ArticleNeuroinformatics2022
Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals.
Article in Neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 33 citations in OpenAlex.
- Condition-dependent disruption of low-frequency EEG-fMRI coupling reveals delayed hemodynamic timing and motor-network reorganization in chronic stroke.NeuroImage. Clinical · 2026Article
- Personalized cognitive enhancement for older adults: An aging-friendly closed-loop human-machine interface framework.Ageing research reviews · 2025Review
- [Research on the relationship between resting-state spontaneous electroencephalography and task-evoked electroencephalography].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Deep multimodal representations and classification of first-episode psychosis via live face processing.Frontiers in psychiatry · 2025Article
- Beta-Band Cortico-Muscular Phase Coherence in Hemiparetic Stroke.Biomedical signal processing and control · 2024Article
- NiReject: toward automated bad channel detection in functional near-infrared spectroscopy.Neurophotonics · 2024Article
- Functional Near-Infrared Spectroscopy as a Personalized Digital Healthcare Tool for Brain Monitoring.Journal of clinical neurology (Seoul, Korea) · 2023Review
- Error-related brain state analysis using electroencephalography in conjunction with functional near-infrared spectroscopy during a complex surgical motor task.Brain informatics · 2022Article
- Deep learning in fNIRS: a review.Neurophotonics · 2022Review
- Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model's fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.