Interdisciplinary Program of Biomedical Engineering, Faculty of Engineering, Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand.
National Electronics and Computer Technology Center, 112 Thailand Science Park, Klong Luang, Pathumthani 12120, Thailand.
Sensors (Basel). 2022 Apr 1;22(7):2702. doi: 10.3390/s22072702.
Speech discrimination is used by audiologists in diagnosing and determining treatment for hearing loss patients. Usually, assessing speech discrimination requires subjective responses. Using electroencephalography (EEG), a method that is based on event-related potentials (ERPs), could provide objective speech discrimination. In this work we proposed a visual-ERP-based method to assess speech discrimination using pictures that represent word meaning. The proposed method was implemented with three strategies, each with different number of pictures and test sequences. Machine learning was adopted to classify between the task conditions based on features that were extracted from EEG signals. The results from the proposed method were compared to that of a similar visual-ERP-based method using letters and a method that is based on the auditory mismatch negativity (MMN) component. The P3 component and the late positive potential (LPP) component were observed in the two visual-ERP-based methods while MMN was observed during the MMN-based method. A total of two out of three strategies of the proposed method, along with the MMN-based method, achieved approximately 80% average classification accuracy by a combination of support vector machine (SVM) and common spatial pattern (CSP). Potentially, these methods could serve as a pre-screening tool to make speech discrimination assessment more accessible, particularly in areas with a shortage of audiologists.
言语辨别能力通常用于听力损失患者的诊断和治疗。评估言语辨别能力通常需要主观反应。使用脑电图（EEG），一种基于事件相关电位（ERP）的方法，可以提供客观的言语辨别能力。在这项工作中，我们提出了一种基于视觉 ERP 的方法，使用代表词义的图片来评估言语辨别能力。所提出的方法采用了三种策略，每种策略使用的图片和测试序列数量不同。机器学习被用于基于从 EEG 信号中提取的特征来对任务条件进行分类。所提出的方法的结果与基于字母的类似视觉 ERP 方法和基于听觉失匹配负波（MMN）成分的方法进行了比较。在这两种基于视觉 ERP 的方法中观察到 P3 成分和晚期正电位（LPP）成分，而在基于 MMN 的方法中观察到 MMN。所提出的方法的三种策略中有两种，以及基于 MMN 的方法，通过支持向量机（SVM）和共同空间模式（CSP）的组合，平均分类准确率达到了 80%左右。这些方法可能可以作为一种预筛选工具，使言语辨别能力评估更容易获得，特别是在缺乏听力学家的地区。
