An Open-Access Multi-modal Dataset for Cognitive, Motor, and Cognitive-Motor Tasks
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910068593655808 |
|---|---|
| author | Ajra, Zaineb Vergotte, Grégoire Perrey, Stéphane Evra, Lilian Pla, Simon Dray, Gérard Montmain, Jacky Xu, Binbin |
| author_facet | Ajra, Zaineb Vergotte, Grégoire Perrey, Stéphane Evra, Lilian Pla, Simon Dray, Gérard Montmain, Jacky Xu, Binbin |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22933 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Open-Access Multi-modal Dataset for Cognitive, Motor, and Cognitive-Motor Tasks Ajra, Zaineb Vergotte, Grégoire Perrey, Stéphane Evra, Lilian Pla, Simon Dray, Gérard Montmain, Jacky Xu, Binbin Neurons and Cognition 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 |
| title | An Open-Access Multi-modal Dataset for Cognitive, Motor, and Cognitive-Motor Tasks |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2603.22933 |