# EEG-Based Methods for Diagnosing Color Vision Deficiency: A Comprehensive Review
## 3 EEG as a Diagnostic Tool  
The EEG, an electrophysiological technique for recording the brain's electrical activity, was pioneered in 1924 [46]. The primary source of the EEG is the cortical pyramidal neurons in the cerebral cortex that are aligned with the brain's surface [47]. It is believed that the neural activity observed through the EEG results from the combined impact of both excitatory and inhibitory postsynaptic potentials generated by groups of neurons firing in a synchronized manner [48].  
It has been proven that EEG is a significant diagnostic method for non-invasive studies on neurological disorders [49; 50]. It is an efficient method for identifying neurological disorder biomarkers, leading to valuable insights into the disorder. For example, the slowing of EEG rhythms can indicate an abnormality in brain function, suggesting an abnormal condition in the individual [51].  
To use EEG as a diagnostic tool, it is crucial to analyze the signals and relate the changes to the specific condition [52]. However, there are some limitations associated with using EEG as a diagnostic tool. One limitation is that EEG measures the brain's activity on the scalp and not directly from the brain, which in turn affects the spatial resolution [53; 54]. This makes it challenging to identify a specific brain region involved in a specific disorder.  
Figure 1: Photoreceptor cells in the human retina. Note that the rod cell is responsible for light sensitivity, enabling better vision in low-light conditions [42; 43; 44].  
Figure 2: A representation of visible light colors with their corresponding wavelengths, overlaid with the normalized responsivities of the three distinct cone cells in the human eye’s retina [45]. The figure illustrates the sensitivity of S-cones to short wavelengths, depicted in blue; M-cones to middle wavelengths, depicted in green; and L-cones to long wavelengths, depicted in red, highlighting the overlapping ranges of these cone cells in color perception.  
Also, EEG data involve complex analysis, making it important for the data to be analyzed by an expert [55]. Other limitations include factors affecting the EEG recording, such as eye movements and muscle activity, which introduce noise into the signal and affect the accuracy [56; 57; 58].  
Since CVD contributes to altering the EEG signals, these changes could be used to detect biomarkers to diagnose CVD. Studies have shown that EEG is highly affected by color perception, indicating that it could be used to detect CVD [59]. For example, in [60], it was found that the oscillation of EEG signals has varying latencies when exposed to different colors. Specifically, the oscillation of green occurs earlier than that of red or blue. Understanding this could suggest that when a person with CVD perceives the color green, no oscillation might be expected, as seen in individuals with normal vision.  
The neurological pathway of color vision is depicted in Figure 3, where the eye absorbs the wavelength of a specific color (e.g., in this figure, green). The specific photoreceptors send this information to the optic nerve, which carries it to the visual cortex in the brain for further processing and interpretation of the color. This process causes specific alterations in EEG signals, enabling the investigation of color perception using EEG signals and BCI techniques.