ArticleNeurophotonics2018
Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution.
Article in Neurophotonics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed, 116 citations in OpenAlex.
- An early detection framework for young Chinese learners at risk of reading difficulty using fNIRS and deep learning.Scientific reports · 2026Article
- Random splicing assisted deep learning for breast cancer cell line classification via Raman spectroscopy.Computational and structural biotechnology journal · 2025Article
- A Lightweight Network with Domain Adaptation for Motor Imagery Recognition.Entropy (Basel, Switzerland) · 2024Article
- Novel Feature Generation for Classification of Motor Activity from Functional Near-Infrared Spectroscopy Signals Using Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Efficient Near-Infrared Spectrum Detection in Nondestructive Wood Testing via Transfer Network Redesign.Sensors (Basel, Switzerland) · 2024Article
- fNIRS-EEG BCIs for Motor Rehabilitation: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- 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
- System Derived Spatial-Temporal CNN for High-Density fNIRS BCI.IEEE open journal of engineering in medicine and biology · 2023Article
- Benchmarking framework for machine learning classification from fNIRS data.Frontiers in neuroergonomics · 2023Article
- Deep learning in fNIRS: a review.Neurophotonics · 2022Review
- Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning.Sensors (Basel, Switzerland) · 2022Article
- Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Novel fNIRS study on homogeneous symmetric feature-based transfer learning for brain-computer interface.Scientific reports · 2022Article
- Explainable artificial intelligence model to predict brain states from fNIRS signals.Frontiers in human neuroscience · 2022Article
- Cross-Modal Transfer Learning From EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System.Frontiers in psychology · 2022Article
- Programming ability prediction: Applying an attention-based convolutional neural network to functional near-infrared spectroscopy analyses of working memory.Frontiers in neuroscience · 2022Article
- Combining robust level extraction and unsupervised adaptive classification for high-accuracy fNIRS-BCI: An evidence on single-trial differentiation between mentally arithmetic- and singing-tasks.Frontiers in neuroscience · 2022Article
- Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021Review
- Decoding different working memory states during an operation span task from prefrontal fNIRS signals.Biomedical optics express · 2021Article
- An effective classification framework for brain-computer interface system design based on combining of fNIRS and EEG signals.PeerJ. Computer science · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.
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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.