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Brain Imaging

Brain Imaging

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These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Related Experiment Video

Updated: Jun 13, 2026

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Published on: August 24, 2017

Using real-time fMRI to control a dynamical system by brain activity classification.

Anders Eklund¹, Henrik Ohlsson, Mats Andersson

¹Div. of Medical Informatics, Linköping University, Sweden. andek@imt.liu.se

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|April 30, 2010

Summary

This summary is machine-generated.

This study demonstrates real-time fMRI control of a dynamical system. Participants successfully balanced an inverted pendulum using brain activity, showcasing potential for communication and rehabilitation applications.

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Area of Science:

Neuroscience
Biomedical Engineering
Human-Computer Interaction

Background:

Real-time functional magnetic resonance imaging (fMRI) offers a non-invasive window into brain activity.
Controlling external systems with brain signals (Brain-Computer Interfaces - BCIs) has significant therapeutic potential.

Purpose of the Study:

To develop and evaluate a real-time fMRI-based method for controlling a dynamical system.
To enable subjects to manipulate an inverted pendulum using volitional brain activity.

Main Methods:

Utilized real-time fMRI to acquire brain activity data.
Employed a neural network for classifying brain states (left hand, right hand, rest) each second.
Integrated the neural network output with a pendulum simulator for closed-loop control.
Provided visual feedback of the pendulum's state via virtual reality (VR) goggles.

Main Results:

Subjects could successfully balance the inverted pendulum for several minutes using both actual and imagined hand movements.
The system utilized 9000 brain voxels per classification.
The average system response time for detecting activity changes was 2-4 seconds.

Conclusions:

Real-time fMRI can effectively control complex dynamical systems.
This BCI approach shows promise for individuals with communication impairments, such as those with locked-in syndrome.
Potential applications include neurorehabilitation for stroke and Parkinson's disease patients, offering real-time feedback for enhanced brain training.