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Related Experiment Video

Updated: Jul 5, 2025

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals

Published on: May 23, 2017

Decoding Single and Paired Phonemes Using 7T Functional MRI.

Maria Araújo Vitória¹, Francisco Guerreiro Fernandes¹, Max van den Boom^1,2

¹Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands.

Brain Topography

|January 23, 2024

Summary

This summary is machine-generated.

Brain activity from individual phonemes can be decoded, even when combined. This supports the feasibility of speech brain-computer interfaces (BCIs) using machine learning on sensorimotor cortex data.

Keywords:

Functional Magnetic Resonance Imaging; Speech brain-computer Interface Phonemes Sensorimotor Speech Neuroprosthesis Speech Production

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Last Updated: Jul 5, 2025

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals

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Published on: May 23, 2017

Functional Magnetic Resonance Imaging fMRI with Auditory Stimulation in Songbirds

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Mapping the After-effects of Theta Burst Stimulation on the Human Auditory Cortex with Functional Imaging

Mapping the After-effects of Theta Burst Stimulation on the Human Auditory Cortex with Functional Imaging

Published on: September 12, 2012

Area of Science:

Neuroscience
Cognitive Science
Biomedical Engineering

Background:

Mouth movements for phoneme pronunciation are represented in the sensorimotor cortex.
This neural representation theoretically enables brain-computer interfaces (BCIs) for continuous speech decoding.

Purpose of the Study:

Investigate the decodability of individual and paired phonemes using sensorimotor cortex activity.
Determine if classifiers trained on single phonemes can decode combined phonemes.

Main Methods:

Functional magnetic resonance imaging (fMRI) at 7 Tesla.
Fifteen participants pronounced single and paired phonemes.
Support Vector Machine (SVM) classification and searchlight analysis.

Main Results:

SVM classification of single and paired phonemes was successful.
Classifying paired phonemes using single-phoneme classifiers achieved 53% accuracy (33% chance level).
Phoneme representations are widely distributed in the ventral sensorimotor cortex.

Conclusions:

Neural activity of isolated phonemes is present and distinguishable within combined phonemes.
Findings support the feasibility of speech BCIs using machine learning algorithms trained on individual phonemes.
This research has implications for developing advanced BCIs with intracranial electrode grids.