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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.
摘要：
这项研究旨在证明使用一种新的无线脑电图（EEG）-肌电图（EMG）可穿戴方法来生成具有嘴巴运动的特征性EEG-EMG混合模式的可行性，以便检测严重言语障碍的不同运动模式。本文介绍了一种基于适用于传感器集成和机器学习应用的新型信号处理技术的嘴巴运动检测方法。本文研究了嘴巴运动与脑电波之间的关系，以努力为失去沟通能力的人开发非语言接口，比如瘫痪的人。进行了一组实验以评估所提出的特征选择方法的功效。确定了口腔运动的分类是有意义的。在音素无声口时也收集了EEG-EMG信号。训练了少量神经网络来对EEG-EMG信号中的音素进行分类，产生95%的分类准确率。这种用于数据收集和处理生物电信号以进行音素识别的技术证明了未来通信辅助工具的有希望的途径。
