# EEG-Based Methods for Diagnosing Color Vision Deficiency: A Comprehensive Review
## 5 EEG-Based Approaches for Diagnosing CVD
### ERP Approaches for Diagnosing CVD  
Despite SSVEP being considered a viable option for diagnosing CVD by various studies, other approaches, such as ERP, have also been employed in this field. However, ERP has rarely been utilized for diagnostic purposes in this domain; rather, it typically explores the relationship between brain activity and color perception. An example of a study conducted for diagnostic purposes is the study by [PERSON] et al. [71], where they used silent substitution to measure visually evoked potentials (VEPs) in infants to detect CVD. ERP, which includes VEPs, is defined as a transient response to specific stimuli [72]. Silent substitution is a technique that stimulates a specific cone cell in the retina by using metamers, which are pairs of lights, while minimizing the response from other types of cones. It depends on adjusting the intensities of different wavelengths of light to stimulate the desired cone type [73]. They analyzed the effects of the stimuli on the EEG signal using a vector voltmeter, where the results showed response differences between healthy and color-deficient infants. This was the only study that utilized silent substitution for diagnosis; however, only infants participated as subjects, with adults included for comparison purposes. The studies then demonstrated that human retina photoreceptors continue to change with age [74; 75], indicating that adult retinas cannot be directly compared with those of infants. Moreover, [PERSON] et al. [76] developed a portable embedded device using Python and BeagleBoard to analyze the effects of CVD on EEG signals. Like the previously mentioned studies, they used color stimuli with the three basic colors (red, green, and blue) while recording the EEG. They then calculated the deviation in the energy, observing more deviation in the particular color associated with CVD in the person. Their results are based on only two subjects, suggesting that further testing may be necessary to ensure more reliable results. Regarding the relationship between color perception and brain activity by analyzing EEG signals while using color stimuli, [PERSON] and [PERSON] [77], along with other studies [78; 79], investigated this by different methods. In [77], the subjects were asked to identify the matching colors of the test color from a set of shades of the basic colors (red, green, and blue) while their EEG signals were being recorded. They then calculated the energy of each EEG signal. In [78], the subjects were shown a few colors of the VIBGYOR spectrum, with a neutral grey background separating each color, and their EEG signals were recorded from several locations in the brain. The researchers then analyzed the spectral width of each EEG signal. Meanwhile, in [79], the subjects were shown a stimulus set consisting of different shapes with different colors (red, green, and blue) on the computer screen, and their EEGs were recorded. Then, the conventional band power values were obtained. Although these methods of stimulation and analysis have demonstrated differences in EEG responses to different colors, none of the three studies conducted the experiment on subjects with CVD, leaving it uncertain whether the results could be generalized to both individuals with normal vision and those with CVD. Additionally, in [77], only three subjects were involved in the study, which is considered insufficient to draw a conclusion. Another issue is that the age range in [77; 79] is too restricted, potentially introducing biases in the results. Another study [80] estimated the power spectral density (PSD) of EEG signals recorded while presenting stimuli of different colors to the subjects, including red, green, yellow, and blue. The study found that the PSD differed for each color, with the classification rate being highest for red and lowest for yellow. This finding could have a significant impact on diagnosing CVD using ERP.