# Decoding Single and Paired Phonemes Using 7T Functional MRI

## TL;DR

This study shows that brain activity related to pronouncing individual and paired phonemes can be decoded using 7T MRI, supporting the potential for speech-based brain-computer interfaces.

## Contribution

The study demonstrates that paired phonemes can be classified with above-chance accuracy using classifiers trained on single phonemes.

## Key findings

- Single and paired phonemes can be decoded from sensorimotor cortex activity using SVM classification.
- Paired phonemes were classified with 53% accuracy, significantly above the 33% chance level.
- Phoneme representations are widely distributed in the ventral sensorimotor cortex.

## Abstract

Several studies have shown that mouth movements related to the pronunciation of individual phonemes are represented in the sensorimotor cortex. This would theoretically allow for brain computer interfaces that are capable of decoding continuous speech by training classifiers based on the activity in the sensorimotor cortex related to the production of individual phonemes. To address this, we investigated the decodability of trials with individual and paired phonemes (pronounced consecutively with one second interval) using activity in the sensorimotor cortex. Fifteen participants pronounced 3 different phonemes and 3 combinations of two of the same phonemes in a 7T functional MRI experiment. We confirmed that support vector machine (SVM) classification of single and paired phonemes was possible. Importantly, by combining classifiers trained on single phonemes, we were able to classify paired phonemes with an accuracy of 53% (33% chance level), demonstrating that activity of isolated phonemes is present and distinguishable in combined phonemes. A SVM searchlight analysis showed that the phoneme representations are widely distributed in the ventral sensorimotor cortex. These findings provide insights about the neural representations of single and paired phonemes. Furthermore, it supports the notion that speech BCI may be feasible based on machine learning algorithms trained on individual phonemes using intracranial electrode grids.