here](https://github.com/aath0/AlgorithmsNeuroscience/issues) the issues we're currently working on." 27 | ] 28 | } 29 | ], 30 | "metadata": { 31 | "kernelspec": { 32 | "display_name": "Python 3", 33 | "language": "python", 34 | "name": "python3" 35 | }, 36 | "language_info": { 37 | "codemirror_mode": { 38 | "name": "ipython", 39 | "version": 3 40 | }, 41 | "file_extension": ".py", 42 | "mimetype": "text/x-python", 43 | "name": "python", 44 | "nbconvert_exporter": "python", 45 | "pygments_lexer": "ipython3", 46 | "version": "3.7.1" 47 | } 48 | }, 49 | "nbformat": 4, 50 | "nbformat_minor": 2 51 | } 52 | -------------------------------------------------------------------------------- /Tutorials/.ipynb_checkpoints/introToSP-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Introduction to Signal Processing\n", 8 | "\n", 9 | "## Overview\n", 10 | "The first part of this notebook will quickly go over several basic concepts from mathematics and physics that are important for signal processing. Nothing in this should be above college mathematics and physics, so if you feel confident you rember it, feel free to skip. \n", 11 | "1. [Complex numbers](ComplexNumbers.ipynb) are fairly essential to understanding how sinusoids are represented in signal processing (and electronics in general). They are also a big help for visualising amplitude and phase of a signal.\n", 12 | "2. [Temporal Vs. Spectral Space](Temporal_vs_Spectral.ipynb) will cover some concepts important to understanding what an electronic signal is and how to decompose it into it's components. The focus will lay on the Fourier series and transforms.\n", 13 | "3. [Acquiring Data](AcquiringData.ipynb) will cover some of the basic concepts of data acquisition, such as the Nyquist-Shanon Theorem\n", 14 | "\n" 15 | ] 16 | } 17 | ], 18 | "metadata": { 19 | "kernelspec": { 20 | "display_name": "Python 3", 21 | "language": "python", 22 | "name": "python3" 23 | }, 24 | "language_info": { 25 | "codemirror_mode": { 26 | "name": "ipython", 27 | "version": 3 28 | }, 29 | "file_extension": ".py", 30 | "mimetype": "text/x-python", 31 | "name": "python", 32 | "nbconvert_exporter": "python", 33 | "pygments_lexer": "ipython3", 34 | "version": "3.7.1" 35 | } 36 | }, 37 | "nbformat": 4, 38 | "nbformat_minor": 2 39 | } 40 | -------------------------------------------------------------------------------- /Tutorials/817_1_PDDys_ODDBALL_Clean_curated-epo.fif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aath0/AlgorithmsNeuroscience/f51b0b391c9d1d1c7bda2965cf0b934ed74b3142/Tutorials/817_1_PDDys_ODDBALL_Clean_curated-epo.fif -------------------------------------------------------------------------------- /Tutorials/About.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## About us\n", 8 | "\n", 9 | "### Pinar Goektepe\n", 10 | "After completing my Bachelor's degree in computer engineering, I started my Master's degree study at University of Bern in 2018 and currently I am working as a research asistant at Computational Cognitive Neuroscience Lab at University of Bern. My research interests include macnine learning, deep learning and their applications in neuroscience. \n", 11 | "\n", 12 | "### Athina Tzovara\n", 13 | "\n", 14 | "My research uses computational techniques to study human cognition. During my PhD, I developed machine learning techniques to predict the outcome of coma patients based on their neural responses to sounds (https://www.ncbi.nlm.nih.gov/pubmed/23148350). Moreover, I have been using machine learning and computational modeling techniques to model physiological data, in relation to [decision-making](https://www.sciencedirect.com/science/article/pii/S1053811912001632?via%3Dihub), or [associative learning](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006243). Importantly, throughout my research, I am advocating open and inclusive science practices, promoting diversity and inclusivity. \n", 15 | "\n", 16 | "### Elpiniki Apostolaki Iosifidou\n", 17 | "under construction \n" 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": {}, 23 | "source": [ 24 | "## Contributing\n", 25 | "\n", 26 | "We welcome new contributions and ideas! [Here](https://github.com/aath0/AlgorithmsNeuroscience/issues/1) you can find the roadmap of this project and [here](https://github.com/aath0/AlgorithmsNeuroscience/issues) the issues we're currently working on." 27 | ] 28 | } 29 | ], 30 | "metadata": { 31 | "kernelspec": { 32 | "display_name": "Python 3", 33 | "language": "python", 34 | "name": "python3" 35 | }, 36 | "language_info": { 37 | "codemirror_mode": { 38 | "name": "ipython", 39 | "version": 3 40 | }, 41 | "file_extension": ".py", 42 | "mimetype": "text/x-python", 43 | "name": "python", 44 | "nbconvert_exporter": "python", 45 | "pygments_lexer": "ipython3", 46 | "version": "3.7.1" 47 | } 48 | }, 49 | "nbformat": 4, 50 | "nbformat_minor": 2 51 | } 52 | -------------------------------------------------------------------------------- /Tutorials/Hello.md: -------------------------------------------------------------------------------- 1 | Hello world -------------------------------------------------------------------------------- /Tutorials/Introduction.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Algorithms in neuroscience" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "## Introduction\n", 15 | "\n", 16 | "As the amount and complexity of data collected in neuroscience increases, advanced algorithmic techniques have become imperative for analysing them. In particular, machine learning (ML) is used to infer intentions based on neural activity, in an attempt to control external devices. Algorithmic decisions can majorly improve the lives of patients suffering from neurological disorders, e.g. through neural implants that automatically detect and stop epileptic seizures before they occur, or by enabling communication in individuals who cannot control their bodies. While the use of algorithms has an enormous potential for bringing positive change, this comes with a cost, as algorithms are often treated as a black box.