Python | Reality Bending Lab

About signal complexity

Mon, 05 Dec 2022 00:00:00 +0000

The signals recorded from the brain or the body are rich in information, and there are many ways to analyze them. For instance, for EEG, one can focus on Event Related Potentials (ERP), time-frequency analyses, microstates, etc.

An alternative framework, used to characterize the general characteristics of the signal, relies on the extraction of indices of “complexity” (a general term for constructs such as entropy, chaos, fractal dimension, predictability). However, that field is quite complex (no pun intended), drawing heavily onto mathematical concepts that psychologists or neuroscientists might not be familiar with.

In order to better understand the world of complexity indices as applied to neurophysiology, we have done some groundwork to help us make better decisions in our future usage of this type of analysis.

A gentle introduction to complexity indices applied to neuroscience.
A indices selection guide in which we compare how different indices relate to one another.

Additionally, we also provide an easy way to compute them in Python in our NeuroKit package (see here for the list of functions and here for an EEG application).

NeuroKit2 0.2.0 is out 🎉

Wed, 18 May 2022 00:00:00 +0000

NeuroKit2 0.2.0 is out! 🎉

What was supposed to be a small release turned out in a massive update. A big thanks - and a warm welcome - to An Shu and Max, the newest member of the Reality Bending Team, and thus maintainers of NeuroKit. They worked massively to update all of the examples and docstrings. New features include:

A BRAND NEW WEBSITE with a revamped documentation, now hopefully much more useful to navigate. Check-it out: https://neuropsychology.github.io/NeuroKit/ and let us know what you think!!
An overhaul of the Complexity Indices: With more than a 100 indices, NeuroKit is now the most comprehensive package to quantify chaos, entropy and fractal dimension of signals.
Tons of other improvements and fixes ☺️

Once again, a big thanks to all the contributors for their help in making NeuroKit an awesome open-source software for physiological signal processing!

Podcast 'Learn Bayesian Stats' with Dominique Makowski

Tue, 01 Feb 2022 00:00:00 +0000

The ‘Learning Bayesian Statistics’ Podcast

I had the chance of being invited to talk about R, Python, Reality Bending and much more! It was my first experience of that kind, so thanks a ton to the host of the podcast Alex Andorra. Listen to it here:

https://www.learnbayesstats.com/episode/55-neuropsychology-illusions-bending-reality-dominique-makowski

Pyllusion has been published

Tue, 30 Nov 2021 00:00:00 +0000

Pyllusion has been published!

Pyllusion is a package for Python that implements a systematic way to manipulate and generate illusions using a set of parameters.

For instance, the famous Müller-Lyer illusion below, which causes the observer to perceive the 2 segments of being different lengths depending on the shape of the arrows, can be generated wit the following lines of code:

mullerlyer = pyllusion.MullerLyer(illusion_strength=30)
mullerlyer.to_image()

To understand more about the parametric approach being implemented in the Pyllusion package, we recommend reading our paper, which includes a hands-on example on how to generate some classic illusions (such as the Delbeouf Illusion), and discusses how Pyllusion contributes to address conceptual and methodological issues in illusion science.

import pyllusion

delboeuf = pyllusion.Delboeuf(illusion_strength=3)
delboeuf.to_image()

Don’t forget to keep a look out for our repo for more exciting open-source projects!

NeuroKit2 0.1.5 'Complexity' is out 🎉

Fri, 12 Nov 2021 00:00:00 +0000

NeuroKit2 0.1.5 is out! 🎉

In the latest 0.1.5 release of NeuroKit2, our team has fixed several bugs in existing functionalities and in particular, overhauled the support for computing complexity measures of neurophysiological signals. We added a ton of new indices of entropy and fractal dimensions, including:

Petrosian’s, Katz’s and Sevcik fractal dimension
Differential, Permutation, Spectral, SVD entropy
Fisher information
Hjorth’s and Lempel-Ziv’s complexity
Relative Roughness
Hurst and Lyapunov exponent(s)
Detrended Fluctuation Analysis (as well as MFDFA)
…

You can compute them all using the new nk.complexity() function!

import neurokit2 as nk

signal = nk.signal_simulate(frequency=[5, 6], noise=0.5)

results, info = nk.complexity(signal, which="fast")
results

  DiffEn       FI    Hjorth       KFD  PEn  ...      PFD        RR       SFD
1.536573  0.01524  1.355543  4.720953  1.0  ... 1.017423  1.638357  1.691036

To understand more about complexity science, we recommend reading our preprint, which introduces the theoretical (and mathematical) meanings of complexity and reviews the existing studies of complexity analysis across multiple fields of psychology.

