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

A Robust Approach to Quantify Nocifensive Blink Reflex Responsiveness.

The European journal of neuroscience·2026
Same author

Genotyping of Candida tropicalis isolates uncovers nosocomial transmission of two lineages in Italian tertiary care hospital.

The Journal of hospital infection·2024
Same author

Cortico-spinal modularity in the parieto-frontal system: A new perspective on action control.

Progress in neurobiology·2023
Same author

Fetal pain and its relevance to abortion policy.

Nature neuroscience·2022
Same author

Brain Responses to Surprising Stimulus Offsets: Phenomenology and Functional Significance.

Cerebral cortex (New York, N.Y. : 1991)·2021
Same author

Waves of Change: Brain Sensitivity to Differential, not Absolute, Stimulus Intensity is Conserved Across Humans and Rats.

Cerebral cortex (New York, N.Y. : 1991)·2020

Related Experiment Video

Updated: Dec 1, 2025

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
10:35

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Published on: June 3, 2013

33.1K

Local spatial analysis: an easy-to-use adaptive spatial EEG filter.

R J Bufacchi1,2, C Magri2, G Novembre1,2

  • 1Neuroscience and Behaviour Laboratory, Istituto Italiano di Tecnologia, Rome, Italy.

Journal of Neurophysiology
|November 11, 2020
PubMed
Summary

Local Spatial Analysis (LSA) is a novel, adaptive EEG spatial filter that overcomes limitations of stationary filters. This easy-to-use method effectively isolates event-related potential (ERP) components masked by widespread activity.

Keywords:
EEG componentselectroencephalography (EEG)event-related potentials (ERPs)referencingspatial filtering

More Related Videos

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

12.0K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.0K

Related Experiment Videos

Last Updated: Dec 1, 2025

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
10:35

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Published on: June 3, 2013

33.1K
Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

12.0K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.0K

Area of Science:

  • Neuroscience
  • Signal Processing
  • Computational Biology

Background:

  • Spatial filters are crucial for isolating event-related potential (ERP) components in electroencephalography (EEG).
  • Commonly used stationary filters (e.g., average reference, surface Laplacian) assume signal stationarity, which contradicts the nonstationary nature of ERPs, potentially leading to misinterpretations.
  • Existing adaptive filters (e.g., ICA, PCA) offer advantages but demand advanced statistical and physiological expertise.

Purpose of the Study:

  • To introduce Local Spatial Analysis (LSA), a novel, adaptive spatial filter for EEG analysis.
  • To provide an easy-to-use and conceptually simple method for highlighting local ERP components obscured by widespread activity.
  • To demonstrate LSA's effectiveness in overcoming the limitations of stationary filters and the complexity of other adaptive filters.

Main Methods:

  • LSA is an adaptive spatial filter that estimates filter parameters at each time point by exploiting trial-by-trial variability in stimulus-evoked neural activity.
  • The method utilizes linear regression to differentiate between local and widespread brain activity.
  • The filter's performance was evaluated using both simulated data and real EEG data from auditory, visual, tactile, and pain stimuli.

Main Results:

  • LSA effectively isolates local ERP components that are masked by larger, widespread activity.
  • The novel adaptive filter demonstrated superior performance compared to widely used stationary filters.
  • LSA enabled the identification of previously undetected ERP components across multiple sensory modalities.

Conclusions:

  • LSA offers a computationally simple yet powerful adaptive approach to EEG spatial filtering.
  • This method bridges the gap between the ease of use of stationary filters and the sophistication of advanced adaptive techniques.
  • LSA facilitates a more accurate interpretation of neural activity by effectively separating local and widespread brain signals, with freely available MATLAB implementation.