ArticleScientific data2018
Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset.
Article in Scientific data, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.
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Who cites it
71 citing papers in PubMed.
- On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface.Sensors (Basel, Switzerland) · 2018Trial
- A Low-Cost Wearable Multimodal Brain Signal Acquisition System Integrating EEG and fNIRS for Depression Detection.Biosensors · 2026Article
- MGFNet: A Multi-Granularity Fusion Network with Coupling-Guided Sparse Routing for Hybrid EEG-fNIRS Decoding.Sensors (Basel, Switzerland) · 2026Article
- Decoupled Bidirectional Spatio-Temporal Fusion Network for Hybrid EEG-fNIRS Cognitive Task Classification.Brain sciences · 2026Article
- Decoding P300 as a shared neural mechanism for oddball target detection and working memory updating.iScience · 2026Article
- EED-CL: Extended EEG Deformer with Contrastive Learning for Robust Emotion Recognition.Bioengineering (Basel, Switzerland) · 2025Article
- HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage.Scientific data · 2025Article
- TSFNet: Temporal-Spatial Fusion Network for Hybrid Brain-Computer Interface.Sensors (Basel, Switzerland) · 2025Article
- TopoTempNet: A High-Accuracy and Interpretable Decoding Method for fNIRS-Based Motor Imagery.Sensors (Basel, Switzerland) · 2025Article
- The relationship between grip strength, 6 minute walking distance and working memory in older adults with mild cognitive impairment: an fNIRS study.BMC geriatrics · 2025Article
- Block-Wise Domain Adaptation for Workload Prediction from fNIRS Data.Sensors (Basel, Switzerland) · 2025Article
- Simultaneous Dataset of Brain, Eye and Hand during Visuomotor Tasks.Scientific data · 2025Article
- The Potential of Near-Infrared Spectroscopy as a Therapeutic Tool Following a Stroke (Review).Sovremennye tekhnologii v meditsine · 2025Review
- Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Multimodal MBC-ATT: cross-modality attentional fusion of EEG-fNIRS for cognitive state decoding.Frontiers in human neuroscience · 2025Article
- The impact of cross-validation choices on pBCI classification metrics: lessons for transparent reporting.Frontiers in neuroergonomics · 2025Article
- Cortex level connectivity between ACT-R modules during EEG-based n-back task.Cognitive neurodynamics · 2024Article
- A multiple session dataset of NIRS recordings from stroke patients controlling brain-computer interface.Scientific data · 2024Article
- Cognitive workload estimation using physiological measures: a review.Cognitive neurodynamics · 2024Article
- Deep learning networks based decision fusion model of EEG and fNIRS for classification of cognitive tasks.Cognitive neurodynamics · 2024Article
11 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
We provide an open access multimodal brain-imaging dataset of simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings. Twenty-six healthy participants performed three cognitive tasks: 1) n-back (0-, 2- and 3-back), 2) discrimination/selection response task (DSR) and 3) word generation (WG) tasks. The data provided includes: 1) measured data, 2) demographic data, and 3) basic analysis results. For n-back (dataset A) and DSR tasks (dataset B), event-related potential (ERP) analysis was performed, and spatiotemporal characteristics and classification results for 'target' versus 'non-target' (dataset A) and symbol 'O' versus symbol 'X' (dataset B) are provided. Time-frequency analysis was performed to show the EEG spectral power to differentiate the task-relevant activations. Spatiotemporal characteristics of hemodynamic responses are also shown. For the WG task (dataset C), the EEG spectral power and spatiotemporal characteristics of hemodynamic responses are analyzed, and the potential merit of hybrid EEG-NIRS BCIs was validated with respect to classification accuracy. We expect that the dataset provided will facilitate performance evaluation and comparison of many neuroimaging analysis techniques.
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