\n", 17 | "\n", 18 | "\n", 19 | "In interdisciplinary fields such as neuroscience, there is often a gap in expertise, as researchers come from very diverse fields as psychology vs. engineering. As a result, researchers from less computational fields might be using algorithms without fully understanding their assumptions, while researchers who are developing algorithms may not be fully aware of the social and ethical implications of their use (i.e. privacy, stigma).\n", 20 | "\n", 21 | "\n", 22 | "The main goal of this resource is to raise awareness about the use of algorithmic decision-making in the field of neuroscience. To this aim, we are assembling material on the application of ML and signal-processing techniques for analysing neuroscientific data, in the form of tutorials." 23 | ] 24 | }, 25 | { 26 | "cell_type": "markdown", 27 | "metadata": {}, 28 | "source": [ 29 | "## Scope and goals\n", 30 | "\n", 31 | "The present set of tutorials focuses on Electroencephalography (EEG), which is a non-invasive technique for measuring electrical activity of the brain by placing electrodes on participants' scalp. It is a relatively cheap, non-invasive technique that can be easily applied in healthy participants and patients. For example, one clinical application where EEG is commonly used is for [diagnosing epilepsy](https://jnnp.bmj.com/content/76/suppl_2/ii2).\n", 32 | "\n", 33 | "Although other neuroscience techniques also use algorithms, at the moment this tutorial will only cover EEG. If you want to contribute with another technique, or elaborate additional EEG methods please consult [our repository](https://github.com/aath0/AlgorithmsNeuroscience/blob/master/CONTRIBUTING.md).\n", 34 | "\n", 35 | "The goals for the present tutorial are three-fold:\n", 36 | "\n", 37 | "* Describe what undelies EEG measurements and common mis-conceptions\n", 38 | "* Provide insights into good practices for analyzing EEG datasets\n", 39 | "* Introduce the user to machine learning approaches and their application in the field of EEG research" 40 | ] 41 | }, 42 | { 43 | "cell_type": "markdown", 44 | "metadata": {}, 45 | "source": [ 46 | "## Recommended resources\n", 47 | "\n", 48 | "Some resources that we recommend to study more theory in the field of EEG research include:\n", 49 | "* [MEG-EEG Primer](https://global.oup.com/academic/product/meg-eeg-primer-9780190497774?cc=ch&lang=en&)\n", 50 | "* [Analyzing Neural Time Series Data](https://mitpress.mit.edu/books/analyzing-neural-time-series-data) " 51 | ] 52 | } 53 | ], 54 | "metadata": { 55 | "kernelspec": { 56 | "display_name": "Python 3", 57 | "language": "python", 58 | "name": "python3" 59 | }, 60 | "language_info": { 61 | "codemirror_mode": { 62 | "name": "ipython", 63 | "version": 3 64 | }, 65 | "file_extension": ".py", 66 | "mimetype": "text/x-python", 67 | "name": "python", 68 | "nbconvert_exporter": "python", 69 | "pygments_lexer": "ipython3", 70 | "version": "3.6.9" 71 | } 72 | }, 73 | "nbformat": 4, 74 | "nbformat_minor": 2 75 | } 76 | -------------------------------------------------------------------------------- /Tutorials/Outline.md: -------------------------------------------------------------------------------- 1 | ## Tutorials folder 2 | 3 | Here you can find the latest version of the tutorials we're currently working on. 4 | -------------------------------------------------------------------------------- /Tutorials/Tutorials.md: -------------------------------------------------------------------------------- 1 | ## Tutorials folder 2 | 3 | Here you can find the latest version of the tutorials we're currently working on. 4 | -------------------------------------------------------------------------------- /Tutorials/introToSP.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Introduction to Signal Processing\n", 8 | "\n", 9 | "## Overview\n", 10 | "The first part of this notebook will quickly go over several basic concepts from mathematics and physics that are important for signal processing. Nothing in this should be above college mathematics and physics, so if you feel confident you rember it, feel free to skip. \n", 11 | "1. [Complex numbers](#Complex-Numbers) are fairly essential to understanding how sinusoids are represented in signal processing (and electronics in general). They are also a big help for visualising amplitude and phase of a signal.\n", 12 | "2. [Temporal Vs. Spectral Space](#Temporal-Vs.-Spectral-Space) will cover some concepts important to understanding what an electronic signal is and how to decompose it into it's components. The focus will lay on the Fourier series and transforms.\n", 13 | "3. [Acquiring Data](#Acquiring-Data) will cover some of the basic concepts of data acquisition, such as the Nyquist-Shanon Theorem\n", 14 | "\n" 15 | ] 16 | } 17 | ], 18 | "metadata": { 19 | "kernelspec": { 20 | "display_name": "Python 3", 21 | "language": "python", 22 | "name": "python3" 23 | }, 24 | "language_info": { 25 | "codemirror_mode": { 26 | "name": "ipython", 27 | "version": 3 28 | }, 29 | "file_extension": ".py", 30 | "mimetype": "text/x-python", 31 | "name": "python", 32 | "nbconvert_exporter": "python", 33 | "pygments_lexer": "ipython3", 34 | "version": "3.7.1" 35 | } 36 | }, 37 | "nbformat": 4, 38 | "nbformat_minor": 2 39 | } 40 | -------------------------------------------------------------------------------- /Tutorials/ml.md: -------------------------------------------------------------------------------- 1 | In this chapter several machine learning methods will be applied for eeg