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

Barriers and Facilitators for Implementing Music Interventions in Care Homes for People with Dementia and Depression: Process Evaluation Results of the Multinational Cluster-Randomized MIDDEL Trial.

Behavioral sciences (Basel, Switzerland)·2025
Same author

Human Brain Responses and Functional Brain Networks Across the Lifespan.

Brain sciences·2025
Same author

Socioeconomic Inequalities Affect Brain Responses of Infants Growing Up in Germany.

Brain sciences·2024
Same author

From co-regulation to self-regulation: Maternal soothing strategies and self-efficacy in relation to maternal reports of infant regulation at 3 and 7 months.

Infant mental health journal·2024
Same author

Maternal self-efficacy development from pregnancy to 3 months after birth.

Infant mental health journal·2022

Related Experiment Video

Updated: May 23, 2025

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection
08:08

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection

Published on: May 31, 2024

745

Evaluating machine- and deep learning approaches for artifact detection in infant EEG: classifier performance,

R Kemmerich1, A Wienke1, U Frischen1

  • 1Bremer Initiative to Foster Early Childhood Development (BRISE), Faculty for Human and Health Sciences, University of Bremen, Bremen, Germany.

Biomedical Physics & Engineering Express
|May 12, 2025
PubMed
Summary

Automated methods using Random Forest and deep learning effectively detect artifacts in infant electroencephalography (EEG) data. These machine learning approaches reduce manual labor and improve consistency in infant brain activity research.

Keywords:
EEGartifact detectiondeep learninginfantmachine learning

More Related Videos

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

10.8K
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.2K

Related Experiment Videos

Last Updated: May 23, 2025

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection
08:08

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection

Published on: May 31, 2024

745
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

10.8K
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.2K

Area of Science:

  • Neuroscience and Biomedical Engineering
  • Developmental Psychology

Background:

  • Electroencephalography (EEG) is crucial for infant brain activity research but is prone to artifacts from infant movement and variability.
  • Manual artifact detection in EEG is time-consuming and subjective, necessitating automated solutions for reliable analysis.

Purpose of the Study:

  • To evaluate the performance of machine learning classifiers (Random Forest, SVM, DL) for automated artifact detection in infant EEG.
  • To assess classifiers without requiring prior feature extraction, directly processing raw, filtered infant EEG data.

Main Methods:

  • Collected EEG data from 294 infants (mean age 8.34 months) as part of the BRISE study.
  • Analyzed 66,851 epochs, with 45% manually annotated as artifacts by an expert.
  • Trained and tested Random Forest, SVM, and deep learning models on filtered EEG data.

Main Results:

  • Random Forest (RF) and deep learning (DL) models achieved high balanced accuracy (0.873 and 0.881, respectively), significantly outperforming SVM (0.756).
  • RF excelled with smaller datasets, while DL required larger datasets for optimal performance.
  • A trade-off exists between classifier certainty, accuracy, and the proportion of data classified.

Conclusions:

  • RF and DL classifiers offer effective automated artifact detection for infant EEG, reducing preprocessing time and enhancing study consistency.
  • Automated artifact detection can facilitate large-scale infant EEG research and improve reproducibility through standardized preprocessing.