# EEG-Based Methods for Diagnosing Color Vision Deficiency: A Comprehensive Review
## 5 EEG-Based Approaches for Diagnosing CVD
### SSVEP Approaches for Diagnosing CVD  
In the field of using SSVEP to diagnose CVD, as mentioned earlier, there were no published studies regarding this before 2020, although it has been known for many years that SSVEP is affected by different colors. [PERSON] et al. studied the effects of some colors, including red, green, blue, white, and gray, on SSVEPs at three different frequencies (5, 12, 30 Hz) [83]. They investigated the frequency amplitudes using canonical correlation analysis (CCA). They concluded that each color generates a different amplitude and phase. The white and red colors produce the largest amplitude. They also found that the best SNR is achieved by using mid-range frequencies, such as 12 Hz. Their study was not intended for diagnostic purposes; however, their findings could be extremely helpful in designing methodologies for diagnosing CVD. Another study also concluded that the red color has the most accurate discrimination among other colors; this finding could be very useful in diagnosing CVD [84]. However, the studies stated that using white is preferable to using red to mitigate the risk associated with the red color, such as inducing epileptic seizures [83]. Also, it was concluded in [70] that colors with high wavelengths, such as red, generate higher SSVEP amplitudes compared to colors with lower wavelengths, such as blue. Generally, many researchers have studied color effects on SSVEPs but have rarely used this finding to diagnose CVD. For the studies that have employed SSVEP for diagnostic purposes, [PERSON] et al. used a method that relies on sweep SSVEP to diagnose CVD [85]. They utilized a stimulus paradigm consisting of a red-green reversal checkerboard with different luminance ratios to elicit the SSVEPs. They then analyzed the signals using CCA and established a method to determine the type and severity of CVD using an equiluminance turning curve. They considered the equiluminance point to occur when the two colors appear to have the same luminance. Since their study used sweep SSVEP, which covers a broad frequency range for stimulation, this added some complexity and time-consuming aspects to their method as they needed to sweep through multiple frequencies [86]. Overall, the broader frequency range may introduce more variability and potential confounding effects in the neural responses. Nonetheless, [PERSON] et al. investigated CVD detection using SSVEP, rather than sweep SSVEP, based on a novel method for identifying metamers called \"metalD\" [87]. This method involves adjusting two light sources that have the same color appearance but differ in spectral distributions to be the same. They hypothesized that the metamer is identified by minimizing the SSVEP caused by the two flickering light sources and found that people with normal color vision have an SSVEP that is close to zero, while people with CVD do not. As they mentioned, EEG measurements are often noisy, and the excessive use of filters can potentially impact the data, leading to decreased accuracy. Thus, [PERSON] et al. [88] tried to improve on what was done in [87] by using Gaussian process regression to reduce the noise of the measurements, enhancing the performance of the method. Nevertheless, EEG measurements are easily affected by external factors, leaving room for continuous improvement in measurement accuracy and noise reduction. In SSVEP approaches, the stimulation frequency varies in each study. For example, in [83], the stimulation frequencies were 5, 12, and 30 Hz, while in [85], the stimulation frequency was 15 Hz, and in [87], the stimulation frequency was 10 Hz. However, the effects of the stimulation frequency were investigated by [PERSON] et al. [89]. They considered the frequency range of 2-38 Hz and observed that hue stimulation led to eliciting larger SSVEPs at lower frequencies, while illuminance stimulation led to eliciting smaller SSVEPs at lower frequencies. They also found that the best frequency for CVD assessment is 16 Hz.