# EEG-Based Methods for Diagnosing Color Vision Deficiency: A Comprehensive Review
## 5 EEG-Based Approaches for Diagnosing CVD
### SSVEP Approaches for Diagnosing CVD
Their results indicate that CVD assessment based on SSVEPs could be improved by optimizing the stimulation frequency. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Reference** & **Year** & **Method/Analysis** & **Channels** & **Extracted Features** & **Findings** \\ \hline \multirow{3}{*}{[71]} & \multirow{3}{*}{-} & Silent substitution, & & & Differences in EEG were observed between color-deficient and healthy infants. \\  & & vector voltmeter & & Oz, O1, O2 & Amplitude & \\  & & & & & \\ \hline \multirow{3}{*}{[77]} & \multirow{3}{*}{2016} & Color stimulus, principal component analysis (PCA) & Cz, O1, Oz, O2 & & Higher EEG energy for correct color choices compared with incorrect choices. \\  & & & (10–20 international system) & Energy & \\ \hline \multirow{3}{*}{[78]} & \multirow{3}{*}{2021} & Color stimulus, multifractal detrended fluctuation (MFDFA) and multi-fractal detrended cross-correlation analysis (MFDXA) & F3, F4, F7, F8, F2, P3, P4, O1, O2 & Spectral width & The complexity of the EEG varies across different perceived colors. \\ \hline \multirow{3}{*}{[79]} & \multirow{3}{*}{2021} & Color stimulus, four transform, Quadratic discriminant analysis, SVM, and KNN classifiers & F3, F4, F2, P3, P2, P4, O1, O2, T7, T8 & \multirow{3}{*}{PSD} & Different electrodes showed a significant impact of different colors. \\ \cline{1-1} \cline{3-3}  & & Color stimulus, interval-Type II fuzzy classifier & F3, F4, F2, P3, Pz, P4, O1, O2, T7, T8 & & \\ \hline \multirow{3}{*}{[76]} & \multirow{3}{*}{2017} & Color stimulus, Python, BeagleBoard & Cz, O1, Oz, O2 & Energy, entropy & \multirow{3}{*}{Energy, entropy compared to healthy people.} \\  & & & & & \\ \hline \multirow{3}{*}{[81]} & \multirow{3}{*}{2020} & Ishihara plates stimulus, GP, Brain Topography and ERP Analysis & F1, F2, P3, F2, F4, C3, Cz, C4, P7, P3, Pz, P4, O1, O2, O2 (10–20 international system) & Variations in latency and amplitudes between healthy and color-deficient people. \\ \cline{1-1} \cline{3-3}  & & Ishihara plates stimulus, CP, Brain Topography and ERP Analysis & F1, F2, P3, P2, P4, O1, O2 (10–20 international system) & Amplitude, latency & Variations in latency and amplitudes between healthy and color-deficient people. \\ \hline \end{tabular}
\end{table} 
Table 3: Summary of existing studies utilizing EEG signals for diagnosing CVD. Generally, based on the studies reviewed earlier, no study had a sufficient sample size, as most did not exceed ten subjects, even though a substantial amount of EEG data is preferred for efficient training of machine learning models. This highlights that the sample sizes in most previous studies were not adequate to draw definitive conclusions. Additionally, potential biases in some previous studies could impact certain conclusions, such as studies that only included male subjects or did not test the method on individuals with CVD. Furthermore, many studies had limited variability in the target age groups, raising questions about whether their methods would yield consistent results across different age ranges. In summary, the field of using EEG signals to diagnose CVD is relatively new, with many areas of ambiguity that need further study and discussion, including limitations related to sample sizes, potential biases, and methodological constraints.