Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Interpretable Network-Level Biomarker Discovery for Alzheimer's Stage Assessment Using Resting-State fNIRS Complexity Graphs.

Brain sciences·2026
Same author

Decoupled Bidirectional Spatio-Temporal Fusion Network for Hybrid EEG-fNIRS Cognitive Task Classification.

Brain sciences·2026
Same author

Time-frequency deep metric learning of resting-state fNIRS signals for staging Alzheimer's disease.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

A multi-graph convolutional network method for Alzheimer's disease diagnosis based on multi-frequency EEG data with dual-mode connectivity.

Frontiers in neuroscience·2025
Same author

Behaviorally inhibited preschoolers experience stronger connectivity among social-related neural regions while interacting with a stranger.

Developmental cognitive neuroscience·2025
Same author

Ipsilateral stimulation shows somatotopy of thumb and shoulder auricular points on the left primary somatosensory cortex using high-density fNIRS.

bioRxiv : the preprint server for biology·2024

Related Experiment Video

Updated: Mar 26, 2026

Exploring Cognitive Functions in Babies, Children & Adults with Near Infrared Spectroscopy
12:40

Exploring Cognitive Functions in Babies, Children & Adults with Near Infrared Spectroscopy

Published on: July 28, 2009

21.2K

Decoding four different sound-categories in the auditory cortex using functional near-infrared spectroscopy.

Keum-Shik Hong1, Hendrik Santosa2

  • 1Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Republic of Korea; School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Republic of Korea.

Hearing Research
|February 2, 2016
PubMed
Summary

Functional near-infrared spectroscopy (fNIRS) can distinguish brain activity from different sounds. The left auditory cortex is better for decoding sound commands, with nature vs. annoying sounds being easier to classify.

Keywords:
Auditory cortexClassificationFunctional near-infrared spectroscopy (fNIRS)Multiple sound categories

More Related Videos

Functional Imaging of Auditory Cortex in Adult Cats using High-field fMRI
10:50

Functional Imaging of Auditory Cortex in Adult Cats using High-field fMRI

Published on: February 19, 2014

12.1K
Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
11:31

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

15.8K

Related Experiment Videos

Last Updated: Mar 26, 2026

Exploring Cognitive Functions in Babies, Children & Adults with Near Infrared Spectroscopy
12:40

Exploring Cognitive Functions in Babies, Children & Adults with Near Infrared Spectroscopy

Published on: July 28, 2009

21.2K
Functional Imaging of Auditory Cortex in Adult Cats using High-field fMRI
10:50

Functional Imaging of Auditory Cortex in Adult Cats using High-field fMRI

Published on: February 19, 2014

12.1K
Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
11:31

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

15.8K

Area of Science:

  • Neuroscience
  • Auditory Perception
  • Brain-Computer Interfaces

Background:

  • The auditory cortex processes diverse sounds crucial for daily function.
  • Distinguishing sound-evoked brain activity is vital for understanding auditory processing and developing brain-computer interfaces (BCIs).
  • Functional near-infrared spectroscopy (fNIRS) offers a non-invasive method to measure hemodynamic responses (HRs) in the brain.

Purpose of the Study:

  • To investigate the feasibility of using fNIRS to differentiate auditory cortex activations caused by various sound categories.
  • To explore hemispheric differences in auditory processing and their implications for BCI applications.
  • To compare the efficacy of different machine learning classifiers for decoding auditory stimuli.

Main Methods:

  • fNIRS was used to measure hemodynamic responses (oxy-hemoglobin signals) in 18 subjects exposed to four sound categories: English speech, non-English speech, annoying sounds, and nature sounds.
  • Features extracted from the oxy-hemoglobin signals included mean, slope, and skewness.
  • Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) classifiers were employed for sound category classification.

Main Results:

  • Understandable (English) speech evoked broader and higher magnitude hemodynamic responses than non-English speech.
  • Annoying sounds activated a wider brain region than nature sounds, although nature sounds yielded stronger signal magnitudes.
  • Classification performance was higher in the left hemisphere compared to the right hemisphere, with LDA outperforming SVM.

Conclusions:

  • fNIRS can successfully distinguish between different sound categories based on auditory cortex activity.
  • The left hemisphere is more effective for decoding auditory commands, particularly for differentiating between annoying and nature sounds.
  • These findings support the potential of fNIRS-based BCIs for auditory command decoding.