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

Population-Level Predictive Variation in Machine Learning Diagnosis of Symptomatic Bacterial Vaginosis.

npj women's health·2026
Same author

Primary prevention of Parkinson's disease: proceedings, the 8C's and a position statement from the Parkinson's disease prevention think tank.

NPJ Parkinson's disease·2026
Same author

Prediction of Alzheimer's disease risk factors from retinal images via deep learning: Development and validation of biologically relevant morphological associations in the UK Biobank.

Journal of Alzheimer's disease : JAD·2026
Same author

Towards tDCS Digital Twins using Deep Learning-based Direct Estimation of Personalized Electrical Field Maps from T1-Weighted MRI.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same author

A Single Reference-Guided Adaptation of Foundation Model Predictions for High-Performance Image Segmentation.

IEEE transactions on bio-medical engineering·2026
Same author

Imaging foundation model for universal enhancement of non-ideal measurement CT.

Nature communications·2026
Same journal

Self-Supervised Based Multi-View Graph Presentation Learning for Drug-Drug Interaction Prediction.

Transactions on artificial intelligence·2025
Same journal

Normalization and Selecting Non-Differentially Expressed Genes Improve Machine Learning Modelling of Cross-Platform Transcriptomic Data.

Transactions on artificial intelligence·2025
See all related articles

Related Experiment Video

Updated: Mar 28, 2026

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
06:11

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

Published on: September 26, 2025

1.2K

Recent Advancements of Transcranial Direct Current Stimulation and Machine Learning: Methods, Challenges, and

Junfu Cheng1, Tara Sahni2, Zeyun Zhao3

  • 1Department of Electrical and Computer Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, USA.

Transactions on Artificial Intelligence
|March 27, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) enhances transcranial direct current stimulation (tDCS) by analyzing diverse data to personalize neuromodulation. Future directions include multimodal data integration and adaptive closed-loop systems for precision medicine.

Keywords:
machine learningprecision neuromodulationtranscranial direct current stimulation (tDCS)

More Related Videos

Simultaneous EEG Monitoring During Transcranial Direct Current Stimulation
07:52

Simultaneous EEG Monitoring During Transcranial Direct Current Stimulation

Published on: June 17, 2013

40.5K
Neuronavigated Focalized Transcranial Direct Current Stimulation Administered During Functional Magnetic Resonance Imaging
09:33

Neuronavigated Focalized Transcranial Direct Current Stimulation Administered During Functional Magnetic Resonance Imaging

Published on: November 15, 2024

2.3K

Related Experiment Videos

Last Updated: Mar 28, 2026

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
06:11

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

Published on: September 26, 2025

1.2K
Simultaneous EEG Monitoring During Transcranial Direct Current Stimulation
07:52

Simultaneous EEG Monitoring During Transcranial Direct Current Stimulation

Published on: June 17, 2013

40.5K
Neuronavigated Focalized Transcranial Direct Current Stimulation Administered During Functional Magnetic Resonance Imaging
09:33

Neuronavigated Focalized Transcranial Direct Current Stimulation Administered During Functional Magnetic Resonance Imaging

Published on: November 15, 2024

2.3K

Area of Science:

  • Neuroscience and Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique.
  • Machine learning (ML) offers potential to advance tDCS from heuristic to adaptive interventions.

Purpose of the Study:

  • Synthesize recent (2020-2025) advancements at the intersection of tDCS and ML.
  • Evaluate the application of ML in tDCS research and identify future directions.

Main Methods:

  • Systematic literature search of PubMed and Google Scholar for peer-reviewed studies.
  • Inclusion criteria focused on studies applying ML techniques to tDCS.
  • Evaluation of data integrity and ML model validation in eligible studies.

Main Results:

  • Sixteen studies met inclusion criteria, utilizing heterogeneous datasets (EEG, neuroimaging, clinico-demographic).
  • ML applications included predicting outcomes, characterizing neural responses, and identifying tDCS sensitivity biomarkers.
  • Support vector machines and random forests were prevalent; evidence remains fragmented due to small cohorts.

Conclusions:

  • ML can reveal physiological structure, optimize tDCS dose, and advance precision neuromodulation.
  • Future directions include multimodal data integration, biophysically grounded models, and adaptive closed-loop systems.
  • ML-guided tDCS systems promise to be mechanistically informed, clinically actionable, and scalable.