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

Zaineb Ajra, Grégoire Vergotte, Stéphane Perrey, Lilian Evra, Simon Pla, Gérard Dray, Jacky Montmain, Binbin Xu

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

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

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
Paper Structure (31 sections, 6 figures, 3 tables)

This paper contains 31 sections, 6 figures, 3 tables.

Table of Contents

  1. Background & Summary
  2. Methods
  3. Ethics statement
  4. Experimental paradigm
  5. Experimental conditions:
  6. Data acquisition
  7. Neurophysiological data acquisition
  8. Physiological data acquisition
  9. Behavioral data acquisition
  10. Subjective data acquisition
  11. Data synchronization
  12. Data Availability
  13. Data Records
  14. EEG data
  15. fNIRS data
  16. ...and 16 more sections

Figures (6)

  • Figure 1: Experimental paradigm overview
  • Figure 2: Configuration of the experimental fNIRS-EEG system. Left: picture of the head cap (rear view). Right: spatial layout of the 32-channel EEG electrodes arranged according to the international 10–20 system.
  • Figure 3: Overview of the synchronized data acquisition setup using Lab Streaming Layer. Arrows indicate individual streams.
  • Figure 4: Overview of the dataset structure
  • Figure 5: Marginal mean and standard error for cognitive load rating of the 7 tasks.
  • ...and 1 more figures