An Open-Access Multi-modal Dataset for Cognitive, Motor, and Cognitive-Motor Tasks

TL;DR

This paper introduces a comprehensive multi-modal neuroimaging dataset capturing EEG, fNIRS, ECG, behavioral, and subjective data from 30 participants performing diverse cognitive, motor, and combined tasks to facilitate advanced analysis of brain dynamics in real-world scenarios.

Contribution

It provides a novel open-access multi-modal dataset with diverse tasks and measures, enabling research on multi-task brain interactions in realistic settings.

Findings

Dataset includes 7 hierarchical tasks from single to combined activities.

Data collected from 30 healthy participants over three sessions.

Resource supports development of advanced analysis and brain-computer interface methods.

Abstract

The incorporation of neuroimaging techniques such as electroenchephalography (EEG) and functional near-infrared spectroscopy (fNIRS) has provided new opportunities for the analysis of dynamic brain processes involved in cognitive and motor functions. Despite the great contribution of the open-access neuroimaging datasets to neuroscience studies, they have mainly remained on a single modality and isolated task paradigms performed in a controlled environments. These limitations restrict the analysis of multi-task effects in real-world applications, thus creating a gap in the understanding of how cognitive and motor processes interact in daily life activities. To address these limitations, we present a multi-modal dataset containing neurophysiological (EEG, fNIRS), physiological (ECG), behavioral, and subjective measures collected from 30 healthy participants over three sessions. This dataset includes a hierarchical series of seven tasks ranging from single cognitive and motor activities, such as N-back, motor, passive motor, mental arithmetic and motor imagery, to combined cognitive-motor interactions simulating real life scenarios. This raw dataset provides a resource for developing advanced preprocessing methods and analysis pipelines, with potential applications in brain-computer interfaces, neurorehabilitation, and other fields requiring an understanding of multi-tasks brain dynamics. https://doi.org/10.18112/openneuro.ds007554.v1.0.0