ArticleSensors (Basel, Switzerland)2022
EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 53 citations in OpenAlex.
- Reviewing digital collaborative interactions with multimodal hyperscanning through an ever-growing database.Frontiers in neuroergonomics · 2026Pooled it
- Human-Centric Cognitive State Recognition Using Physiological Signals: A Systematic Review of Machine Learning Strategies Across Application Domains.Sensors (Basel, Switzerland) · 2025Pooled it
- Machine learning performance in EEG-based mental workload classification across task types: a systematic review.Frontiers in neuroergonomics · 2025Pooled it
- Synergy and Synchrony Between Beta-Theta Neurofeedback, Heart Rate Variability Biofeedback and Breathwork Via Continuous Feedback in Improving Reaction Time.Applied psychophysiology and biofeedback · 2026Article
- Machine Learning Based Identification of Depressive Symptoms Among Students in a Chinese University Using Functional Near-Infrared Spectroscopy.Alpha psychiatry · 2025Article
- Compact Colocated Bimodal EEG/fNIRS Multi-Distance Sensor.Sensors (Basel, Switzerland) · 2025Article
- Multimodal MBC-ATT: cross-modality attentional fusion of EEG-fNIRS for cognitive state decoding.Frontiers in human neuroscience · 2025Article
- Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Movement Sensing Opportunities for Monitoring Dynamic Cognitive States.Sensors (Basel, Switzerland) · 2024Review
- Strategic Integration: A Cross-Disciplinary Review of the fNIRS-EEG Dual-Modality Imaging System for Delivering Multimodal Neuroimaging to Applications.Brain sciences · 2024Review
- A Brain Network Analysis Model for Motion Sickness in Electric Vehicles Based on EEG and fNIRS Signal Fusion.Sensors (Basel, Switzerland) · 2024Article
- Deep learning networks based decision fusion model of EEG and fNIRS for classification of cognitive tasks.Cognitive neurodynamics · 2024Article
- Novel Feature Generation for Classification of Motor Activity from Functional Near-Infrared Spectroscopy Signals Using Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging.IEEE journal of translational engineering in health and medicine · 2024Article
- Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder.Brain sciences · 2023Article
- From local counterfactuals to global feature importance: efficient, robust, and model-agnostic explanations for brain connectivity networks.Computer methods and programs in biomedicine · 2023Article
- Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is high demand for techniques to estimate human mental workload during some activities for productivity enhancement or accident prevention. Most studies focus on a single physiological sensing modality and use univariate methods to analyse multi-channel electroencephalography (EEG) data. This paper proposes a new framework that relies on the features of hybrid EEG-functional near-infrared spectroscopy (EEG-fNIRS), supported by machine-learning features to deal with multi-level mental workload classification. Furthermore, instead of the well-used univariate power spectral density (PSD) for EEG recording, we propose using bivariate functional brain connectivity (FBC) features in the time and frequency domains of three bands: delta (0.5-4 Hz), theta (4-7 Hz) and alpha (8-15 Hz). With the assistance of the fNIRS oxyhemoglobin and deoxyhemoglobin (HbO and HbR) indicators, the FBC technique significantly improved classification performance at a 77% accuracy for 0-back vs. 2-back and 83% for 0-back vs. 3-back using a public dataset. Moreover, topographic and heat-map visualisation indicated that the distinguishing regions for EEG and fNIRS showed a difference among the 0-back, 2-back and 3-back test results. It was determined that the best region to assist the discrimination of the mental workload for EEG and fNIRS is different. Specifically, the posterior area performed the best for the posterior midline occipital (POz) EEG in the alpha band and fNIRS had superiority in the right frontal region (AF8).
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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.