A standardised open science framework for sharing and re-analysing neural data acquired to continuous stimuli

TL;DR

This paper introduces a comprehensive open science framework for sharing, storing, and re-analyzing neural data from continuous sensory experiments, promoting standardization and accessibility in neurophysiology research.

Contribution

It proposes a standardized data structure and workflow compatible with existing tools, facilitating data sharing and re-analysis in continuous sensory neural data studies.

Findings

Developed a data structure based on CND with precise conventions.

Integrated the framework with popular EEG/MEG analysis toolboxes.

Created a web-based data browser for easy data exploration.

Abstract

Neurophysiology research has demonstrated that it is possible and valuable to investigate sensory processing in scenarios involving continuous sensory streams, such as speech and music. Over the past 10 years or so, novel analytic frameworks combined with the growing participation in data sharing has led to a surge of publicly available datasets involving continuous sensory experiments. However, open science efforts in this domain of research remain scattered, lacking a cohesive set of guidelines. This paper presents an end-to-end open science framework for the storage, analysis, sharing, and re-analysis of neural data recorded during continuous sensory experiments. We propose a data structure that builds on existing custom structures (Continuous-event Neural Data or CND), providing precise naming conventions and data types, as well as a workflow for storing and loading data in the general-purpose BIDS structure. The framework has been designed to interface with existing EEG/MEG analysis toolboxes, such as Eelbrain, NAPLib, MNE, and mTRF-Toolbox. We present guidelines by taking both the user view (rapidly re-analyse existing data) and the experimenter view (store, analyse, and share), making the process straightforward and accessible. Additionally, we introduce a web-based data browser that enables the effortless replication of published results and data re-analysis.