analysis. -------------------------------------------------------------------------------- /Tutorials/preprocess.md: -------------------------------------------------------------------------------- 1 | This chapter is about preprocessing of data. -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-cayman -------------------------------------------------------------------------------- /datasets/817_1_PDDys_ODDBALL_Clean_curated-epo.fif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aath0/AlgorithmsNeuroscience/f51b0b391c9d1d1c7bda2965cf0b934ed74b3142/datasets/817_1_PDDys_ODDBALL_Clean_curated-epo.fif -------------------------------------------------------------------------------- /datasets/904_1_PDDys_ODDBALL_Clean_curated.fif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aath0/AlgorithmsNeuroscience/f51b0b391c9d1d1c7bda2965cf0b934ed74b3142/datasets/904_1_PDDys_ODDBALL_Clean_curated.fif -------------------------------------------------------------------------------- /datasets/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R06.edf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aath0/AlgorithmsNeuroscience/f51b0b391c9d1d1c7bda2965cf0b934ed74b3142/datasets/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R06.edf -------------------------------------------------------------------------------- /datasets/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R10.edf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aath0/AlgorithmsNeuroscience/f51b0b391c9d1d1c7bda2965cf0b934ed74b3142/datasets/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R10.edf -------------------------------------------------------------------------------- /datasets/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R14.edf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aath0/AlgorithmsNeuroscience/f51b0b391c9d1d1c7bda2965cf0b934ed74b3142/datasets/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R14.edf -------------------------------------------------------------------------------- /docs/Gemfile: -------------------------------------------------------------------------------- 1 | source 'https://rubygems.org' 2 | 3 | group :jekyll_plugins do 4 | gem 'github-pages' 5 | gem 'jekyll-feed', '~> 0.6' 6 | 7 | # Textbook plugins 8 | gem 'jekyll-redirect-from' 9 | gem 'jekyll-scholar' 10 | end 11 | 12 | # Windows does not include zoneinfo files, so bundle the tzinfo-data gem 13 | gem 'tzinfo-data', platforms: [:mingw, :mswin, :x64_mingw, :jruby] 14 | 15 | # Performance-booster for watching directories on Windows 16 | gem 'wdm', '~> 0.1.0' if Gem.win_platform? 17 | 18 | # Development tools 19 | gem 'guard', '~> 2.14.2' 20 | gem 'guard-jekyll-plus', '~> 2.0.2' 21 | gem 'guard-livereload', '~> 2.5.2' 22 | -------------------------------------------------------------------------------- /docs/Guardfile: -------------------------------------------------------------------------------- 1 | guard 'jekyll-plus', serve: true do 2 | watch /.*/ 3 | ignore /^_site/ 4 | end 5 | 6 | guard 'livereload' do 7 | watch /.*/ 8 | end 9 | -------------------------------------------------------------------------------- /docs/Makefile: -------------------------------------------------------------------------------- 1 | .PHONY: help book clean serve 2 | 3 | help: 4 | @echo "Please use 'make ' where is one of:" 5 | @echo " install to install the necessary dependencies for jupyter-book to build" 6 | @echo " book to convert the content/ folder into Jekyll markdown in _build/" 7 | @echo " clean to clean out site build files" 8 | @echo " runall to run all notebooks in-place, capturing outputs with the notebook" 9 | @echo " serve to serve the repository locally with Jekyll" 10 | @echo " build to build the site HTML and store in _site/" 11 | @echo " site to build the site HTML, store in _site/, and serve with Jekyll" 12 | 13 | 14 | install: 15 | jupyter-book install ./ 16 | 17 | book: 18 | jupyter-book build ./ 19 | 20 | runall: 21 | jupyter-book run ./content 22 | 23 | clean: 24 | python scripts/clean.py 25 | 26 | serve: 27 | bundle exec guard 28 | 29 | build: 30 | jupyter-book build ./ --overwrite 31 | 32 | site: build 33 | bundle exec jekyll build 34 | touch _site/.nojekyll 35 | -------------------------------------------------------------------------------- /docs/_build/Tutorial_files/About.html: -------------------------------------------------------------------------------- 1 | --- 2 | redirect_from: 3 | - "/tutorial-files/about" 4 | interact_link: content/Tutorial_files/About.ipynb 5 | kernel_name: python3 6 | kernel_path: content/Tutorial_files 7 | has_widgets: false 8 | title: |- 9 | About Us 10 | pagenum: 15 11 | prev_page: 12 | url: /Tutorial_files/ApplyingMachineLearningMethodstoTemporalDimensions.html 13 | next_page: 14 | url: /Tutorial_files/Outline.html 15 | suffix: .ipynb 16 | search: learning research computational techniques com issues degree study university bern currently am working neuroscience machine www science article here github aath algorithmsneuroscience us pinar goektepe after completing bachelors computer engineering started masters asistant cognitive lab interests include macnine deep applications athina tzovara uses human cognition during phd developed predict outcome coma patients based neural responses sounds ncbi nlm nih gov pubmed moreover using modeling model physiological data relation decision making sciencedirect pii s viadihub associative journals plos org ploscompbiol id journal pcbi importantly throughout advocating open inclusive practices promoting diversity inclusivity elpiniki apostolaki iosifidou under construction contributing welcome new contributions 17 | 18 | comment: "***PROGRAMMATICALLY GENERATED, DO NOT EDIT. 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About Us
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About us

