Rahman Nimra, Khan Danish Mahmood, Masroor Komal, Arshad Mehak, Rafiq Amna, Fahim Syeda Maham
Department of Telecommunications Engineering, NED University of Engineering and Technology, Karachi, Sindh 75270 Pakistan.
Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Sindh 75270 Pakistan.
Cogn Neurodyn. 2024 Dec;18(6):3565-3583. doi: 10.1007/s11571-024-10167-0. Epub 2024 Sep 4.
Numerous individuals encounter challenges in verbal communication due to various factors, including physical disabilities, neurological disorders, and strokes. In response to this pressing need, technology has actively pursued solutions to bridge the communication gap, recognizing the inherent difficulties faced in verbal communication, particularly in contexts where traditional methods may be inadequate. Electroencephalogram (EEG) has emerged as a primary non-invasive method for measuring brain activity, offering valuable insights from a cognitive neurodevelopmental perspective. It forms the basis for Brain-Computer Interfaces (BCIs) that provide a communication channel for individuals with neurological impairments, thereby empowering them to express themselves effectively. EEG-based BCIs, especially those adapted to decode imagined speech from EEG signals, represent a significant advancement in enabling individuals with speech disabilities to communicate through text or synthesized speech. By utilizing cognitive neurodevelopmental insights, researchers have been able to develop innovative approaches for interpreting EEG signals and translating them into meaningful communication outputs. To aid researchers in effectively addressing this complex challenge, this review article synthesizes key findings from state-of-the-art significant studies. It investigates into the methodologies employed by various researchers, including preprocessing techniques, feature extraction methods, and classification algorithms utilizing Deep Learning and Machine Learning approaches and their integration. Furthermore, the review outlines the potential avenues for future research, with the goal of advancing the practical implementation of EEG-based BCI systems for decoding imagined speech from a cognitive neurodevelopmental perspective.
许多人由于各种因素，包括身体残疾、神经紊乱和中风，在言语交流中遇到困难。针对这一迫切需求，技术积极寻求解决方案以弥合交流差距，认识到言语交流中存在的固有困难，特别是在传统方法可能不足的情况下。脑电图（EEG）已成为测量大脑活动的主要非侵入性方法，从认知神经发育的角度提供了有价值的见解。它构成了脑机接口（BCI）的基础，为神经功能受损的个体提供了一个交流渠道，从而使他们能够有效地表达自己。基于脑电图的脑机接口，特别是那些适用于从脑电图信号中解码想象中的言语的接口，代表了使言语残疾个体能够通过文本或合成语音进行交流的重大进展。通过利用认知神经发育的见解，研究人员能够开发出创新方法来解释脑电图信号并将其转化为有意义的交流输出。为了帮助研究人员有效应对这一复杂挑战，这篇综述文章综合了最新重要研究的关键发现。它研究了不同研究人员采用的方法，包括预处理技术、特征提取方法以及利用深度学习和机器学习方法的分类算法及其整合。此外，该综述概述了未来研究的潜在途径，目标是从认知神经发育的角度推进基于脑电图的脑机接口系统用于解码想象中的言语的实际应用。
