# EEG-Based Methods for Diagnosing Color Vision Deficiency: A Comprehensive Review
## 6 Data Analysis and Interpretation  
Previous studies have revealed that EEG features undergo changes based on different colors, such as alterations in frequency band values or signal complexity. These changes can serve as significant biomarkers for detecting CVD. The main steps utilized to classify EEG signals for CVD diagnosis comprise data collection, data pre-processing, feature extraction, feature selection, and classification, as shown in Figure 4[91]. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**ERP Technique**} & \multicolumn{2}{c}{**SSVEP Technique**} \\ \hline Higher accuracy in measuring scalp EEG allows for & Greater SNR and less susceptibility to external \\ more precise measurements of brain activity. & environmental factors. \\ \hline Comfortable stimulation process from & Rapid recording capabilities enable efficient data collection \\ the subjects’ point of view. & and analysis for tasks requiring quick responses. \\ \hline Pros & No limitations for individuals who can & Precise quantification of specific responses, as it enables \\ perform the recording. & accurate measurement of responses to visual stimuli at \\  & perform the recording. & particular frequencies, aiding in identifying and analyzing \\  & Numerous studies have utilized it, making its & brain activity patterns with high specificity. \\  & methods and analysis techniques clearer & Unbiased identification, as it relies on neural responses to \\  & to other researchers. & visual stimuli without subjective interpretation. \\ \hline Cons & Easily affected by noise, eye movements, and body & Some subjects find it annoying due to the \\  & movements, which can impact the signal’s reliability. & flickering stimulation. \\ \cline{2-3}  & May involve subjective judgment in identifying & Limited usability for individuals with certain neurological \\ neural responses, potentially introducing biases or & disorders, such as epilepsy, who should avoid \\  & variations in the analysis process. & flickering stimuli. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pros and cons of ERP and SSVEP techniques. Figure 4: Methodology for detecting color vision deficiency using EEG signals. The first step, data collection, involves recording the EEG data of multiple subjects under different circumstances. The second step, data pre-processing, involves isolating the noise that usually accompanies EEG data, such as eye movements. The third step, feature extraction, involves extracting specific features, such as the frequency domain, from the EEG signal. The fourth step, feature selection, involves selecting relevant features to minimize dimensionality. The final step, classification, involves training on the chosen features to classify the EEG signals into the normal and CVD classes. Although the details of the steps may vary and are not always unified in published studies, the typical approach includes spatial and spectral filtering of the data. Besides using filters such as the band-pass filter (BPF), low-pass filter (LPF), and high-pass filter (HPF) to eliminate environmental noise, and the notch filter to eliminate power line interference, other techniques have been employed to remove artifacts, such as eye blinking. These techniques include signal space projection (SSP), used in [81], and principal component analysis (PCA), used in [77; 81]. The filtering parameters vary in every study, as illustrated in Table 5. Note that the filtering parameters all depend on the procedures used in the study. Various analysis techniques are used in diagnosing CVD using EEG signals. These techniques include CCA and PCA, which have been widely utilized in previous research. The CCA technique has been extensively employed in the examination of SSVEPs in previous studies [85; 86; 87; 88; 89]. It is commonly used to describe the correlations among SSVEP signals from multiple channels and to measure the size of SSVEP in each trial, enabling the extraction of its features [92].