Department of ECE, University of Florida, Gainesville, FL 32611, USA.
Sensors (Basel). 2023 Jun 14;23(12):5575. doi: 10.3390/s23125575.
Brain-Computer Interfaces (BCIs) have become increasingly popular in recent years due to their potential applications in diverse fields, ranging from the medical sector (people with motor and/or communication disabilities), cognitive training, gaming, and Augmented Reality/Virtual Reality (AR/VR), among other areas. BCI which can decode and recognize neural signals involved in speech and handwriting has the potential to greatly assist individuals with severe motor impairments in their communication and interaction needs. Innovative and cutting-edge advancements in this field have the potential to develop a highly accessible and interactive communication platform for these people. The purpose of this review paper is to analyze the existing research on handwriting and speech recognition from neural signals. So that the new researchers who are interested in this field can gain thorough knowledge in this research area. The current research on neural signal-based recognition of handwriting and speech has been categorized into two main types: invasive and non-invasive studies. We have examined the latest papers on converting speech-activity-based neural signals and handwriting-activity-based neural signals into text data. The methods of extracting data from the brain have also been discussed in this review. Additionally, this review includes a brief summary of the datasets, preprocessing techniques, and methods used in these studies, which were published between 2014 and 2022. This review aims to provide a comprehensive summary of the methodologies used in the current literature on neural signal-based recognition of handwriting and speech. In essence, this article is intended to serve as a valuable resource for future researchers who wish to investigate neural signal-based machine-learning methods in their work.
脑机接口（BCIs）近年来越来越受欢迎，因为它们在医疗领域（患有运动和/或交流障碍的人）、认知训练、游戏、增强现实/虚拟现实（AR/VR）等领域的潜在应用。BCI 可以解码和识别涉及言语和手写的神经信号，有潜力极大地满足严重运动障碍者的交流和互动需求。该领域的创新和前沿进展有可能为这些人开发一个高度可访问和互动的交流平台。本文旨在分析基于神经信号的手写和语音识别的现有研究。以便对这个领域感兴趣的新研究人员可以在这个研究领域获得全面的知识。基于神经信号的手写和语音识别的当前研究分为两种主要类型：侵入性和非侵入性研究。我们研究了最新的关于将基于语音活动的神经信号和基于手写活动的神经信号转换为文本数据的论文。在这篇综述中还讨论了从大脑中提取数据的方法。此外，这篇综述还简要总结了这些研究中使用的数据集、预处理技术和方法，这些研究发表于 2014 年至 2022 年之间。这篇综述旨在提供基于神经信号的手写和语音识别当前文献中使用的方法学的全面总结。从本质上讲，本文旨在为希望在工作中研究基于神经信号的机器学习方法的未来研究人员提供有价值的资源。
