Centre for Healthcare Technologies, Department of Biomedical Engineering, SSN College of Engineering, Rajiv Gandhi Salai (OMR), Kalavakkam, Tamil Nadu, 603110, India.
Cogn Process. 2022 Nov;23(4):593-618. doi: 10.1007/s10339-022-01103-3. Epub 2022 Jul 6.
Articulation imagery, a form of mental imagery, refers to the activity of imagining or speaking to oneself mentally without an articulation movement. It is an effective domain of research in speech impaired neural disorders, as speech imagination has high similarity to real voice communication. This work employs electroencephalography (EEG) signals acquired from articulation and articulation imagery in identifying the vowel being imagined during different tasks. EEG signals from chosen electrodes are decomposed using the empirical mode decomposition (EMD) method into a series of intrinsic mode functions. Brain connectivity estimators and entropy measures have been computed to analyze the functional cooperation and causal dependence between different cortical regions as well as the regularity in the signals. Using machine learning techniques such as multiclass support vector machine (MSVM) and random forest (RF), the vowels have been classified. Three different training and testing protocols (Articulation-AR, Articulation imagery-AI and Articulation vs Articulation imagery-AR vs AI) were employed for identifying the vowel being imagined of articulating. An overall classification accuracy of 80% was obtained for articulation imagery protocol which was found to be higher than the other two protocols. Also, MSVM techniques outperformed the RF technique in terms of the classification accuracy. The effect of brain connectivity estimators and machine learning techniques seems to be reliable in identifying the vowel from the subjects' thought and thereby assisting the people with speech impairment.
发音意象，一种心理意象形式，是指在没有发音运动的情况下，想象或自言自语的活动。它是言语障碍神经紊乱研究的一个有效领域，因为言语想象与真实的语音交流具有高度的相似性。这项工作利用脑电图 (EEG) 信号，从发音和发音意象中识别出在不同任务中想象的元音。选择电极的 EEG 信号使用经验模态分解 (EMD) 方法分解为一系列固有模式函数。脑连接估计器和熵测度已被计算出来，以分析不同皮质区域之间的功能合作和因果依赖关系以及信号的规律性。使用机器学习技术，如多类支持向量机 (MSVM) 和随机森林 (RF)，对元音进行分类。采用了三种不同的训练和测试协议（发音-AR、发音意象-AI 和发音与发音意象-AR 与 AI）来识别发音的元音。发音意象协议的总体分类准确率为 80%，高于其他两种协议。此外，MSVM 技术在分类准确率方面优于 RF 技术。脑连接估计器和机器学习技术的效果似乎可以可靠地从受试者的思维中识别出元音，从而帮助言语障碍者。
