Chowdhury Adiba Tabassum, Hassanein Ahmed, Al Shibli Aous N, Khanafer Youssuf, AbuHaweeleh Mohannad Natheef, Pedersen Shona, Chowdhury Muhammad E H
Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, Bangladesh.
Department of Basic Medical Science, College of Medicine, Qatar University, QU Health, Doha, Qatar.
Front Hum Neurosci. 2025 Jul 10;19:1637174. doi: 10.3389/fnhum.2025.1637174. eCollection 2025.
Inner speech recognition (ISR) is an emerging field with significant potential for applications in brain-computer interfaces (BCIs) and assistive technologies. This review focuses on the critical role of machine learning (ML) in decoding inner speech, exploring how various ML techniques improve the analysis and classification of neural signals. We analyze both traditional methods such as support vector machines (SVMs) and random forests, as well as advanced deep learning approaches like convolutional neural networks (CNNs), which are particularly effective at capturing the dynamic and non-linear patterns of inner speech-related brain activity. Also, the review covers the challenges of acquiring high-quality neural signals and discusses essential preprocessing methods for enhancing signal quality. Additionally, we outline and synthesize existing approaches for improving ISR through ML, that can lead to many potential implications in several domains, including assistive communication, brain-computer interfaces, and cognitive monitoring. The limitations of current technologies were also discussed, along with insights into future advancements and potential applications of machine learning in inner speech recognition (ISR). Building on prior literature, this work synthesizes and organizes existing ISR methodologies within a structured mathematical framework, reviews cognitive models of inner speech, and presents a detailed comparative analysis of existing ML approaches, thereby offering new insights into advancing the field.
内心言语识别（ISR）是一个新兴领域，在脑机接口（BCI）和辅助技术方面具有巨大的应用潜力。本综述重点关注机器学习（ML）在解码内心言语中的关键作用，探讨各种ML技术如何改进神经信号的分析和分类。我们分析了传统方法，如支持向量机（SVM）和随机森林，以及先进的深度学习方法，如卷积神经网络（CNN），它们在捕捉与内心言语相关的大脑活动的动态和非线性模式方面特别有效。此外，本综述涵盖了获取高质量神经信号的挑战，并讨论了提高信号质量的基本预处理方法。此外，我们概述并综合了通过ML改进ISR的现有方法，这些方法可能在多个领域产生许多潜在影响，包括辅助通信、脑机接口和认知监测。还讨论了当前技术的局限性，以及对机器学习在内心言语识别（ISR）中的未来进展和潜在应用的见解。基于先前的文献，这项工作在一个结构化的数学框架内综合并组织了现有的ISR方法，回顾了内心言语的认知模型，并对现有的ML方法进行了详细的比较分析，从而为推动该领域的发展提供了新的见解。
