AlEssa Ghada N, Alzahrani Saleh I
Biomedical Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
Front Bioeng Biotechnol. 2025 Jan 7;12:1498401. doi: 10.3389/fbioe.2024.1498401. eCollection 2024.
Color vision deficiency (CVD), a common visual impairment, affects individuals' ability to differentiate between various colors due to malfunctioning or absent color photoreceptors in the retina. Currently available diagnostic tests require a behavioral response, rendering them unsuitable for individuals with limited physical and communication abilities, such as those with locked-in syndrome. This study introduces a novel, non-invasive method that employs brain signals, specifically Steady-State Visually Evoked Potentials (SSVEPs), along with Ishihara plates to diagnose CVD. This method aims to provide an alternative diagnostic tool that addresses the limitations of current tests.
Electroencephalography (EEG) recordings were obtained from 16 subjects, including 5 with CVD (specifically Deuteranomaly), using channels O1, O2, Pz, and Cz. The subjects were exposed to visual stimuli at frequencies of 15 Hz and 18 Hz to assess the proposed method. The subjects focused on specific visual stimuli in response to questions related to the Ishihara plates. Their responses were analyzed to determine the presence of CVD. Feature extraction was performed using Power Spectral Density (PSD), Canonical Correlation Analysis (CCA), and a combined PSD + CCA, followed by classification to categorize subjects into two classes: normal vision and CVD.
The results indicate that the proposed method effectively diagnoses CVD in individuals with limited communication abilities. The classification accuracy of SSVEP exceeded 75% across the three classifiers: Decision Tree (DT), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The SVM classifier demonstrated higher accuracy compared to the other classifiers, exceeding 90%.
These observations suggest that the SVM classifier, utilizing the combined feature set of PSD + CCA, may be the most effective in this classification task. These findings demonstrate that the proposed method is an accurate and reliable diagnostic tool for CVD, particularly for individuals unable to communicate.
色觉缺陷（CVD）是一种常见的视觉障碍，由于视网膜中色光感受器功能失常或缺失，影响个体区分各种颜色的能力。目前可用的诊断测试需要行为反应，这使得它们不适用于身体和沟通能力有限的个体，例如闭锁综合征患者。本研究引入了一种新颖的非侵入性方法，该方法利用脑信号，特别是稳态视觉诱发电位（SSVEP），结合石原氏色盲测试图来诊断CVD。该方法旨在提供一种替代诊断工具，以解决当前测试的局限性。
从16名受试者身上获取脑电图（EEG）记录，其中包括5名患有CVD（特别是绿色弱）的受试者，使用O1、O2、Pz和Cz通道。受试者暴露于15Hz和18Hz频率的视觉刺激下，以评估所提出的方法。受试者针对与石原氏色盲测试图相关的问题专注于特定的视觉刺激。分析他们的反应以确定是否存在CVD。使用功率谱密度（PSD）、典型相关分析（CCA）以及PSD + CCA组合进行特征提取，然后进行分类，将受试者分为两类：正常视力和CVD。
结果表明，所提出的方法能够有效地诊断沟通能力有限的个体的CVD。在决策树（DT）、k近邻（KNN）和支持向量机（SVM）这三种分类器中，SSVEP的分类准确率超过75%。与其他分类器相比，SVM分类器表现出更高的准确率，超过90%。
这些观察结果表明，利用PSD + CCA组合特征集的SVM分类器在该分类任务中可能是最有效的。这些发现表明，所提出的方法是一种准确可靠的CVD诊断工具，特别是对于无法沟通的个体。
