Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Correction to: Towards Network-Based Neurofeedback of Visual Areas to Enhance Visual Perception.

Brain topography·2026

Same author

Reopening Motor Learning Windows: Targeted Re-Engagement of Latent Pathways via Non-Invasive Neuromodulation.

Life (Basel, Switzerland)·2026

Same author

Towards Network-Based Neurofeedback of Visual Areas to Enhance Visual Perception.

Brain topography·2026

Same author

An out-of-the-lab evaluation of dry EEG technology on a large-scale motor imagery brain-computer interface dataset.

Journal of neural engineering·2025

Same author

Emergent technologies in clinical neurophysiology to study the central nervous system: IFCN handbook chapter.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology·2025

Same author

Electrical spinal cord stimulation promotes focal sensorimotor activation that accelerates brain-computer interface skill learning.

Proceedings of the National Academy of Sciences of the United States of America·2025

Same journal

Ultra-flexible wireless endovascular stimulator for cortical simulation.

Journal of neural engineering·2026

Same journal

Influence of frequency and pulse train duration on respiratory responses during transcutaneous phrenic nerve stimulation in humans.

Journal of neural engineering·2026

Same journal

Dynamic functional graph-Laplacian priors integrated with optimization for EEG source localization.

Journal of neural engineering·2026

Same journal

Unveiling subject-specific causal latency in motor imagery: a physiologically transparent BCI via Riemannian tangent space fusion.

Journal of neural engineering·2026

Same journal

Cross-subject decoding of human neural data for speech Brain Computer Interfaces.

Journal of neural engineering·2026

Same journal

Cognitive and brain function enhancement in Gen X group after personalized, AI supervised EEG-neurofeedback training.

Journal of neural engineering·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 20, 2025

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Published on: September 18, 2012

Closed-loop EEG study on visual recognition during driving.

Ruslan Aydarkhanov¹, Marija Ušćumlić^2,3, Ricardo Chavarriaga^4,5

¹Medical Image Processing Laboratory, Center for Neuroprosthetics, Interschool Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech H4, 1202 Geneva, Switzerland.

Journal of Neural Engineering

|January 25, 2021

Summary

This summary is machine-generated.

This study demonstrates decoding visual recognition using electroencephalogram (EEG) signals during simulated driving, paving the way for brain-computer interfaces (BCIs) that adapt to natural behavior.

Keywords:

brain–computer interfaces driving electroencephalography eye tracking visual recognition

More Related Videos

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Published on: June 17, 2019

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram

Published on: March 13, 2018

Related Experiment Videos

Last Updated: Nov 20, 2025

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Published on: September 18, 2012

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Published on: June 17, 2019

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram

Published on: March 13, 2018

Area of Science:

Neuroscience
Human-Computer Interaction
Biomedical Engineering

Background:

Traditional brain-computer interfaces (BCIs) use rigid structures, limiting real-world application.
Decoding electroencephalogram (EEG) signals during natural activities like driving is challenging due to unconstrained behavior.

Purpose of the Study:

To investigate the feasibility of decoding visual recognition EEG signatures under naturalistic driving conditions.
To explore brain-computer interface (BCI) applications that accommodate natural ocular behavior in dynamic environments.

Main Methods:

Subjects performed visual search tasks in a car simulator, with eye movements and EEG recorded.
Eye fixation-related potentials (EFRPs) were analyzed for decoding performance on single trials.
A closed-loop BCI system integrated multiple EFRP decoding probabilities.

Main Results:

The BCI system achieved an average online accuracy of 0.37 ± 0.06, significantly above chance.
Classification algorithms and feature spaces showed moderate performance (up to 0.6 AUC), also above chance.
Gaze duration (dwell time) was identified as an informative feature for decoding.

Conclusions:

Visual recognition of sudden events can be decoded from EEG signals during active driving.
This research provides a foundation for developing driver assistance and recommender systems using brain signals.