An Overview of Open Source Deep Learning-Based Libraries for Neuroscience

Abstract

In recent years, deep learning has revolutionized machine learning and its applications, producing results comparable to human experts in several domains, including neuroscience. Each year, hundreds of scientific publications present applications of deep neural networks for biomedical data analysis. Due to the fast growth of the domain, it could be a complicated and extremely time-consuming task for worldwide researchers to have a clear perspective of the most recent and advanced software libraries. This work contributes to clarifying the current situation in the domain, outlining the most useful libraries that implement and facilitate deep learning applications for neuroscience, allowing scientists to identify the most suitable options for their research or clinical projects. This paper summarizes the main developments in deep learning and their relevance to neuroscience; it then reviews neuroinformatic toolboxes and libraries collected from the literature and from specific hubs of software projects oriented to neuroscience research. The selected tools are presented in tables detailing key features grouped by the domain of application (e.g., data type, neuroscience area, task), model engineering (e.g., programming language, model customization), and technological aspect (e.g., interface, code source). The results show that, among a high number of available software tools, several libraries stand out in terms of functionalities for neuroscience applications. The aggregation and discussion of this information can help the neuroscience community to develop their research projects more efficiently and quickly, both by means of readily available tools and by knowing which modules may be improved, connected, or added.

SelfEEG: A Python library for Self-Supervised Learning in Electroencephalography

“…Despite several deep learning frameworks having been developed for the analysis of EEG data, a library focused on the construction of self-supervised learning pipelines on EEG data is still not available to the best of our knowledge, hindering the advancement of the scientific knowledge and the progress in the field. A comprehensive review of open-source projects related to neuroscientific data analysis is provided in (Tshimanga et al, 2023). A few examples are EEG-DL (Hou et al, Feb. 2020) and torchEEG (Zhang et al, 2024), which characterized for their completeness and spread among the neuroscientific community.…”