Pinar Goektepe

After completing my Bachelor's degree in computer engineering, I started my Master's degree study at University of Bern in 2018 and currently I am working as a research asistant at Computational Cognitive Neuroscience Lab at University of Bern. My research interests include macnine learning, deep learning and their applications in neuroscience.

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Athina Tzovara

My research uses computational techniques to study human cognition. During my PhD, I developed machine learning techniques to predict the outcome of coma patients based on their neural responses to sounds (https://www.ncbi.nlm.nih.gov/pubmed/23148350). Moreover, I have been using machine learning and computational modeling techniques to model physiological data, in relation to decision-making, or associative learning. Importantly, throughout my research, I am advocating open and inclusive science practices, promoting diversity and inclusivity.

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Elpiniki Apostolaki Iosifidou

under construction

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Contributing

We welcome new contributions and ideas! Here you can find the roadmap of this project and here the issues we're currently working on.

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52 | -------------------------------------------------------------------------------- /docs/_build/Tutorial_files/ComplexNumbers_temp.html: -------------------------------------------------------------------------------- 1 | --- 2 | redirect_from: 3 | - "/tutorial-files/complexnumbers-temp" 4 | interact_link: content/Tutorial_files/ComplexNumbers_temp.ipynb 5 | kernel_name: python3 6 | kernel_path: content/Tutorial_files 7 | has_widgets: false 8 | title: |- 9 | Complex Numbers 10 | pagenum: 2 11 | prev_page: 12 | url: /Tutorial_files/introToSP.html 13 | next_page: 14 | url: /Tutorial_files/Temporal_vs_Spectral.html 15 | suffix: .ipynb 16 | search: complex numbers please check following link github com aath algorithmsneuroscience blob master tutorials complexnumbers ipynb tutorial 17 | 18 | comment: "***PROGRAMMATICALLY GENERATED, DO NOT EDIT. SEE ORIGINAL FILES IN /content***" 19 | --- 20 | 21 |
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Complex Numbers
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Complex Numbers

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Please check the following link (Complex Numbers) to see Complex Numbers tutorial.

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Outline
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Tutorials folder

Here you can find the latest version of the tutorials we're currently working on.

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Introduction to Signal Processing
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Introduction to Signal Processing

Overview

The first part of this notebook will quickly go over several basic concepts from mathematics and physics that are important for signal processing. Nothing in this should be above college mathematics and physics, so if you feel confident you rember it, feel free to skip.

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  1. Complex numbers are fairly essential to understanding how sinusoids are represented in signal processing (and electronics in general). They are also a big help for visualising amplitude and phase of a signal.
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  2. Temporal Vs. Spectral Space will cover some concepts important to understanding what an electronic signal is and how to decompose it into it's components. The focus will lay on the Fourier series and transforms.
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  3. Acquiring Data will cover some of the basic concepts of data acquisition, such as the Nyquist-Shanon Theorem
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Machine Learning
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In this chapter several machine learning methods will be applied for eeg analysis.

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Preprocessing
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This chapter is about preprocessing of data.

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Fill it out with entries 4 | # for your own content. 5 | # 6 | # Each entry has the following schema: 7 | # 8 | # - title: mytitle # Title of chapter or section 9 | # url: /myurl # URL of section relative to the /content/ folder. 10 | # sections: # Contains a list of more entries that make up the chapter's sections 11 | # not_numbered: true # if the section shouldn't have a number in the sidebar 12 | # (e.g. Introduction or appendices) 13 | # expand_sections: true # if you'd like the sections of this chapter to always 14 | # be expanded in the sidebar. 15 | # external: true # Whether the URL is an external link or points to content in the book 16 | # 17 | # Below are some special values that trigger specific behavior: 18 | # - search: true # Will provide a link to a search page 19 | # - divider: true # Will insert a divider in the sidebar 20 | # - header: My Header # Will insert a header with no link in the sidebar 21 | # 22 | # See the links below for an example. 