Open access dataset integrating EEG and fNIRS during Stroop tasks.
Zemeng ChenChenyang GaoTing LiXiang JiShuyu LiuMing XiaoPublished in: Scientific data (2023)
Conflict monitoring and processing are crucial components of the human cognitive system, with significant implications for daily life and the diagnosis of cognitive disorders. The Stroop task, combined with brain function detection technology, has been widely employed as a classical paradigm for investigating conflict processing. However, there remains a lack of public datasets that integrate Electroencephalogram (EEG) and functional Near-infrared Spectroscopy (fNIRS) to simultaneously record brain activity during a Stroop task. We introduce a dual-modality Stroop task dataset incorporating 34-channel EEG (sampling frequency is 1000 Hz) and 20-channel high temporal resolution fNIRS (sampling frequency is 100 Hz) measurements covering the whole frontal cerebral cortex from 21 participants (9 females/12 males, aged 23.0 ± 2.3 years). Event-related potential analysis of EEG recordings and activation analysis of fNIRS recordings were performed to show the significant Stroop effect. We expected that the data provided would be utilized to investigate multimodal data processing algorithms during cognitive processing.
Keyphrases
- functional connectivity
- resting state
- working memory
- electronic health record
- machine learning
- endothelial cells
- healthcare
- big data
- emergency department
- physical activity
- mental health
- minimally invasive
- deep learning
- white matter
- subarachnoid hemorrhage
- cerebral ischemia
- climate change
- induced pluripotent stem cells
- high density
- label free
- loop mediated isothermal amplification