A novel non-invasive EEG-SSVEP diagnostic tool for color vision deficiency in individuals with locked-in syndrome
IntroductionColor 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 unsuitabl...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2024.1498401/full |
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| author | Ghada N. AlEssa Saleh I. Alzahrani |
| author_facet | Ghada N. AlEssa Saleh I. Alzahrani |
| author_sort | Ghada N. AlEssa |
| collection | DOAJ |
| description | IntroductionColor 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.MethodsElectroencephalography (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.ResultsThe 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%.DiscussionThese 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. |
| format | Article |
| id | doaj-art-44d34008e62e42148be38267d454c924 |
| institution | Kabale University |
| issn | 2296-4185 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj-art-44d34008e62e42148be38267d454c9242025-01-07T06:51:43ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852025-01-011210.3389/fbioe.2024.14984011498401A novel non-invasive EEG-SSVEP diagnostic tool for color vision deficiency in individuals with locked-in syndromeGhada N. AlEssaSaleh I. AlzahraniIntroductionColor 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.MethodsElectroencephalography (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.ResultsThe 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%.DiscussionThese 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.https://www.frontiersin.org/articles/10.3389/fbioe.2024.1498401/fullcolor vision deficiencydiagnosingEEGSSVEPsignal processingfeature extraction |
| spellingShingle | Ghada N. AlEssa Saleh I. Alzahrani A novel non-invasive EEG-SSVEP diagnostic tool for color vision deficiency in individuals with locked-in syndrome Frontiers in Bioengineering and Biotechnology color vision deficiency diagnosing EEG SSVEP signal processing feature extraction |
| title | A novel non-invasive EEG-SSVEP diagnostic tool for color vision deficiency in individuals with locked-in syndrome |
| title_full | A novel non-invasive EEG-SSVEP diagnostic tool for color vision deficiency in individuals with locked-in syndrome |
| title_fullStr | A novel non-invasive EEG-SSVEP diagnostic tool for color vision deficiency in individuals with locked-in syndrome |
| title_full_unstemmed | A novel non-invasive EEG-SSVEP diagnostic tool for color vision deficiency in individuals with locked-in syndrome |
| title_short | A novel non-invasive EEG-SSVEP diagnostic tool for color vision deficiency in individuals with locked-in syndrome |
| title_sort | novel non invasive eeg ssvep diagnostic tool for color vision deficiency in individuals with locked in syndrome |
| topic | color vision deficiency diagnosing EEG SSVEP signal processing feature extraction |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2024.1498401/full |
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