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ORIGINAL RESEARCH article

Front. Bioeng. Biotechnol.
Sec. Biosensors and Biomolecular Electronics
Volume 12 - 2024 | doi: 10.3389/fbioe.2024.1498401

A Novel Non-Invasive EEG-SSVEP Diagnostic Tool for Color Vision Deficiency in Individuals with Locked-in Syndrome

Provisionally accepted
Ghada E AlEssa Ghada E AlEssa Saleh Ibrahim Alzahrani Saleh Ibrahim Alzahrani *
  • Department of Biomedical Engineering, College of Engineering, Imam Abdulrahman Bin Faisal University, Damam, Saudi Arabia

The final, formatted version of the article will be published soon.

    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.

    Keywords: color vision deficiency, Diagnosing, EEG, SSVEP, Signal processing, feature extraction, Classification

    Received: 18 Sep 2024; Accepted: 20 Dec 2024.

    Copyright: © 2024 AlEssa and Alzahrani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Saleh Ibrahim Alzahrani, Department of Biomedical Engineering, College of Engineering, Imam Abdulrahman Bin Faisal University, Damam, 31451, Saudi Arabia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.