cortex-example VS moabb

Compare cortex-example vs moabb and see what are their differences.

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cortex-example moabb
1 1
180 619
1.7% 2.7%
5.3 8.6
5 months ago 5 days ago
C# Python
MIT License BSD 3-clause "New" or "Revised" License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

cortex-example

Posts with mentions or reviews of cortex-example. We have used some of these posts to build our list of alternatives and similar projects.
  • EEG headset for measuring cognitive engagement?
    1 project | /r/BCI | 29 Nov 2022
    Emotiv does offer power band at 8Hz for free https://emotiv.gitbook.io/cortex-api/data-subscription. They also provide example code on their GitHub https://github.com/Emotiv/cortex-v2-example. As long as you are familiar with one of those coding languages, setup is fast after reading through the README. But if you are requiring something be easy to learn in under 3 minutes then you might want to consider other options.

moabb

Posts with mentions or reviews of moabb. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing cortex-example and moabb you can also consider the following projects:

brainflow - BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors

rustymind - A driver, parser and real time plotter for NeuroSky MindWave EEG headset

MI-EEG-1D-CNN - A new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a 'baseline' class) using a data augmentation algorithm and a limited number of EEG channels. Paper: https://doi.org/10.1088/1741-2552/ac4430

EEGwithRaspberryPI - Open-Source board for converting RaspberryPI to Brain-computer interface