# Wireless Mouth Motion Recognition System Based on EEG-EMG Sensors for Severe Speech Impairments

## TL;DR

This paper introduces a wireless system using brain and muscle signals to recognize mouth movements, helping people with severe speech impairments communicate.

## Contribution

A novel wireless EEG-EMG wearable system is proposed for detecting mouth movements and phonemes with high accuracy.

## Key findings

- A new signal processing method was developed for sensor integration and machine learning applications.
- A few-shot neural network achieved 95% accuracy in classifying phonemes from EEG-EMG signals.
- The system shows promise for nonverbal communication aids for individuals with paralysis or speech impairments.

## Abstract

This study aims to demonstrate the feasibility of using a new wireless electroencephalography (EEG)–electromyography (EMG) wearable approach to generate characteristic EEG-EMG mixed patterns with mouth movements in order to detect distinct movement patterns for severe speech impairments. This paper describes a method for detecting mouth movement based on a new signal processing technology suitable for sensor integration and machine learning applications. This paper examines the relationship between the mouth motion and the brainwave in an effort to develop nonverbal interfacing for people who have lost the ability to communicate, such as people with paralysis. A set of experiments were conducted to assess the efficacy of the proposed method for feature selection. It was determined that the classification of mouth movements was meaningful. EEG-EMG signals were also collected during silent mouthing of phonemes. A few-shot neural network was trained to classify the phonemes from the EEG-EMG signals, yielding classification accuracy of 95%. This technique in data collection and processing bioelectrical signals for phoneme recognition proves a promising avenue for future communication aids.