23 | 24 | 25 | # Top-level page 26 | - title: Background 27 | url: /Tutorial_files/Introduction 28 | not_numbered: false 29 | 30 | # Divider for meta-pages and content page 31 | - divider: true 32 | 33 | - title: Introduction to Signal Processing 34 | url: /Tutorial_files/introToSP 35 | not_numbered: false 36 | expand_sections: true 37 | sections: 38 | - title: Complex Numbers 39 | url: /Tutorial_files/ComplexNumbers_temp 40 | not_numbered: true 41 | - title: Temporal Vs. Spectral Space 42 | url: /Tutorial_files/Temporal_vs_Spectral 43 | not_numbered: true 44 | - title: Acquiring Data 45 | url: /Tutorial_files/AcquiringData 46 | not_numbered: true 47 | 48 | - divider: true 49 | 50 | - title: Preprocessing 51 | url: /Tutorial_files/preprocess 52 | not_numbered: false 53 | expand_sections: true 54 | sections: 55 | - title: Data Loading 56 | url: /Tutorial_files/DataLoading 57 | not_numbered: true 58 | - title: Data Visualization 59 | url: /Tutorial_files/DataVisualization 60 | not_numbered: true 61 | - title: Baseline Correction 62 | url: /Tutorial_files/BaselineCorrection 63 | not_numbered: true 64 | - title: Choosing a Reference 65 | url: /Tutorial_files/ReferencingforEEGdata 66 | not_numbered: true 67 | 68 | - divider: true 69 | 70 | - title: Machine Learning 71 | url: /Tutorial_files/ml 72 | not_numbered: false 73 | expand_sections: true 74 | sections: 75 | - title: Formatting a dataset 76 | url: /Tutorial_files/DatasetConstruction 77 | not_numbered: true 78 | - title: Single-Participant Analysis 79 | url: /Tutorial_files/ApplyingMachineLearningMethods_1 80 | not_numbered: true 81 | - title: Group-Level Analysis 82 | url: /Tutorial_files/ApplyingMachineLearningMethods_2 83 | not_numbered: true 84 | - title: Classication over time 85 | url: /Tutorial_files/ApplyingMachineLearningMethodstoTemporalDimensions 86 | not_numbered: true 87 | 88 | - divider: true 89 | 90 | - title: About Us 91 | url: /Tutorial_files/About 92 | not_numbered: false 93 | 94 | - divider: true 95 | 96 | - title: Outline 97 | url: /Tutorial_files/Outline 98 | not_numbered: false 99 | 100 | 101 | 102 | 103 | 104 | -------------------------------------------------------------------------------- /docs/_includes/buttons.html: -------------------------------------------------------------------------------- 1 |
2 | {% include buttons/download.html %} 3 | {% if page.interact_link %} 4 | {% include buttons/thebelab.html %} 5 | {% include buttons/nbinteract.html %} 6 | {% include buttons/binder.html %} 7 | {% include buttons/jupyterhub.html %} 8 | {% endif %} 9 |
10 | -------------------------------------------------------------------------------- /docs/_includes/buttons/binder.html: -------------------------------------------------------------------------------- 1 | {% if site.use_binder_button %} 2 | 3 | {% if site.use_jupyterlab %} 4 | {% assign binder_interact_prefix="urlpath=lab/tree/" %} 5 | {% else %} 6 | {% assign binder_interact_prefix="filepath=" %} 7 | {% endif %} 8 | 9 | {% capture interact_url_binder %}v2/gh/{{ site.binder_repo_org }}/{{ site.binder_repo_name }}/{{ site.binder_repo_branch }}?{{ binder_interact_prefix }}{{ page.interact_link | url_encode }}{% endcapture %} 10 | {% capture interact_icon_binder %}{{ site.images_url | relative_url }}/logo_binder.svg{% endcapture %} 11 | 12 | 13 | 14 | {%- endif %} -------------------------------------------------------------------------------- /docs/_includes/buttons/download.html: -------------------------------------------------------------------------------- 1 | {% if site.use_download_button -%} 2 |
3 | 4 |
5 | {% if page.interact_link -%} 6 | 7 | 8 | 9 | {% endif %} 10 | 11 |
12 |
13 | {%- endif %} 14 | -------------------------------------------------------------------------------- /docs/_includes/buttons/jupyterhub.html: -------------------------------------------------------------------------------- 1 | {% if site.use_jupyterhub_button %} 2 | 3 | {% if site.use_jupyterlab %} 4 | {% assign hub_app="lab" %} 5 | {% else %} 6 | {% assign hub_app="notebook" %} 7 | {% endif %} 8 | 9 | {% capture interact_url_jupyterhub %}hub/user-redirect/git-pull?repo={{ site.binder_repo_base }}/{{ site.binder_repo_org }}/{{ site.binder_repo_name }}&branch={{ site.binder_repo_branch }}&subPath={{ page.interact_link | url_encode }}&app={{ hub_app }}{% endcapture %} 10 | {% capture interact_icon_jupyterhub %}{{ site.images_url | relative_url }}/logo_jupyterhub.svg{% endcapture %} 11 | 12 | 13 | {% endif %} 14 | -------------------------------------------------------------------------------- /docs/_includes/buttons/nbinteract.html: -------------------------------------------------------------------------------- 1 | {% if site.use_show_widgets_button and page.has_widgets -%} 2 | 3 | {% endif %} -------------------------------------------------------------------------------- /docs/_includes/buttons/thebelab.html: -------------------------------------------------------------------------------- 1 | {% if site.use_thebelab_button -%} 2 | 3 | {% endif %} -------------------------------------------------------------------------------- /docs/_includes/css_entry.scss: -------------------------------------------------------------------------------- 1 | @import 'inuitcss/settings/settings.core'; 2 | @import 'settings/settings.global.scss'; 3 | 4 | @import 'inuitcss/tools/tools.font-size'; 5 | @import 'inuitcss/tools/tools.clearfix'; 6 | @import 'inuitcss/tools/tools.hidden'; 7 | @import 'inuitcss/tools/tools.mq'; 8 | 9 | @import 'inuitcss/elements/elements.page'; 10 | @import 'inuitcss/elements/elements.headings'; 11 | @import 'inuitcss/elements/elements.images'; 12 | @import 'inuitcss/elements/elements.tables'; 13 | @import 'elements/elements.typography'; 14 | @import 'elements/elements.syntax-highlighting'; 15 | @import 'elements/elements.tables'; 16 | @import 'elements/elements.links'; 17 | 18 | @import 'components/components.textbook__page'; 19 | -------------------------------------------------------------------------------- /docs/_includes/fb_tags.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /docs/_includes/footer.html: -------------------------------------------------------------------------------- 1 |
2 |

