Kim Jongin, Lee Suh-Kyung, Lee Boreom
Department of Medical System Engineering (DMSE), Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea.
J Neural Eng. 2014 Jun;11(3):036010. doi: 10.1088/1741-2560/11/3/036010. Epub 2014 May 8.
The objective of this study is to find components that might be related to phoneme representation in the brain and to discriminate EEG responses for each speech sound on a trial basis.
We used multivariate empirical mode decomposition (MEMD) and common spatial pattern for feature extraction. We chose three vowel stimuli, /a/, /i/ and /u/, based on previous findings, such that the brain can detect change in formant frequency (F2) of vowels. EEG activity was recorded from seven native Korean speakers at Gwangju Institute of Science and Technology. We applied MEMD over EEG channels to extract speech-related brain signal sources, and looked for the intrinsic mode functions which were dominant in the alpha bands. After the MEMD procedure, we applied the common spatial pattern algorithm for enhancing the classification performance, and used linear discriminant analysis (LDA) as a classifier.
The brain responses to the three vowels could be classified as one of the learned phonemes on a single-trial basis with our approach.
The results of our study show that brain responses to vowels can be classified for single trials using MEMD and LDA. This approach may not only become a useful tool for the brain-computer interface but it could also be used for discriminating the neural correlates of categorical speech perception.
本研究的目的是找出可能与大脑中音素表征相关的成分，并在每次试验的基础上区分每个语音的脑电图反应。
我们使用多变量经验模态分解（MEMD）和共同空间模式进行特征提取。基于先前的研究结果，我们选择了三个元音刺激，即/a/、/i/和/u/，以便大脑能够检测元音共振峰频率（F2）的变化。在光州科学技术院，我们记录了七名以韩语为母语者的脑电图活动。我们对脑电图通道应用MEMD以提取与语音相关的脑信号源，并寻找在阿尔法波段占主导地位的固有模态函数。在MEMD过程之后，我们应用共同空间模式算法来提高分类性能，并使用线性判别分析（LDA）作为分类器。
使用我们的方法，可以在单次试验的基础上，将对这三个元音的大脑反应分类为已学音素之一。
我们的研究结果表明，使用MEMD和LDA可以在单次试验中对元音的大脑反应进行分类。这种方法不仅可能成为脑机接口的有用工具，还可用于区分分类语音感知的神经关联。
