# EEG-Based Methods for Diagnosing Color Vision Deficiency: A Comprehensive Review
## 5 EEG-Based Approaches for Diagnosing CVD
### Pros and Cons of Both Techniques  
Each technique employed in diagnosing CVD using EEG signals, including ERP and SSVEP, has distinct advantages and limitations. The pros and cons of both ERP and SSVEP techniques are summarized in Table 4. Understanding these factors is crucial in selecting the most appropriate approach to achieve the desired goals. The primary disparity between the two techniques lies in their operational principles: ERPs are considered responses to individual stimuli, whereas SSVEPs are viewed as cumulative responses to flickering stimulation over an entire period of stimulation [64].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Year** & **Method/Analysis** & **Channels** & **Extracted Features** & **Findings** \\ \hline \multirow{8}{*}{[82]} & \multirow{8}{*}{2021} & \multirow{8}{*}{Ishihara plates stimulus, FDR correction, KNN classifier} & Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6, Fz, Cz, Pz (10–20 international system)} & \multirow{8}{*}{Frequency} & People with CVD have different values for the frequency bands compared to individuals with normal color vision, and the best results showing differences were observed for the electrodes P4, T6, and O2. \\ \hline \multirow{8}{*}{[85]} & \multirow{2}{*}{2021} & \multirow{2}{*}{\begin{tabular}{c} Sweep SSVEP, CCA, color vision severity index (ICVD) \\ \end{tabular} } & PO3, PO4, POz, O1, O2, and O2 (10–20 international system) & Amplitude, frequency & The type and severity of CVD can be determined by ICVD. \\ \hline \multirow{8}{*}{[87]} & \multirow{8}{*}{2021} & \multirow{8}{*}{\begin{tabular}{c} SSVEP, Metamer \\ Identification Algorithm \\ (metalD), CCA \\ \end{tabular} } & F3, Fz, F4, T7, C3, CZ, C4, T8, CP3, CP4, P3, Pz, P4, PO7, PO8, Oz (10–10 international system)} & Frequency & People with normal color vision have an SSVEP that is close to zero, while people with CVD do not. \\ \hline \multirow{8}{*}{[88]} & \multirow{2}{*}{2022} & \multirow{2}{*}{\
\begin{tabular}{c} SSVEP, Gaussian process \\ regression \\ (metalD+) \\ \end{tabular} } & O1, Oz, O2, PO7, PO3, PO2, PO2, PO4, PO8, P5, P3, P1, Pz, P2, P4, P6, CPz & Frequency & This method can improve the measurement and reduce noise compared to the metalD method. \\ \hline \multirow{8}{*}{[89]} & \multirow{2}{*}{2023} & \multirow{2}{*}{
\begin{tabular}{c} SSVEP, CCA \\ \end{tabular} } & O1, Oz, O2, PO7, PO3, PO2, PO4, PO8, P5, P3, P1, Pz, P2, P4, P6, CPz & Amplitude & CVD assessment efficiency depends on the visual stimulation frequency. \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._Regarding the pros and cons of each technique, ERPs excel in measuring scalp EEG, with reported accuracies ranging from 85% to 95% [90]. Additionally, subjects may find the recording process more comfortable due to the absence of flickering stimulation, which can sometimes be bothersome during EEG recordings.  
Conversely, SSVEPs offer reliable performance in measuring scalp EEG, with reported accuracies between 80% and 95% [90], slightly inferior to ERP techniques. Moreover, SSVEPs typically exhibit a higher SNR compared to ERPs. This superiority in SNR can be attributed to SSVEPs being less susceptible to ocular artifacts and other noise, such as electromyographic interference. Other advantages of SSVEP include rapid recording capabilities, precise quantification of specific responses, and unbiased identification [64]. Despite its advantages in SNR and noise resilience, SSVEP may induce discomfort in some subjects due to the flickering stimulation used. Furthermore, the flickering stimulation may impose limitations on certain individuals, such as those with epilepsy, who should avoid exposure to such stimuli during recordings [83].