{{ site.footer_text }}

3 |
4 | -------------------------------------------------------------------------------- /docs/_includes/google_analytics.html: -------------------------------------------------------------------------------- 1 | {% if site.google_analytics.mytrackingcode %} 2 | 3 | 4 | 11 | {% endif %} 12 | -------------------------------------------------------------------------------- /docs/_includes/js/nbinteract.html: -------------------------------------------------------------------------------- 1 | {% if site.use_show_widgets_button and page.has_widgets %} 2 | 3 | 4 | 5 | 33 | {% endif %} -------------------------------------------------------------------------------- /docs/_includes/js/print.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 33 | -------------------------------------------------------------------------------- /docs/_includes/js/thebelab-cell-button.html: -------------------------------------------------------------------------------- 1 | {% if site.use_thebelab_button -%} 2 | 27 | {% endif %} 28 | -------------------------------------------------------------------------------- /docs/_includes/js/thebelab-page-config.html: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /docs/_includes/js/thebelab.html: -------------------------------------------------------------------------------- 1 | 2 | {% if site.use_thebelab_button %} 3 | 4 | 5 | {% include js/thebelab-cell-button.html %} 6 | 7 | 95 | {% endif %} 96 | -------------------------------------------------------------------------------- /docs/_includes/mathjax.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 29 | 30 | -------------------------------------------------------------------------------- /docs/_includes/metadata.json: -------------------------------------------------------------------------------- 1 | { 2 | "@context": "http://schema.org", 3 | "@type": "NewsArticle", 4 | "mainEntityOfPage": "{{ page.url | replace:'index.html','' | prepend: site.baseurl | prepend: site.url }}", 5 | "headline": "{% if page.title %}{{ page.title | escape }}{% else %}{{ site.title | escape }}{% endif %}", 6 | "datePublished": "{% if page.date %}{{ page.date | date_to_xmlschema }}{% else %}{{ site.time | date_to_xmlschema }}{% endif %}", 7 | "dateModified": "{% if page.date %}{{ page.date | date_to_xmlschema }}{% else %}{{ site.time | date_to_xmlschema }}{% endif %}", 8 | "description": "{{ page.content | strip_html | strip_newlines | truncate: 160 }}", 9 | "author": { 10 | "@type": "Person", 11 | "name": "{{ site.author }}" 12 | }, 13 | "publisher": { 14 | "@type": "Organization", 15 | "name": "Data 100 at UC Berkeley", 16 | "logo": { 17 | "@type": "ImageObject", 18 | "url": "{{ site.logo | prepend: site.baseurl | prepend: site.url }}", 19 | "width": 60, 20 | "height": 60 21 | } 22 | }, 23 | "image": { 24 | "@type": "ImageObject", 25 | "url": "{{ site.logo | prepend: site.baseurl | prepend: site.url }}", 26 | "height": 60, 27 | "width": 60 28 | } 29 | } 30 | -------------------------------------------------------------------------------- /docs/_includes/page-nav.html: -------------------------------------------------------------------------------- 1 | {% comment %} 2 | Only the URLs from the TOC are used here. The title for 3 | prev/next is pulled from the respective page's metadata. 4 | We loop through the "build" collection to determine the 5 | page title based on the *current* page's next/prev URL. 6 | {% endcomment %} 7 | 38 | -------------------------------------------------------------------------------- /docs/_includes/search/lunr/lunr-en.js: -------------------------------------------------------------------------------- 1 | var initQuery = function() { 2 | // See if we have a search box 3 | var searchInput = document.querySelector('input#lunr_search'); 4 | if (searchInput === null) { 5 | return; 6 | } 7 | 8 | // Function to parse our lunr cache 9 | var idx = lunr(function () { 10 | this.field('title') 11 | this.field('excerpt') 12 | this.field('categories') 13 | this.field('tags') 14 | this.ref('id') 15 | 16 | this.pipeline.remove(lunr.trimmer) 17 | 18 | for (var item in store) { 19 | this.add({ 20 | title: store[item].title, 21 | excerpt: store[item].excerpt, 22 | categories: store[item].categories, 23 | tags: store[item].tags, 24 | id: item 25 | }) 26 | } 27 | }); 28 | 29 | // Run search upon keyup 30 | searchInput.addEventListener('keyup', function () { 31 | var resultdiv = document.querySelector('#results'); 32 | var query = document.querySelector("input#lunr_search").value.toLowerCase(); 33 | var result = 34 | idx.query(function (q) { 35 | query.split(lunr.tokenizer.separator).forEach(function (term) { 36 | q.term(term, { boost: 100 }) 37 | if(query.lastIndexOf(" ") != query.length-1){ 38 | q.term(term, { usePipeline: false, wildcard: lunr.Query.wildcard.TRAILING, boost: 10 }) 39 | } 40 | if (term != ""){ 41 | q.term(term, { usePipeline: false, editDistance: 1, boost: 1 }) 42 | } 43 | }) 44 | }); 45 | 46 | // Empty the results div 47 | while (resultdiv.firstChild) { 48 | resultdiv.removeChild(resultdiv.firstChild); 49 | } 50 | 51 | resultdiv.insertAdjacentHTML('afterbegin', '