Of course, the NeuroKit2 Python package also includes tons of other useful features for physiological signal processing (see this quick example)!

Don’t forget to watch our repo to keep a look out for more complexity functionalities coming up! 👀

R or Python for Psychologists

Fri, 22 May 2020 00:00:00 +0000

Many psychology students or researchers are faced with the challenge - or the opportunity - of learning a programming language. Which one should you learn?

As an ex- psych student and a daily user and developer of some of them, here’s my take on this hot debate.

What has programming to do with psychology?

If you’re a very young psychology student, or a future one, you might wonder: why the heck would I have to learn programming in psychology? “Psychology is kinda like philosophy, it’s just learning how people’s minds work by reading books and overthinking stuff”. If you still think that, you’re in for one hell of a ride!

Psychology is, since its very beginning, a hard and experimental science. The founding fathers of psychology were dedicated to find ways to objectively measure psychological phenomena and uncovering the mathematical laws that govern Human behaviour (one of the fields of psychology is even called psychophysics). True, this sciency nature has been toned down by the booming popularity of pseudo-scientific approaches like psychoanalysis throughout the 20th century, that contributed to the stereotypical public image of the shrink doodling while listening to a neurotic patient. But that’s a distorted and old-fashioned view, not really representative of the future of psychology.

The fact is that psychology is very closely connected with statistics. Many great statistical advances were made by psychologists, and all true psychological discoveries are backed by statistical findings. And this importance of statistics is - and will be - growing further, partly due to the recent realization of some major issues in the field due to improper statistical procedures (coined the “replicability crisis”). Moreover, psychology is more and more relying on advanced data-acquiring methods (smartphone apps, website behaviour data, online surveys, physiological and brain recording devices like EEG, MRI, etc.). And these new formats often require specialized knowledge (web-scraping, database querying, neuroimaging, signal processing, machine learning, …). And with great data-power comes great data-analysis-responsibilities. Even in the most applied kind of clinical or psychotherapeutic specializations, where you’d think you’d be safe, they are starting to use data intensive methods like neuro-feedback, virtual reality, experience sampling, and other forms of smartphone sensing and surveying.

Long story short, no matter which branch of psychology you specialize in, you will be confronted with some technical aspects that won’t be able to solve with Excel. Moreover, these technical skills are the ones that will make the most difference between students, and that will matter a lot if you want to pursue research or want to go work in the private sector. The golden era where people were recruited in research based on their theoretical expertise is over: technical skills are now the golden ticket to enter - and successfully leave - academia.

So, ready to dive into programming? Fear not! It’s not that complicated. Moreover, it’s one of the most rewarding skill you can develop. I can assure you that you won’t regret the time invested in learning it 😊

But where should you start?

Learn both R and Python

This increasing relationship between psychology and statistics on the one hand, and other more general technical aspects on the other, is the reason why R and Python are so popular in psychology. Both languages are free, open-source, suited for beginners, and have a large base of users with a ton of learning material online. What’s the difference between them?

Put simply, R is for statistics, Python is for the rest.

So why is there a virulent debate going on, and a choice to make? It’s true that I, in theory, would agree with some popular recommandations and suggest learning both, as they are complementary and have their own strengths and weaknesses. I myself use both on a daily basis, so why would preach what I practice?

That said, many opinionated people are also arguing in favour of one or the other (usually the only one they know…) will say that learning both is essentially a waste of time. They will put forth a strong argument: you can do whatever you do in R in Python, and vice-versa. In other words, both languages can be used to achieve your goals, so it’s better to specialize in one than have a limited knowledge of both.

