ArticleSensors (Basel, Switzerland)2022
Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks.
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 19 papers.
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Who cites it
19 citing papers in PubMed.
- Hand movements prediction via fNIRS and AI algorithms.BMC medical informatics and decision making · 2026Article
- Advances in (Bio)Sensors for Physiological Monitoring: A Special Issue Review.Sensors (Basel, Switzerland) · 2026Article
- Wearable technologies for assisted mobility in the real world.Nature communications · 2025Review
- Transformer-based deep learning model for predicting fNIRS short-channel signals.Neurophotonics · 2025Article
- Improved performance of fNIRS-BCI by stacking of deep learning-derived frequency domain features.PloS one · 2025Article
- A Lightweight Network with Domain Adaptation for Motor Imagery Recognition.Entropy (Basel, Switzerland) · 2024Article
- Hybrid Functional Near-Infrared Spectroscopy System and Electromyography for Prosthetic Knee Control.Biosensors · 2024Article
- Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional Near-Infrared Spectroscopy Brain-Computer Interface Application.Sensors (Basel, Switzerland) · 2024Article
- Integrating fNIRS and machine learning: shedding light on Parkinson's disease detection.EXCLI journal · 2024Article
- Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging.IEEE journal of translational engineering in health and medicine · 2024Article
- fNIRS-EEG BCIs for Motor Rehabilitation: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- State-of-the-Art on Brain-Computer Interface Technology.Sensors (Basel, Switzerland) · 2023Review
- Brain Connectivity Analysis in Distinct Footwear Conditions during Infinity Walk Using fNIRS.Sensors (Basel, Switzerland) · 2023Article
- Cortical activation of neuromuscular electrical stimulation synchronized mirror neuron rehabilitation strategies: an fNIRS study.Frontiers in neurology · 2023Article
- System Derived Spatial-Temporal CNN for High-Density fNIRS BCI.IEEE open journal of engineering in medicine and biology · 2023Article
- Deep learning in fNIRS: a review.Neurophotonics · 2022Review
- A Unified Local-Global Feature Extraction Network for Human Gait Recognition Using Smartphone Sensors.Sensors (Basel, Switzerland) · 2022Article
- LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI.Sensors (Basel, Switzerland) · 2022Article
- Brain-Computer interface control of stepping from invasive electrocorticography upper-limb motor imagery in a patient with quadriplegia.Frontiers in human neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research presents a brain-computer interface (BCI) framework for brain signal classification using deep learning (DL) and machine learning (ML) approaches on functional near-infrared spectroscopy (fNIRS) signals. fNIRS signals of motor execution for walking and rest tasks are acquired from the primary motor cortex in the brain's left hemisphere for nine subjects. DL algorithms, including convolutional neural networks (CNNs), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM) are used to achieve average classification accuracies of 88.50%, 84.24%, and 85.13%, respectively. For comparison purposes, three conventional ML algorithms, support vector machine (SVM), k-nearest neighbor (k-NN), and linear discriminant analysis (LDA) are also used for classification, resulting in average classification accuracies of 73.91%, 74.24%, and 65.85%, respectively. This study successfully demonstrates that the enhanced performance of fNIRS-BCI can be achieved in terms of classification accuracy using DL approaches compared to conventional ML approaches. Furthermore, the control commands generated by these classifiers can be used to initiate and stop the gait cycle of the lower limb exoskeleton for gait rehabilitation.
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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.