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

Sensory Modalities01:15

Sensory Modalities

1.5K
Sensation typically is the process by which the sensory receptors and sense organs detect stimuli from the internal and external environment and transmit this information to the central nervous system for processing.
General senses refer to the broad category of sensory information detected by receptors in the body and can be further grouped into somatic and visceral senses. Somatic sensations include touch, pressure, temperature, and pain and are essential for navigating our environment and...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Lateral graphene-metallene interfaces at the nanoscale.

Nanoscale·2025
Same author

Diverse surface reconstructions in MAX phases.

Nanoscale·2025
Same author

Screening of Material Defects using Universal Machine-Learning Interatomic Potentials.

Small (Weinheim an der Bergstrasse, Germany)·2025
Same author

Gabapentin drug interactions in water and aqueous solutions of green betaine based compounds through volumetric, viscometric and interfacial properties.

Scientific reports·2025
Same author

Genomic alterations and transcriptional phenotypes in circulating free DNA and matched metastatic tumor.

Genome medicine·2025
Same author

Unraveling the effect of choline-based choline based ionic liquids on the physicochemical properties and taste behavior of D( +)-glucose in aqueous solutions.

BMC chemistry·2025
Same journal

Cortex-anchored sensor-space harmonics for event-related EEG.

Journal of neural engineering·2026
Same journal

Neural mechanisms of mixed speech and grasp representation in sensorimotor cortices.

Journal of neural engineering·2026
Same journal

Developing a binary communication protocol between biological neural networks using virtual white matter.

Journal of neural engineering·2026
Same journal

Spatiotemporally distinctive astrocytic and neuronal responses to repetitive intracortical microstimulation.

Journal of neural engineering·2026
Same journal

A neural mass modelling framework for evaluating EEG source localisation of seizure activity.

Journal of neural engineering·2026
Same journal

Functional and effective connectivity methods from SEEG for characterizing epileptogenic networks in refractory epilepsy: a comprehensive review and future directions.

Journal of neural engineering·2026
See all related articles

Related Experiment Video

Updated: Aug 11, 2025

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns
09:42

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

6.1K

EEG-based detection of modality-specific visual and auditory sensory processing.

Faghihe Massaeli1, Mohammad Bagheri1, Sarah D Power1,2

  • 1Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. Johns, Canada.

Journal of Neural Engineering
|February 7, 2023
PubMed
Summary
This summary is machine-generated.

Researchers developed a passive brain-computer interface (pBCI) capable of distinguishing between visual and auditory tasks using electroencephalography (EEG). This advancement allows for more tailored human-machine interactions by identifying the type of cognitive resources utilized, especially under high mental workload.

Keywords:
electroencephalographypassive brain computer interfacevisual and auditory processing

More Related Videos

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography
09:25

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Published on: July 26, 2019

7.0K
Quantitative Assessment of Cortical Auditory-tactile Processing in Children with Disabilities
09:38

Quantitative Assessment of Cortical Auditory-tactile Processing in Children with Disabilities

Published on: January 29, 2014

10.9K

Related Experiment Videos

Last Updated: Aug 11, 2025

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns
09:42

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

6.1K
Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography
09:25

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Published on: July 26, 2019

7.0K
Quantitative Assessment of Cortical Auditory-tactile Processing in Children with Disabilities
09:38

Quantitative Assessment of Cortical Auditory-tactile Processing in Children with Disabilities

Published on: January 29, 2014

10.9K

Area of Science:

  • Neuroscience
  • Human-Computer Interaction
  • Cognitive Science

Background:

  • Passive brain-computer interfaces (pBCI) monitor user mental states to adapt human-machine interactions.
  • Detecting mental workload level is common, but identifying the specific type of cognitive resources (e.g., visual vs. auditory) is crucial for more effective adaptations.
  • Current pBCI research primarily focuses on workload levels, overlooking the potential of differentiating resource types.

Purpose of the Study:

  • To investigate if electroencephalography (EEG) can differentiate between visual and auditory processing tasks.
  • To determine the impact of sensory processing demand levels on the accuracy of distinguishing task types.
  • To explore the feasibility of a pBCI that detects both the level and type of attentional resources.

Main Methods:

  • 15 participants performed designed visual and auditory tasks under varying demand levels.
  • Electroencephalography (EEG) data was recorded during task performance.
  • Traditional machine learning algorithms were employed to classify task types based on EEG signals.

Main Results:

  • Auditory and visual processing tasks were distinguished with 77.1% accuracy under high demand conditions.
  • Classification accuracy did not exceed chance levels in the low demand condition.
  • EEG signals show potential for differentiating cognitive resource types.

Conclusions:

  • The study supports the feasibility of developing pBCIs that can identify the type of attentional resources required.
  • High demand scenarios showed promising accuracy in distinguishing between visual and auditory tasks.
  • Further research is needed to establish demand thresholds for accurate type detection, but results are promising for safety-critical applications.