An fNIRS Dataset for Cognitive Decoding during a Multi-day Block-design Stroop Task

Area of Science:

• Neuroscience
• Cognitive Science
• Biomedical Engineering

Background:

• Cognitive state decoding is crucial for brain-computer interfaces (BCIs), psychiatric applications, and neuroscience.
• Existing datasets for cognitive state decoding using optical neuroimaging, specifically fNIRS, are limited.
• There is a need for comprehensive fNIRS datasets to advance research in cognitive neuroscience and BCI development.

Purpose of the Study:

• To introduce and share a novel functional near-infrared spectroscopy (fNIRS) dataset.
• To facilitate research in cognitive state decoding using neuroimaging data.
• To support the development of advanced signal processing algorithms and machine learning models for hemodynamic signals.

Main Methods:

• Acquired fNIRS data from 55 young adults performing a color-word Stroop task.
• Collected frontal hemoglobin responses across three separate sessions for each participant within a 2-week period.
• Ensured collection of over 30 trials per condition to maintain data richness while minimizing participant fatigue.

Main Results:

• Successfully generated a robust fNIRS dataset capturing frontal hemodynamic responses during cognitive conflict.
• The dataset contains detailed trial-by-trial data, suitable for various analytical approaches.
• Demonstrated the feasibility of collecting high-quality fNIRS data over multiple sessions without significant mental fatigue.

Conclusions:

• The released fNIRS dataset is a valuable resource for BCI research, cognitive neuroscience, and psychiatric studies.
• This dataset will accelerate the development of cognitive state decoders and neurofeedback systems.
• Facilitates advancements in signal processing for hemodynamic data and the creation of large-scale, cross-subject fNIRS models.