'+result.length+' Result(s) found

'); 52 | for (var item in result) { 53 | var ref = result[item].ref; 54 | if(store[ref].teaser){ 55 | var searchitem = 56 | '
'+ 57 | '
'+ 58 | '

'+ 59 | ''+store[ref].title+''+ 60 | '

'+ 61 | '
'+ 62 | ''+ 63 | '
'+ 64 | '

'+store[ref].excerpt.split(" ").splice(0,20).join(" ")+'...

'+ 65 | '
'+ 66 | '
'; 67 | } 68 | else{ 69 | var searchitem = 70 | '
'+ 71 | '
'+ 72 | '

'+ 73 | ''+store[ref].title+''+ 74 | '

'+ 75 | '

'+store[ref].excerpt.split(" ").splice(0,20).join(" ")+'...

'+ 76 | '
'+ 77 | '
'; 78 | } 79 | resultdiv.insertAdjacentHTML('beforeend', searchitem); 80 | } 81 | }); 82 | }; 83 | 84 | initFunction(initQuery); 85 | -------------------------------------------------------------------------------- /docs/_includes/search/lunr/lunr-store.js: -------------------------------------------------------------------------------- 1 | var store = [ 2 | {%- for c in site.collections -%} 3 | {%- if forloop.last -%} 4 | {%- assign l = true -%} 5 | {%- endif -%} 6 | {%- assign docs = c.docs | where_exp:'doc','doc.search != false' -%} 7 | {%- for doc in docs -%} 8 | {%- if doc.header.teaser -%} 9 | {%- capture teaser -%}{{ doc.header.teaser }}{%- endcapture -%} 10 | {%- else -%} 11 | {%- assign teaser = site.teaser -%} 12 | {%- endif -%} 13 | { 14 | "title": {{ doc.title | jsonify }}, 15 | {% assign truncateWords=site.search_max_words_in_content %} 16 | "excerpt": {{ doc.search | jsonify }}, 17 | "categories": {{ doc.categories | jsonify }}, 18 | "tags": {{ doc.tags | jsonify }}, 19 | "url": {{ doc.url | absolute_url | jsonify }}, 20 | "teaser": 21 | {%- if teaser contains "://" -%} 22 | {{ teaser | jsonify }} 23 | {%- else -%} 24 | {{ teaser | absolute_url | jsonify }} 25 | {%- endif -%} 26 | }{%- unless forloop.last and l -%},{%- endunless -%} 27 | {%- endfor -%} 28 | {%- endfor -%}] -------------------------------------------------------------------------------- /docs/_includes/topbar.html: -------------------------------------------------------------------------------- 1 |
2 | 3 |
4 | 12 | {% include buttons.html %} 13 |
14 | 15 | 20 | 21 | Search 22 | 23 |
24 | -------------------------------------------------------------------------------- /docs/_layouts/default.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | {% include head.html %} 4 | 5 | 6 | {% include js/thebelab-page-config.html %} 7 | 8 | 9 |
10 | {% include sidebar.html %} 11 | {% if page.search_page != true %} 12 | {% endif %} 13 | {% include topbar.html %} 14 |
15 |
16 | {{ content }} 17 |
18 |
19 | {% include page-nav.html %} 20 | {% include footer.html %} 21 |
22 | 23 |
24 | 25 | 26 | 27 | 28 | -------------------------------------------------------------------------------- /docs/_sass/components/_components.book__topbar.scss: -------------------------------------------------------------------------------- 1 | .c-topbar__label { 2 | @include inuit-font-size(12px); 3 | display: inline-block; 4 | margin-left: $spacing-unit-tiny; 5 | vertical-align: middle; 6 | text-transform: uppercase; 7 | } 8 | 9 | .c-topbar { 10 | .hamburger, .buttons { 11 | float: left; 12 | } 13 | 14 | #js-sidebar-toggle { 15 | margin-right: 5px; 16 | padding-top: 4px; 17 | } 18 | 19 | span.hamburger-box { 20 | width: 40px; 21 | height: 30px; 22 | padding-left: 10px; 23 | } 24 | 25 | .c-topbar__buttons { 26 | @include mq($from: tablet) { 27 | width: calc(100% - #{$right-sidebar-width} - 20px) 28 | } 29 | } 30 | 31 | .topbar-right-button { 32 | display: block; 33 | float: right; 34 | padding: 0 1rem; 35 | 36 | img { 37 | width: 20px; 38 | margin-top: 4px; 39 | } 40 | } 41 | } 42 | 43 | // Download buttons 44 | 45 | .download-buttons { 46 | display: none; 47 | position: absolute; 48 | 49 | button { 50 | min-width: 100px !important; 51 | border: 1px white solid !important; 52 | border-radius: 0 !important; 53 | } 54 | } 55 | 56 | .download-buttons-dropdown { 57 | position: relative; 58 | display: inline-block; 59 | 60 | &:hover div.download-buttons { 61 | display: block; 62 | } 63 | 64 | img { 65 | height: 18px; 66 | } 67 | } 68 | -------------------------------------------------------------------------------- /docs/_sass/components/_components.interact-button.scss: -------------------------------------------------------------------------------- 1 | /** 2 | * Stylings for Interact and Show Widget buttons. 3 | * 4 | * [1]: We abuse CSS selector specificity here since the buttons at the top of 5 | * the notebook might have both .interact-button and .js=nbinteract-widget. 6 | * [2]: We want the top buttons to be large. 7 | * [3]: However, a .js=nbinteract-widget appearing alone midway through the 8 | * notebook should be small. 9 | * 10 | */ 11 | 12 | $color-interact-button: #5a5a5a !default; 13 | 14 | %interact-button { 15 | @include inuit-font-size(14px); 16 | background-color: $color-interact-button; 17 | border-radius: 3px; 18 | border: none; 19 | color: white; 20 | cursor: pointer; 21 | display: inline-block; 22 | font-weight: 700; 23 | /* [2] */ 24 | padding: $spacing-unit-tiny $spacing-unit-med; 25 | text-decoration: none; 26 | 27 | &:hover, 28 | &:focus { 29 | text-decoration: none; 30 | } 31 | } 32 | 33 | .interact-button-logo { 34 | height: 1.35em; 35 | padding-right: 10px; 36 | margin-left: -5px; 37 | } 38 | 39 | .buttons { 40 | margin-bottom: $spacing-unit; 41 | 42 | /* [1] */ 43 | .interact-button { 44 | @extend %interact-button; 45 | } 46 | } 47 | 48 | .js-nbinteract-widget { 49 | @extend %interact-button; 50 | 51 | /* [3] */ 52 | padding: $spacing-unit-tiny $spacing-unit; 53 | margin-bottom: $spacing-unit-small; 54 | } 55 | 56 | // If the interact button link is changed with a REST param 57 | div.interact-context { 58 | display: inline; 59 | padding-left: 1em; 60 | } -------------------------------------------------------------------------------- /docs/_sass/components/_components.page__footer.scss: -------------------------------------------------------------------------------- 1 | .footer { 2 | text-align: center; 3 | font-size: 14px; 4 | padding: 20px; 5 | opacity: 0.7; 6 | margin-bottom: 0px; 7 | } 8 | -------------------------------------------------------------------------------- /docs/_sass/components/_components.page__nav.scss: -------------------------------------------------------------------------------- 1 | /** 2 | * Styling for the Next Page / Previous Page links at the bottom of textbook 3 | * pages. 