ArticleFrontiers in neuroscience2018
Speech Recognition via fNIRS Based Brain Signals.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- A technical review of canonical correlation analysis for neuroscience applications.Human brain mapping · 2020Pooled it
- Single-subject detection of speech network activation using functional near-infrared spectroscopy.Neurophotonics · 2026Article
- A high-precision synchronization and centroid-based framework for single-trial decoding of perceived natural speech.Frontiers in computational neuroscience · 2026Article
- DL-QC-fNIRS: a deep learning tool for automated quality control in functional near-infrared spectroscopy signals.Neurophotonics · 2026Article
- Progress, challenges and future of linguistic neural decoding with deep learning.Communications biology · 2025Review
- Classification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models.Brain topography · 2025Article
- Neural signals, machine learning, and the future of inner speech recognition.Frontiers in human neuroscience · 2025Review
- Boosting brain-computer interfaces with functional electrical stimulation: potential applications in people with locked-in syndrome.Journal of neuroengineering and rehabilitation · 2023Review
- Decoding of the speech envelope from EEG using the VLAAI deep neural network.Scientific reports · 2023Article
- Dataset of Speech Production in intracranial.Electroencephalography.Scientific data · 2022Article
- Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior.Frontiers in neuroscience · 2022Review
- A low-cost, wearable, do-it-yourself functional near-infrared spectroscopy (DIY-fNIRS) headband.HardwareX · 2021Article
- Predicting Student Performance Using Machine Learning in fNIRS Data.Frontiers in human neuroscience · 2021Article
- A New Statistical Approach for fNIRS Hyperscanning to Predict Brain Activity of Preschoolers' Using Teacher's.Frontiers in human neuroscience · 2021Article
- Projections and the Potential Societal Impact of the Future of Neurotechnologies.Frontiers in neuroscience · 2021Review
- Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain-Computer Interface.Frontiers in neuroscience · 2020Article
- Information Content of Prefrontal Cortex Activity Quantifies the Difficulty of Narrated Stories.Scientific reports · 2019Article
- Neural correlates of cognitive control in women with a history of sexual violence suggest altered prefrontal cortical activity during cognitive processing.Women's health (London, England)Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we present the first evidence that perceived speech can be identified from the listeners' brain signals measured via functional-near infrared spectroscopy (fNIRS)-a non-invasive, portable, and wearable neuroimaging technique suitable for ecologically valid settings. In this study, participants listened audio clips containing English stories while prefrontal and parietal cortices were monitored with fNIRS. Machine learning was applied to train predictive models using fNIRS data from a subject pool to predict which part of a story was listened by a new subject not in the pool based on the brain's hemodynamic response as measured by fNIRS. fNIRS signals can vary considerably from subject to subject due to the different head size, head shape, and spatial locations of brain functional regions. To overcome this difficulty, a generalized canonical correlation analysis (GCCA) was adopted to extract latent variables that are shared among the listeners before applying principal component analysis (PCA) for dimension reduction and applying logistic regression for classification. A 74.7% average accuracy has been achieved for differentiating between two 50 s. long story segments and a 43.6% average accuracy has been achieved for differentiating four 25 s. long story segments. These results suggest the potential of an fNIRS based-approach for building a speech decoding brain-computer-interface for developing a new type of neural prosthetic system.
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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.