ArticleJournal of biomedical optics2019
fNIRS improves seizure detection in multimodal EEG-fNIRS recordings.
Article in Journal of biomedical optics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 71 citations in OpenAlex.
- Cortical Region Reporting Patterns in Neurodevelopmental Disorders: A Systematic Review of fNIRS Studies.Biosensors · 2026Pooled it
- Functional near-infrared spectroscopy (fNIRS) in medical research: applications, opportunities, and challenges.Chinese medicine · 2026Review
- Inferring neural sources from electroencephalography: foundations and frontiers.Journal of neural engineering · 2026Review
- Noninvasive imaging techniques to map language areas using BOLD signal fluctuations in pediatric epilepsy: a review.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2026Review
- The protocol for mesoscopic wide-field optical imaging in mice: from zero to hero.Biology methods & protocols · 2026Review
- Motor Intention Quantization for Patients With Disorders of Consciousness by Multimodal BCI Combining Electroencephalography and Functional Near-Infrared Spectroscopy.CNS neuroscience & therapeutics · 2025Article
- A Transformer-Based Framework With Data Augmentation for Robust Seizure Detection Across Invasive and Noninvasive Neural Recordings.CNS neuroscience & therapeutics · 2025Article
- A review of hybrid EEG-based multimodal human-computer interfaces using deep learning: applications, advances, and challenges.Biomedical engineering letters · 2025Review
- Wide-Based Illumination and Detection in Functional Near-Infrared Spectroscopy for Enhanced Seizure Detection in Grey Matter.Sensors (Basel, Switzerland) · 2025Article
- Protocol for Simultaneous EEG and fNIRS Measurement in Characterizing Brain State.Phenomics (Cham, Switzerland) · 2025Article
- Intracranial functional near-infrared spectroscopy: an animal feasibility study.Frontiers in medical technology · 2025Article
- Automatic detection and prediction of epileptic EEG signals based on nonlinear dynamics and deep learning: a review.Frontiers in neuroscience · 2025Review
- Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Strategic Integration: A Cross-Disciplinary Review of the fNIRS-EEG Dual-Modality Imaging System for Delivering Multimodal Neuroimaging to Applications.Brain sciences · 2024Review
- Hybrid Integrated Wearable Patch for Brain EEG-fNIRS Monitoring.Sensors (Basel, Switzerland) · 2024Article
- Flexible circuit-based spatially aware modular optical brain imaging system for high-density measurements in natural settings.Neurophotonics · 2024Article
- Flexible-circuit-based 3-D aware modular optical brain imaging system for high-density measurements in natural settings.medRxiv : the preprint server for health sciences · 2024Article
- EF-Net: Mental State Recognition by Analyzing Multimodal EEG-fNIRS via CNN.Sensors (Basel, Switzerland) · 2024Article
- A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning.Frontiers in neuroscience · 2024Review
- Opportunities and obstacles in non-invasive brain stimulation.Frontiers in human neuroscience · 2024Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
In the context of epilepsy monitoring, electroencephalography (EEG) remains the modality of choice. Functional near-infrared spectroscopy (fNIRS) is a relatively innovative modality that cannot only characterize hemodynamic profiles of seizures but also allow for long-term recordings. We employ deep learning methods to investigate the benefits of integrating fNIRS measures for seizure detection. We designed a deep recurrent neural network with long short-term memory units and subsequently validated it using the CHBMIT scalp EEG database-a compendium of 896 h of surface EEG seizure recordings. After validating our network using EEG, fNIRS, and multimodal data comprising a corpus of 89 seizures from 40 refractory epileptic patients was used as model input to evaluate the integration of fNIRS measures. Following heuristic hyperparameter optimization, multimodal EEG-fNIRS data provide superior performance metrics (sensitivity and specificity of 89.7% and 95.5%, respectively) in a seizure detection task, with low generalization errors and loss. False detection rates are generally low, with 11.8% and 5.6% for EEG and multimodal data, respectively. Employing multimodal neuroimaging, particularly EEG-fNIRS, in epileptic patients, can enhance seizure detection performance. Furthermore, the neural network model proposed and characterized herein offers a promising framework for future multimodal investigations in seizure detection and prediction.
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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.