Although I do not agree with that statement, I do acknowledge that people have limited time and resources to learn. Saying “just learn both” is easy, but is arguably an unrealistic expectation for the vast majority of people. So why learning both can be a long-term goal (especially if you want to do research), you have to start somewhere. So, what starter language should you select?

Ash choosing his starter programming language. He has the choice between R, Python and Bulbasaur, i.e, Matlab - the one that no one likes.

What about Matlab?

There was a time when Matlab was the boss. It was used everywhere and had the best functionalities for neuroimaging, signal processing and maths. But that time is over. Matlab is already a zombie language, which burial process will continue in the years to come. Why is it dead? Because it is expensive, closed, ugly, and most importantly because the alternatives (namely R and Python) are now more powerful and featured than Matlab.

Agamemnon reacting to king Priam saying "The city of Matlab will never be conquered".

The truth is, there are only two reasons people still use Matlab: habit (it’s hard to learn a new approach if your old way of doing things still works) and SPM (a toolbox for neuroimaging that is still - for now - the leader in the field).

But seriously, don’t waste time on it if you have limited resources, it’s just not worth it. You will learn an outdated tool that you won’t be able to use in another lab if they don’t agree to pay for an expensive license (unless you’re a pirate ☠️). Whereas with open and free languages like R or Python, you have access to the best tools and can use them freely everywhere. Also, it makes you a supporter of open-science, which is good 😁.

How to decide between R and Python

Time has come to make a decision.

Despite what people say, R and Python are not equivalent. You can argue as much as you want, but doing statistics and data visualization in Python is not as fast, easy and neat as it is in R. And signal processing or neuroimaging is not as powerful in R as compared to Python. Note that both languages are still growing and changing, and they are influencing themselves: for instance, many popular Python modules (e.g., pandas, statsmodels, seaborn, …) have been directly inspired by R. As such, the boundaries between the two languages are fading (and I’m not even mentioning the great advances in interoperability, with tools like reticulate that allow you to use one language directly inside the other).

That being said, Python and R remain very different languages at their core, with a different feel and vibe to it. R was made by statisticians for statistics, and the majority of its users are academics and researchers. On the contrary, Python is a true general-purpose “programming” language, widely used outside of academia, in the private sector.

Here are some things to consider when deciding on what language to learn:

Reasons to choose Python

You have some basic knowledge or familiarity with programming

For instance, you know what a logical loop is. Python being a true programming language, having any prior experience will be useful.

You are good with logic and spatial representation (like imagining shapes in 3D, rotating them, etc.)

In Python, you will have to think with a “programming” mindset. That means perceiving things in terms of logical statements and blocks, understanding data as 2D or 3D tables that you have to slice and recombine.

You are comfortable with maths

In Python, numbers and numbers combinations are used a lot. Paradoxically, you will typically see a lot more maths in Python than in R.

You plan to do signal processing or experimental tasks creation

These are some of the domains where Python is well-established (which doesn’t mean that R doesn’t have some great tools in development).

You are good at googling and don’t mind spending time looking for the right answer

Python has so much material online that it’s sometimes hard to find the appropriate thing. Harder than in R, in my opinion, which has more well-defined “gold-standard” textbooks and tutorials.

Reasons to choose R

You have no experience with programming whatsoever

R is not made to be used as a traditional programming language. It’s more of finding what functions to apply to what, and that makes it easy for beginners.

You are interested in statistical analyses, modelling things, and making inferences from data

R excels at this. You can create powerful models super easily and jump into their understanding and interpretation.

You like making nice figures and plots

R, through the ggplot ecosystem, has hands down the best tools for visualization. Your imagination is the limit, and you can even create art (check-out the artworks by Thomas Lin Pedersen and Danielle Navarro 😍).

You are not so good with stats or maths

You heard it right! To start with R you don’t need to know stats or maths like a boss. R, in fact, will help you to become proficient at it, by slowly opening more and more layers of complexity to you, if you are deemed worthy.

