Speech mode classification from electrocorticography: transfer between electrodes and participants

Aurélie de Borman ¹, Benjamin Wittevrongel ¹, Bob Van Dyck ¹

⁰Laboratory for Neuro- and Psychophysiology, KU Leuven, Leuven, Belgium.

Journal of Neural Engineering +

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July 22, 2025

View abstract on PubMed

Summary

This study developed accurate speech detectors for brain-computer interfaces (BCIs) by classifying brain activity during speaking, listening, and silence. These detectors show promise for real-world BCI applications by distinguishing intended speech from other language processing.

Area Of Science

Neuroscience
Biomedical Engineering
Machine Learning

Background

Speech brain-computer interfaces (BCIs) aim to restore communication for individuals with speech loss.
Speech detectors are crucial for BCIs to differentiate speech intent from silence.
Existing detectors must account for non-speaking language-related brain activity like reading or listening.

Purpose Of The Study

To analyze brain activity across various speech modes: speaking, listening, imagining speaking, reading, and mouthing.
To develop and evaluate a speech mode classifier using electrocorticography (ECoG) data.
To assess the transferability of trained classifiers across participants for single- and multi-electrode configurations.

Main Methods

Collected ECoG data from 29 participants performing different speech-related tasks.
Developed linear classifiers for speech mode detection.
Evaluated classification accuracy for single- and multi-electrode setups.
Assessed cross-participant classifier transferability for binary and multiclass scenarios.

Main Results

High classification accuracies achieved: 88.89% for single-electrode and 96.49% for multi-electrode classifiers distinguishing speaking, listening, and silence.
Optimal electrode locations identified on the superior temporal gyrus and sensorimotor cortex.
Single-electrode classifiers demonstrated successful transfer across recording sites.
Multi-electrode classifiers showed better transferability for binary tasks compared to multiclass tasks.

Conclusions

Accurate speech detection is vital for reliable speech BCIs, preventing false outputs and enabling use outside laboratory settings.
Cross-participant transfer of classifiers is valuable for reducing training time, especially when subject training is difficult.
The developed speech mode classifiers hold significant potential for advancing practical speech BCI technology.

Keywords:

brain–computer interface electrocorticography speech transfer learning