Panachakel Jerrin Thomas, Ramakrishnan Angarai Ganesan
Medical Intelligence and Language Engineering Laboratory, Department of Electrical Engineering, Indian Institute of Science, Bangalore, India.
Front Neurosci. 2021 Apr 29;15:642251. doi: 10.3389/fnins.2021.642251. eCollection 2021.
Over the past decade, many researchers have come up with different implementations of systems for decoding covert or imagined speech from EEG (electroencephalogram). They differ from each other in several aspects, from data acquisition to machine learning algorithms, due to which, a comparison between different implementations is often difficult. This review article puts together all the relevant works published in the last decade on decoding imagined speech from EEG into a single framework. Every important aspect of designing such a system, such as selection of words to be imagined, number of electrodes to be recorded, temporal and spatial filtering, feature extraction and classifier are reviewed. This helps a researcher to compare the relative merits and demerits of the different approaches and choose the one that is most optimal. Speech being the most natural form of communication which human beings acquire even without formal education, imagined speech is an ideal choice of prompt for evoking brain activity patterns for a BCI (brain-computer interface) system, although the research on developing real-time (online) speech imagery based BCI systems is still in its infancy. Covert speech based BCI can help people with disabilities to improve their quality of life. It can also be used for covert communication in environments that do not support vocal communication. This paper also discusses some future directions, which will aid the deployment of speech imagery based BCI for practical applications, rather than only for laboratory experiments.
在过去十年中，许多研究人员提出了不同的系统实现方案，用于从脑电图（EEG）中解码隐蔽或想象的语音。从数据采集到机器学习算法，它们在几个方面彼此不同，因此，不同实现之间的比较往往很困难。这篇综述文章将过去十年中发表的所有关于从脑电图中解码想象语音的相关工作整合到一个单一框架中。对设计这样一个系统的每个重要方面进行了综述，例如要想象的单词选择、要记录的电极数量、时间和空间滤波、特征提取和分类器。这有助于研究人员比较不同方法的相对优缺点，并选择最优化的方法。语音是人类即使没有接受正规教育也能掌握的最自然的交流形式，想象语音是为脑机接口（BCI）系统唤起大脑活动模式的理想提示选择，尽管基于实时（在线）语音想象的BCI系统的研究仍处于起步阶段。基于隐蔽语音的BCI可以帮助残疾人提高生活质量。它还可用于不支持语音通信的环境中的隐蔽通信。本文还讨论了一些未来的方向，这将有助于基于语音想象的BCI在实际应用中得到部署，而不仅仅用于实验室实验。