You are interested in joining the academic community

Because most of its users are academics, R has a fantastic community online, for instance on Twitter #rstats. It’s also super inclusive (e.g., the R-Ladies).

Other considerations

What your peers are learning

It’s easier to learn together, so try to discuss it with your class or lab mates if you can.

What your lab is using

It might be easier if you have mentors that can understand what you are doing and guide you. But that should not be a priority, as it can lead to cargo cult-like old habits reproduction (especially if your lab has a tradition of Matlab 🤭). Instead of submitting to the tradition, assess what the goals and objectives are, and pick the best tool to achieve them. And if you have any issue convincing your boss / supervisor about it, ask some help on Twitter, I bet you’ll get a lot of it.

Hands on!

👉 Looking for places to start? Check out this 10-min crash course introduction to Python and this collection of resources for R.

Thanks for reading! 🐦 Don’t forget to join me on X (@Dom_Makowski) and leave a comment below 👇

One Python code line for a Mandelbrot fractal

Sat, 16 May 2020 00:00:00 +0000

Mandelbrot Set

I wrote a small Python function to easily generate and plot a Mandelbrot set. This function is now available through the NeuroKit2 package, and can be used as follows:

import neurokit2 as nk

nk.fractal_mandelbrot(show=True)

The Mandelbrot set is defined in the between -2 and 2 on the x (real) and y (imaginary) axes. Following that, the image can be cropped accodingly by changing the coordinates. Moreover, the colors can be tweaked by changing the the colormap (cmap).

m = nk.fractal_mandelbrot(real_range=(-2, 0.75), imaginary_range=(-1.25, 1.25))
plt.imshow(m.T, cmap="viridis")
plt.axis("off")
plt.show()

Buddhabrot Set

It is also possible to generate a Buddhabrot:

b = nk.fractal_mandelbrot(size=1500,
                          real_range=(-2, 0.75), imaginary_range=(-1.25, 1.25),
                          buddha=True, iterations=200)
plt.imshow(b.T, cmap="gray")
plt.axis("off")
plt.show()

Added the option to return a so-called 'Buddhabrot'🧘 Amazing to see these shapes emerging from such a simple formula 🤯 #fractalart pic.twitter.com/7nzxsvQa6R
— Dominique Makowski 🧙 (@Dom_Makowski) May 7, 2020

Althoug the NeuroKit Python package is primarily devoted at physiological signal processing, in also includes tons of other useful features.

👉 Discover more about NeuroKit here 👈

Have fun!

Thanks for reading! Do not hesitate to tweet and share this post, and leave a comment below 🤗

🐦 Don’t forget to join me on X @Dom_Makowski

Generate an articial ECG signal in Python

Fri, 17 May 2019 00:00:00 +0000

Create a natural ECG signal

Generating artificial physiological signals can be very useful to build, test your analysis pipeline or develop and validate a new algorithm.

Generating a synthetic, yet realistic, ECG signal in Python can be easily achieved with the ecg_simulate() function available in the NeuroKit2 package.

In the example below, we will generate 8 seconds of ECG, sampled at 200 Hz (i.e., 200 points per second) - hence the length of the signal will be 8 * 200 = 1600 data points. We can also specify the average heart rate, although note that there will be some natural variability (which is a good thing, because it makes it realistic).

import neurokit2 as nk  # Load the package

simulated_ecg = nk.ecg_simulate(duration=8, sampling_rate=200, heart_rate=80)

nk.signal_plot(simulated_ecg, sampling_rate=200)  # Visualize the signal

The simulation is based on the ECGSYN algorithm (McSharry et al., 2003).

However, for fast and stable results (as the realistic algorithm naturally generates some variability), one can approximate the QRS complex by a Daubechies wavelet. An ECG based on this method can also be obtained in NeuroKit by changing the method as follows:

simulated_ecg = nk.ecg_simulate(duration=8, sampling_rate=200, method="daubechies")

nk.signal_plot(simulated_ecg, sampling_rate=200)

While faster and stable, the generated ECG is far from being realistic.