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| − | + | '''[https://www.medusabci.com/ MEDUSA©] is a Python-based open-source software ecosystem to facilitate the creation of brain-computer interface (BCI) systems and neuroscience experiments''' <ref>Eduardo Santamaría-Vázquez, Víctor Martínez-Cagigal, Diego Marcos-Martínez, Víctor Rodríguez-González, Sergio Pérez-Velasco, Selene Moreno-Calderón, Roberto Hornero, MEDUSA©: A novel Python-based software ecosystem to accelerate brain-computer interface and cognitive neuroscience research. Computer Methods and Programs in Biomedicine, 2023, DOI: [https://doi.org/10.1016/j.cmpb.2023.107357 doi.org/10.1016/j.cmpb.2023.107357].</ref>. The software boasts a range of features, including complete compatibility with lab streaming layer, a collection of ready-made examples for common BCI paradigms, extensive tutorials and [https://docs.medusabci.com/ documentation], an accessible online app marketplace, and a robust modular design, among others. | |
| + | |||
| + | == Software architecture design == | ||
| + | [https://www.medusabci.com/ MEDUSA©] comprises a modular design composed of three main independent entities: | ||
| + | |||
| + | # '''[https://www.medusabci.com/solutions/medusa-platform/ MEDUSA© Platform]''': the platform is a Python-based user interface for visualizing biosignals and conducting real-time experiments. Primarily built on PyQt, it offers straightforward installation via [https://www.medusabci.com/ binaries] or execution through the [https://github.com/medusabci/medusa-platform source code]. Real-time visualization is fully customizable, encompassing temporal graphs, power spectral density graphs, as well as power-based and connectivity-based topoplots. By using an user management system, the platform allows users to install and develop apps directly linked to their accounts. | ||
| + | # '''[https://www.medusabci.com/solutions/medusa-kernel/ MEDUSA© Kernel]''': the kernel stands as an independent [https://pypi.org/project/medusa-kernel/ PyPI package] that encapsulates all classes and functions required to record and process the biosignals of the experiments. While employed by the MEDUSA© Platform for real-time processing, the kernel holds the versatility to be installed as a Python package in any local Python project. These encompass linear and non-linear temporal methods, spectral metrics, statistical analysis, as well as specialized algorithms for electroencephalogram (EEG) and magnetoencephalogram (MEG) data, alongside state-of-the-art processing algorithms to process many BCI control signals. A comprehensive list of the processing algorithms can be found in the [https://docs.medusabci.com/kernel documentation]. | ||
| + | # '''[https://www.medusabci.com/market/ App marketplace]''': the MEDUSA© website provides users with the capability to create and manage a profile within the ecosystem. Within the app marketplace, users can explore and download open-source apps or contribute their own creations. User-developed apps may be designated as public or private, with accessibility options for selected individuals. Presently, MEDUSA© offers a comprehensive range of BCI paradigms, encompassing c-VEP, ERP, P300-based spellers, motor imagery (MI), and neurofeedback (NF). Additionally, it includes a variety of cognitive psychology tests such as the [[wikipedia:Stroop_effect|Stroop task]], [[wikipedia:Go/no-go|Go/No-Go test]], Dual [[wikipedia:N-back|N-back test]], [[wikipedia:Corsi_block-tapping_test|Corsi Block-Tapping test]], and more. | ||
| + | |||
| + | == Main features == | ||
| + | The main features of MEDUSA© can be summarized as follows: | ||
| + | |||
| + | * Open-source: MEDUSA© is an open-source project, allowing users to freely access and modify the code to suit their research needs. Furthermore, the kernel can be used in any custom Python project to load biosignals recorded by MEDUSA© and/or to use the processing algorithms included in the ecosystem. | ||
| + | * Full Python-based: the platform is constructed entirely using Python, a high-level programming language. Applications such as BCI paradigms and neuroscience experiments can be developed within Python using PyQt, or in any other programming language by utilizing a built-in TCP/IP asynchronous protocol to communicate with the platform. Notably, many publicly available apps are developed in Unity. This choice was driven by specific requirements, such as precise synchronization between EEG and stimuli for paradigms like c-VEP, or to enhance user experience through visually appealing designs, as seen in MI and NF based apps. | ||
| + | * LSL compatible: MEDUSA© can record and process signals streamed through the [https://labstreaminglayer.org/#/ lab streaming layer] (LSL) protocol, making it compatible with a wide range of biomedical devices. | ||
| + | * Modular design: the software is composed of (1) the platform (user interface and signal management) and (2) the kernel (a PyPI package with processing functions and classes), providing a modular and scalable framework. | ||
| + | * Control signals: the ecosystem also includes pre-built examples for various BCI paradigms, such as P300, c-VEP, SMR, and NF applications, streamlining the development process for researchers. | ||
| + | |||
| + | == Supported BCI paradigms == | ||
| + | (in progress) | ||
| + | |||
==Links== | ==Links== | ||
[https://www.medusabci.com/ Website] | [https://www.medusabci.com/ Website] | ||
Revision as of 15:27, 11 April 2024
MEDUSA© is a Python-based open-source software ecosystem to facilitate the creation of brain-computer interface (BCI) systems and neuroscience experiments [1]. The software boasts a range of features, including complete compatibility with lab streaming layer, a collection of ready-made examples for common BCI paradigms, extensive tutorials and documentation, an accessible online app marketplace, and a robust modular design, among others.
