biology

NeuroKit

NeuroKit is a biology topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand NeuroKit rather than just read about it. In short: NeuroKit ("nk") is an open source toolbox for physiological signal processing. The most recent version, NeuroKit2, is written in Python and is available from the PyPI package repository.

Key takeaways

NeuroKit belongs to biology; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect NeuroKit to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of NeuroKit from memory before moving on to harder problems.

Reference excerpt

NeuroKit ("nk") is an open source toolbox for physiological signal processing. The most recent version, NeuroKit2, is written in Python and is available from the PyPI package repository. As of June 2022, the software was used in 94 scientific publications. NeuroKit2 is presented as one of the most popular and contributor-friendly open-source software for neurophysiology based on the number of downloads, the number of contributors, and other GitHub metricsa.

History The first version of NeuroKit was created as a PhD side-project of Dominique Makowski in 2017. It was officially deprecated in 2020 and has been replaced by the current version, NeuroKit2. A few major updates have been released since:

February 08, 2021: The 0.1.0 release coincides with the first publication of the software. May 18, 2022: The 0.2.0 release coincides with an overhaul of the documentation. NeuroKit has received the 2024 Commendation Award from the Society for the Improvement of Psychological Science (SIPS).

Features NeuroKit2 includes tools to work with cardiac activity from electrocardiography (ECG) and photoplethysmography (PPG), electrodermal activity (EDA), respiratory (RSP), electromyography (EMG), and electrooculography (EOG) signals. It enables the computation of Heart Rate Variability (HRV) and Respiratory Variability (RRV) metrics. It also implements a variety of different algorithms to detect R-peaks and other QRS waves, including an efficient in-house R-peak detector. For neurophysiological signals such as EEG, it supports microstates and frequency band analysis. It also includes a comprehensive set of functions used for fractal physiology, allowing the computation of various measures of complexity (including entropy and fractal dimensions).

Design The software was designed to be accessible to users without programming experience, with the possibility of using high-level functions to run entire preprocessing or analysis routines.

Worked examples

Example 1 — a first encounter with NeuroKit

Start with the simplest possible case. Write down what NeuroKit claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In biology, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to NeuroKit before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about NeuroKit ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of NeuroKit

In research: NeuroKit appears in biology research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses NeuroKit in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: NeuroKit is common in secondary-school and first-year university syllabi. It links to neighbouring topics Neuroimaging software, Python (programming language) scientific libraries, Software using the MIT license, so understanding it makes those chapters shorter.
In everyday life: Look for NeuroKit outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study NeuroKit in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what NeuroKit means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain NeuroKit out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is NeuroKit in simple terms?

NeuroKit ("nk") is an open source toolbox for physiological signal processing. The most recent version, NeuroKit2, is written in Python and is available from the PyPI package repository.

Why does NeuroKit matter?

Because it connects several biology ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study NeuroKit?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on NeuroKit.