ArticleFrontiers in neuroscience2020
Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain-Computer Interface.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Human-Centric Cognitive State Recognition Using Physiological Signals: A Systematic Review of Machine Learning Strategies Across Application Domains.Sensors (Basel, Switzerland) · 2025Pooled it
- DSGF-Net: A Lightweight Dual-Stream Gated Fusion Network for Cross-Subject fNIRS Motor Task Classification.Sensors (Basel, Switzerland) · 2026Article
- fNIRS-STCT: A Novel Hybrid Spatial and Temporal CNN and Transformer Network for fNIRS Signal Classification.Brain topography · 2026Article
- An early detection framework for young Chinese learners at risk of reading difficulty using fNIRS and deep learning.Scientific reports · 2026Article
- Exploring cognitive load through neuropsychological features: an analysis using fNIRS-eye tracking.Medical & biological engineering & computing · 2025Article
- Attention-based Transformer-LSTM architecture for early diagnosis and staging of early-stage Parkinson's disease using fNIRS data.Frontiers in aging neuroscience · 2025Article
- A cross-attention swin transformer network for EEG-based subject-independent cognitive load assessment.Cognitive neurodynamics · 2024Article
- CT-Net: an interpretable CNN-Transformer fusion network for fNIRS classification.Medical & biological engineering & computing · 2024Article
- Cognitive workload estimation using physiological measures: a review.Cognitive neurodynamics · 2024Article
- Novel Feature Generation for Classification of Motor Activity from Functional Near-Infrared Spectroscopy Signals Using Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Working memory load recognition with deep learning time series classification.Biomedical optics express · 2024Article
- Decoding Mental Effort in a Quasi-Realistic Scenario: A Feasibility Study on Multimodal Data Fusion and Classification.Sensors (Basel, Switzerland) · 2023Article
- CGAN-rIRN: a data-augmented deep learning approach to accurate classification of mental tasks for a fNIRS-based brain-computer interface.Biomedical optics express · 2023Article
- Neural networks contribution in face mask detection to reduce the spread of COVID-19.Multimedia tools and applications · 2023Article
- Image Classification Based on Light Convolutional Neural Network Using Pulse Couple Neural Network.Computational intelligence and neuroscience · 2023Article
- Impacts of complex electromagnetic radiation and low-frequency noise exposure conditions on the cognitive function of operators.Frontiers in public health · 2023Article
- Investigation of the neural correlation with task performance and its effect on cognitive load level classification.PloS one · 2023Article
- Deep learning in fNIRS: a review.Neurophotonics · 2022Review
- Training Monitoring in Sports: It Is Time to Embrace Cognitive Demand.Sports (Basel, Switzerland) · 2022Review
- Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
8 authors.
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Abstract
Cognitive workload is one of the widely invoked human factors in the areas of human-machine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and mental workload (MWL) is vital and requires accurate neuroimaging to monitor and evaluate the cognitive states of the brain. In this study, we have decoded four classes of MWL using long short-term memory (LSTM) with 89.31% average accuracy for brain-computer interface (BCI). The brain activity signals are acquired using functional near-infrared spectroscopy (fNIRS) from the prefrontal cortex (PFC) region of the brain. We performed a supervised MWL experimentation with four varying MWL levels on 15 participants (both male and female) and 10 trials of each MWL per participant. Real-time four-level MWL states are assessed using fNIRS system, and initial classification is performed using three strong machine learning (ML) techniques, support vector machine (SVM),
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