4 | */ 5 | 6 | $color-nav-links: rgba(0, 140, 255, 0.7); 7 | 8 | .c-page__nav__prev, 9 | .c-page__nav__next { 10 | flex: 1; 11 | color: $color-nav-links; 12 | border: 1px solid $color-nav-links; 13 | border-radius: 3px; 14 | padding: $spacing-unit-small 0; 15 | } 16 | 17 | .c-page__nav__next { 18 | text-align: right; 19 | } 20 | -------------------------------------------------------------------------------- /docs/_sass/components/_components.page__onthispage.scss: -------------------------------------------------------------------------------- 1 | /** 2 | * Styling for the onthispage elements. 3 | * 4 | * [1]: The sidebar is implemented as ul and li elements so we need to remove 5 | * the bullets and margins. Also make chapter fonts a bit bigger. 6 | * [2]: The entries are tags so we need to remove the default styling. 7 | * [3]: The sidebar divider is just an empty element with a border. 8 | * [4]: The current section needs a higher specificity to override the :hover 9 | * selectors used previously. 10 | * [5]: The logo displayed above the sidebar 11 | * [6]: The footer at the bottom of the sidebar 12 | */ 13 | $color-sidebar-bg: rgba(255, 255, 255, 0) !default; 14 | $color-sidebar-entry: #364149 !default; 15 | $color-sidebar-entry--active: $color-links !default; 16 | $color-sidebar-divider: #bbb !default; 17 | 18 | .c-textbook__sidebar { 19 | background-color: $color-sidebar-bg; 20 | padding: $spacing-unit-small; 21 | 22 | @include inuit-font-size(14px); 23 | border-right: 1px solid rgba(0, 0, 0, 0.07); 24 | opacity: 0.6; 25 | -webkit-transition: opacity 0.2s ease-in-out; 26 | transition: opacity 0.2s ease-in-out; 27 | 28 | &:hover { 29 | opacity: 1; 30 | } 31 | } 32 | 33 | /* [1] */ 34 | .c-sidebar__chapters { 35 | list-style: none; 36 | margin-left: 0; 37 | margin-bottom: 0; 38 | } 39 | 40 | li.c-sidebar__chapter > a { 41 | font-size: 1.2em; 42 | } 43 | 44 | /* [1] */ 45 | .c-sidebar__sections, .c-sidebar__subsections { 46 | list-style: none; 47 | margin-bottom: 0; 48 | } 49 | 50 | .c-sidebar__sections { 51 | margin-left: $spacing-unit-small; 52 | } 53 | 54 | .c-sidebar__subsections { 55 | margin-left: 20px; 56 | } 57 | 58 | /* [2] */ 59 | .c-sidebar__entry { 60 | display: block; 61 | 62 | padding: $spacing-unit-tiny; 63 | 64 | color: $color-sidebar-entry; 65 | text-decoration: none; 66 | 67 | &:hover { 68 | text-decoration: underline; 69 | } 70 | 71 | &:visited { 72 | color: $color-sidebar-entry; 73 | } 74 | } 75 | 76 | /* [4] */ 77 | .c-sidebar__entry--active.c-sidebar__entry--active { 78 | color: $color-sidebar-entry--active; 79 | } 80 | 81 | /* [3] */ 82 | .c-sidebar__divider { 83 | border-top: 1px solid $color-sidebar-divider; 84 | margin: $spacing-unit-tiny; 85 | } 86 | 87 | /* [5] */ 88 | img.textbook_logo { 89 | margin-top: 20px; 90 | max-height: 100px; 91 | margin: 0px auto 20px auto; 92 | display: block; 93 | } 94 | 95 | /* [6] */ 96 | p.sidebar_footer { 97 | text-align: center; 98 | padding: 10px 20px 0px 0px; 99 | font-size: .9em; 100 | } -------------------------------------------------------------------------------- /docs/_sass/components/_components.search.scss: -------------------------------------------------------------------------------- 1 | /* ========================================================================== 2 | SEARCH 3 | ========================================================================== */ 4 | // Taken from https://github.com/mmistakes/minimal-mistakes 5 | // Variables 6 | $large: 1024px !default; 7 | $x-large: 1280px !default; 8 | $type-size-1: 2.441em !default; 9 | $type-size-2: 1.953em !default; 10 | $type-size-3: 1.563em !default; 11 | $type-size-6: 0.75em !default; 12 | $intro-transition: intro 0.3s both !default; 13 | 14 | // Rules 15 | .layout--search { 16 | .archive__item-teaser { 17 | margin-bottom: 0.25em; 18 | } 19 | } 20 | 21 | .search__toggle { 22 | margin-left: 1rem; 23 | margin-right: 1rem; 24 | border: 0; 25 | outline: none; 26 | color: #393e46; 27 | background-color: transparent; 28 | cursor: pointer; 29 | -webkit-transition: 0.2s; 30 | transition: 0.2s; 31 | 32 | &:hover { 33 | color: #000; 34 | } 35 | } 36 | 37 | .search-icon { 38 | width: 100%; 39 | height: 100%; 40 | } 41 | 42 | .search-content { 43 | //display: none; 44 | //visibility: hidden; 45 | padding-top: 1em; 46 | padding-bottom: 1em; 47 | 48 | &__inner-wrap { 49 | width: 100%; 50 | margin-left: auto; 51 | margin-right: auto; 52 | padding-left: 1em; 53 | padding-right: 1em; 54 | -webkit-animation: $intro-transition; 55 | animation: $intro-transition; 56 | -webkit-animation-delay: 0.15s; 57 | animation-delay: 0.15s; 58 | 59 | .search-input { 60 | display: block; 61 | margin-bottom: 0; 62 | padding: 0; 63 | border: none; 64 | outline: none; 65 | box-shadow: none; 66 | background-color: transparent; 67 | font-size: $type-size-3; 68 | } 69 | } 70 | 71 | &.is--visible { 72 | display: block; 73 | visibility: visible; 74 | 75 | &::after { 76 | content: ""; 77 | display: block; 78 | } 79 | } 80 | 81 | .results__found { 82 | margin-top: 0.5em; 83 | font-size: $type-size-6; 84 | } 85 | 86 | .archive__item { 87 | margin-bottom: 2em; 88 | } 89 | 90 | .archive__item-title { 91 | margin-top: 0; 92 | } 93 | 94 | .archive__item-excerpt { 95 | margin-bottom: 0; 96 | } 97 | } -------------------------------------------------------------------------------- /docs/_sass/components/_components.thebelab.scss: -------------------------------------------------------------------------------- 1 | 2 | .thebelab-cell { 3 | // To ensure that thebelab cells are always the top of the Z-stack 4 | position: relative; 5 | z-index: 999; 6 | } 7 | 8 | .thebelab-button { 9 | z-index: 999; 10 | display: inline-block; 11 | padding: 0.35em 1.2em; 12 | margin: 0px 1px; 13 | border-radius: 0.12em; 14 | box-sizing: border-box; 15 | text-decoration: none; 16 | font-family: 'Roboto', sans-serif; 17 | font-weight: 300; 18 | text-align: center; 19 | transition: all 0.2s; 20 | background-color: #dddddd; 21 | border: 0.05em solid white; 22 | color: #000000; 23 | } 24 | 25 | .thebelab-button:hover{ 26 | border: 0.05em solid black; 27 | background-color: #fcfcfc; 28 | } 29 | 30 | 31 | div.jp-OutputArea-output { 32 | padding: 5px; 33 | } 34 | -------------------------------------------------------------------------------- /docs/_sass/hamburgers/_base.scss: -------------------------------------------------------------------------------- 1 | // Hamburger 2 | // ================================================== 3 | .hamburger { 4 | padding: $hamburger-padding-y $hamburger-padding-x; 5 | display: inline-block; 6 | cursor: pointer; 7 | 8 | transition-property: opacity, filter; 9 | transition-duration: $hamburger-hover-transition-duration; 10 | transition-timing-function: $hamburger-hover-transition-timing-function; 11 | 12 | // Normalize (