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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

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Brain-computer interfacing based on cognitive control.

Mariska J Vansteensel¹, Dora Hermes, Erik J Aarnoutse

¹Section of Brain Function and Plasticity, Department of Neurology and Neurosurgery, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, the Netherlands.

Annals of Neurology

|June 3, 2010

Summary

This summary is machine-generated.

Brain-computer interfaces (BCIs) can utilize the cognitive control network for communication and environmental control in paralyzed individuals. Functional magnetic resonance imaging (fMRI) is feasible for localizing these networks noninvasively.

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Assessment and Communication for People with Disorders of Consciousness

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Published on: August 1, 2017

Area of Science:

Neuroscience
Biomedical Engineering
Cognitive Science

Background:

Brain-computer interfaces (BCIs) offer communication and control for paralyzed individuals.
Current BCIs primarily focus on the sensorimotor cortex, but alternative networks are needed.
The cognitive control network is a potential alternative for BCI applications.

Purpose of the Study:

Investigate the cognitive control network for BCI applications.
Determine the feasibility of using fMRI for noninvasive localization of the cognitive control network.

Main Methods:

Three epilepsy patients with subdural grid electrodes in the left dorsolateral prefrontal cortex (DLPFC) were studied.
Electrocorticographic (ECoG) signals from the DLPFC were used for BCI control.
Functional magnetic resonance imaging (fMRI) was used for prelocalization, confirmed with ECoG during mental calculation tasks.

Main Results:

All subjects achieved rapid and accurate BCI control by modulating gamma-power in the left DLPFC.
fMRI successfully prelocalized the cognitive control network.
ECoG signals confirmed the fMRI findings during localizer tasks.

Conclusions:

The cognitive control network is a viable source for BCI signals.
Translating neuroimaging insights into clinical BCI applications is feasible.