# EEG-Based Methods for Diagnosing Color Vision Deficiency: A Comprehensive Review
## 9 Conclusions  
In conclusion, the available color vision assessments exhibit significant limitations, necessitating the exploration of alternative methods. Recent studies have demonstrated the potential of EEG signals in diagnosing CVD by observing their response to different colors. This finding holds significant implications for utilizing EEG signals and employing machine learning algorithms to classify and diagnose CVD non-invasively. The non-invasive use of EEG in the diagnosis of CVD opens up new avenues for research and clinical implementation. By leveraging the distinctive changes in SSVEP responses to different colors, EEG-based approaches offer promising prospects in accurately identifying and classifying color vision deficiencies. Nonetheless, it is crucial to acknowledge that there are challenges in this field that need to be addressed in future research. These challenges include refining methodologies and protocols for capturing and analyzing EEG signals, enhancing the accuracy and reliability of diagnostic algorithms, and overcoming the limitations associated with user interfaces and usability. More research is also needed to develop standard operating protocols, confirm the reliability of EEG-based CVD diagnosis in various populations, and investigate possible clinical applications. Future research efforts should concentrate on overcoming the identified problems and knowledge gaps in light of these findings and their implications. The development of EEG-based CVD diagnosis depends critically on cooperation between researchers, physicians, biomedical engineers  
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