Software architecture design
MEDUSA© comprises a modular design composed of three main independent entities:
- MEDUSA© Platform: the platform is a Python-based user interface for visualizing biosignals and conducting real-time experiments. Primarily built on PyQt, it offers straightforward installation via binaries or execution through the source code. Real-time visualization is fully customizable, encompassing temporal graphs, power spectral density graphs, as well as power-based and connectivity-based topoplots. By using an user management system, the platform allows users to install and develop apps directly linked to their accounts.
- MEDUSA© Kernel: the kernel stands as an independent PyPI package that encapsulates all classes and functions required to record and process the biosignals of the experiments. While employed by the MEDUSA© Platform for real-time processing, the kernel holds the versatility to be installed as a Python package in any local Python project. These encompass linear and non-linear temporal methods, spectral metrics, statistical analysis, as well as specialized algorithms for electroencephalogram (EEG) and magnetoencephalogram (MEG) data, alongside state-of-the-art processing algorithms to process many BCI control signals. A comprehensive list of the processing algorithms can be found in the documentation.
- App marketplace: the MEDUSA© website provides users with the capability to create and manage a profile within the ecosystem. Within the app marketplace, users can explore and download open-source apps or contribute their own creations. User-developed apps may be designated as public or private, with accessibility options for selected individuals. Presently, MEDUSA© offers a comprehensive range of BCI paradigms, encompassing c-VEP, ERP, P300-based spellers, motor imagery (MI), and neurofeedback (NF). Additionally, it includes a variety of cognitive psychology tests such as the Stroop task, Go/No-Go test, Dual N-back test, Corsi Block-Tapping test, and more.
Main features
The main features of MEDUSA© can be summarized as follows:
- Open-source: MEDUSA© is an open-source project, allowing users to freely access and modify the code to suit their research needs. Furthermore, the kernel can be used in any custom Python project to load biosignals recorded by MEDUSA© and/or to use the processing algorithms included in the ecosystem.
- Full Python-based: the platform is constructed entirely using Python, a high-level programming language. Applications such as BCI paradigms and neuroscience experiments can be developed within Python using PyQt, or in any other programming language by utilizing a built-in TCP/IP asynchronous protocol to communicate with the platform. Notably, many publicly available apps are developed in Unity. This choice was driven by specific requirements, such as precise synchronization between EEG and stimuli for paradigms like c-VEP, or to enhance user experience through visually appealing designs, as seen in MI and NF based apps.
- LSL compatible: MEDUSA© can record and process signals streamed through the lab streaming layer (LSL) protocol, making it compatible with a wide range of biomedical devices.
- Modular design: the software is composed of (1) the platform (user interface and signal management) and (2) the kernel (a PyPI package with processing functions and classes), providing a modular and scalable framework.
- Control signals: the ecosystem also includes pre-built examples for various BCI paradigms, such as P300, c-VEP, SMR, and NF applications, streamlining the development process for researchers.
Supported BCI paradigms
(in progress)
Links
Website LinkedIn GitHub Twitter YouTube
- ↑ Eduardo Santamaría-Vázquez, Víctor Martínez-Cagigal, Diego Marcos-Martínez, Víctor Rodríguez-González, Sergio Pérez-Velasco, Selene Moreno-Calderón, Roberto Hornero, MEDUSA©: A novel Python-based software ecosystem to accelerate brain-computer interface and cognitive neuroscience research. Computer Methods and Programs in Biomedicine, 2023, DOI: doi.org/10.1016/j.cmpb.2023.107357.