Large-scale fMRI dataset for the design of motor-based Brain-Computer Interfaces

Area of Science:

• Neuroscience
• Neuroimaging
• Biomedical Engineering

Background:

• Functional Magnetic Resonance Imaging (fMRI) is crucial for mapping sensorimotor cortical organization and identifying Brain-Computer Interface (BCI) target sites.
• Existing functional data for mapping BCI targets across the lifespan, especially in children, is limited.
• Understanding developmental changes in sensorimotor representation is vital for effective BCI design.

Purpose of the Study:

• To present a comprehensive dataset of fMRI data collected during standardized motor and somatosensory tasks.
• To provide a valuable resource for studying sensorimotor cortical organization across a wide age range (6-89 years).
• To support the development of pediatric motor-based implanted BCIs.

Main Methods:

• Collected fMRI data from 155 human participants (adults and children).
• Participants performed standardized motor and somatosensory tasks involving various body parts (fingers, hands, arms, feet, legs, mouth).
• Data was specifically designed to map and localize BCI target areas.

Main Results:

• A large-scale dataset of fMRI data is now available.
• The dataset captures sensorimotor task performance across a broad age spectrum.
• This resource enables detailed analysis of developmental trajectories in cortical organization.

Conclusions:

• The dataset is a significant resource for understanding sensorimotor representation development.
• It will facilitate the design and optimization of BCI systems, particularly for pediatric applications.
• This work addresses a critical gap in neuroimaging data for BCI research across the lifespan.