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Updated: May 8, 2026

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Two Seconds to Speak: Increasing Communication Speed for fMRI-Based Brain-Computer Interfaces.

Daniëlle Evenblij¹, Michael Lührs^1,2, Reebal W Rafeh³

¹Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.

Brain Connectivity

|September 16, 2025

Summary

This summary is machine-generated.

Functional magnetic resonance imaging (fMRI)-based brain-computer interfaces (BCIs) can be made more efficient using short mental tasks. Individualized task selection improves accuracy for yes/no communication in BCIs.

Keywords:

brain-based communication brain–computer interface mental imagery multi-voxel pattern analysis multiclass classification syndrome “locked-in”

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Published on: July 21, 2021

Area of Science:

Neuroscience
Biomedical Engineering
Human-Computer Interaction

Background:

Brain-computer interfaces (BCIs) offer motor-independent communication for individuals with lost motor function.
Functional magnetic resonance imaging (fMRI)-based BCIs utilize hemodynamic brain activity from mental tasks.
Improving the efficiency and speed of fMRI-BCIs is crucial for clinical applications due to the slow hemodynamic response.

Purpose of the Study:

To enhance brain-computer interface (BCI) efficiency by discriminating brain activation patterns from short (2-second) mental tasks.
To identify optimal mental task combinations for high-accuracy multi-class classification in fMRI-BCIs.
To compare accuracy-based versus preference-based individualized task selection for yes/no communication paradigms.

Main Methods:

Tested the reliability of discriminating distributed 3T-fMRI brain activation patterns from 2-second mental tasks across 2- to 7-class classifications.
Identified optimal mental task combinations for maximizing classification accuracy.
Evaluated individualized task selection strategies (accuracy-based vs. preference-based) for a yes/no communication task.

Main Results:

Achieved 78% mean accuracy for 2-class decoding, with 3- to 7-class accuracies above chance.
Mental calculation and spatial navigation tasks were most frequently linked to high decoding accuracy.
Yes/no answers were encoded with high accuracy (83% accuracy-based, 81% preference-based) using individualized tasks.

Conclusions:

Short mental tasks (2 seconds) can reliably evoke discriminable fMRI activation patterns, increasing BCI efficiency.
Individualized task selection, whether accuracy- or preference-based, significantly enhances communication accuracy in fMRI-BCIs.
This fMRI-BCI paradigm is suitable for diverse patient populations, offering potential for greatly improved clinical utility.