Open Cognitive Science
Collaboration drives scientific progress. I build open methods for cognitive science research — tools, techniques, and infrastructure that others can reuse to create, implement, and share their own work. Inspired and educated by projects like the Open Science Framework and the Turing Way, I have personally benefited from the open science community and I am convinced of its value, especially for under-resourced laboratories and institutions. I contribute across the whole pipeline: experimental design, the technology that runs the experiments, the analysis methods, the languages that implement them, and the tools to communicate and share the results.
About me
My name is Luis Alberto Barradas Chacón. I am a PhD candidate at TU Graz. I am a certified mindfulness facilitator and a member of the Mexican Society for Bio- and Neurofeedback, as well as the Mexican Biosignals group. This webpage collects some of my work as an engineer, behavioral data analyst, statistician, and cognitive scientist.
Experimental design
I have been programming cognitive experiments since my first training in Biofeedback and Neurofeedback in 2010. I have learned to use NBS Presentation and E-Prime, but have found that the most diverse and stable solution for most of my projects has always been achieved with PsychoPy and OpenVIBE. Both are open source and have large communities of users and developers. Designs like the Attention Network Test (ANT), the N-Back task, and the Random Dot Motion Task (RDM), among others, can be found freely available on my GitHub.
Technology for Cognitive Science
A good solution for my lab is often a good solution for others. I rely on Lab Streaming Layer (LSL) for cognitive experiments that require synchronized measurement of multiple signals. With it, open standards like the Brain Imaging Data Structure (BIDS) and the Extensible Data Format (XDF) form part of the data management and sharing solutions I propose. These implementations allow a large number of devices to be used in the same experiment, and for the data to be shared and analyzed in a standardized way.
Data analysis
Open source is second nature in software development. The world of computing just wouldn’t exist as it is without it. The NumFOCUS project knows that. Throughout my career, they have been a great source of inspiration and support. I use the Python scientific stack and the Julia programming language for all my data analysis. I have found great use in MNE-Python and PyTorch for my signal processing and deep learning needs.
Scientific communication
Scientific communication underpins everything else I do. I have used Jupyter notebooks throughout my entire higher education. Most recently, Quarto has fulfilled my needs for a more robust and reproducible scientific communication tool. Together with these, git and GitHub have become strong tools for sharing and collaborating on my projects. This of course does not replace personal communication, in the form of courses, workshops, and conferences, but it does make it easier to share and collaborate in different languages and with continuously changing versions.
Science is a human right
Scientific knowledge is a common good. The right mindset and tools can make its discovery, management, and sharing available to everyone. My work is one attempt to put that principle into practice — and to help others do the same.