Abstract
This study quantifies the amplitude and latency variability of the visual pattern evoked potential (P100) using the MUSE device and compares this variability between two populations: color blind versus normal eyes.
The main objective of this work is to identify the impact of color on brain waves in color-blind and non-color-blind individuals. Thus, P100 wave was used and the amplitude and latency generated by each visual stimulus was analyzed. The results show that different colors present different amplitudes and latencies in the two samples and between colors. The Wilcoxon test was used to compare the samples. In both samples, blue and red color with 100% saturation present higher amplitudes. Blue amplitudes value is statistically different considering the two samples. The blue amplitude is higher in color-blind individuals, meaning that it causes greater stimulation in brain activity. On the other hand, although the amplitude of the red color is high, it is similar in both samples. Yellow, Blue, Yellow and Saturated Green present discrepant values of P100 wave amplitude in both samples. Amplitudes are much higher in non-color-blind individuals than in color-blind individuals. According to this study, different colors can cause different amplitudes and latency in P100 waves, considering color blind and non-color-blind people. In addition, our findings also confirm previous studies that indicate that energy in theta and alpha bands can be used to discriminate some colors. In summary, this study is useful to find metrics to classify colors helping in marketing strategies and design typography. The P100 wave used in this kind of strategies is a novelty in the literature and can be considered for future studies.
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References
Rakshit, A., Lahiri, R.: Discriminating different color from EEG signals using interval-type 2 fuzzy space classifier (a neuro-marketing study on the effect of color to cognitive state). In: 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2016, February 201. https://doi.org/10.1109/ICPEICES.2016.7853388
Clarke, T., Costall, A.: The emotional connotations of color: a qualitative investigation. Color Res. Appl. 33(5), 406–410 (2008). https://doi.org/10.1002/COL.20435
Chai, M.T., et al.: Exploring EEG effective connectivity network in estimating influence of color on emotion and memory. Front. Neuroinform. 13, 66 (2019). https://doi.org/10.3389/FNINF.2019.00066/BIBTEX
Haider Ali, S.R.K., Khan, Y., Shah, M.: Color psychology in marketing. J. Bus. Tour.4(1), 183–190 (2021). https://doi.org/10.34260/JBT.V4I1.99
Yoto, A., Katsuura, T., Iwanaga, K., Shimomura, Y.: Effects of object color stimuli on human brain activities in perception and attention referred to EEG alpha band response. J. Physiol. Anthropol. 26(3), 373–379 (2007). https://doi.org/10.2114/JPA2.26.373
Wilms, L., Oberfeld, D.: Color and emotion: effects of hue, saturation, and brightness. Psychol. Res. 82(5), 896–914 (2017). https://doi.org/10.1007/s00426-017-0880-8
Ko, Y.H., Shen, I.H., Lee, D.S.: Color combinations of visual display terminal (VDT) icon on user preferences and EEG response. Percept. Mot .Skills 110(2), 411–428 (2010). https://doi.org/10.2466/PMS.110.2.411-428
Wang, Y., Wang, S., Xu, M.: The function of color and structure based on EEG features in landscape recognition. Int. J. Environ. Res. Public Health 18(9), 4866 (2021). https://doi.org/10.3390/IJERPH18094866
Che, X., Zheng, Y., Chen, X., Song, S., Li, S.: Decoding color visual working memory from EEG signals using graph convolutional neural networks. Int. J. Neural Syst. 32(2), 2250003 (2022). https://doi.org/10.1142/S0129065722500034.
Al-Nafjan, A.: Feature selection of EEG signals in neuromarketing. PeerJ Comput. Sci. 8, e944 (2022). https://doi.org/10.7717/PEERJ-CS.944
Krigolson, O.E., Williams, C.C., Norton, A., Hassall, C.D., Colino, F.L.: Choosing MUSE: validation of a low-cost, portable EEG system for ERP research. Front. Neurosci. 11(Mar), 109 (2017). https://doi.org/10.3389/FNINS.2017.00109/BIBTEX
D’Andrade, R.G., Romney, A.K.: A quantitative model for transforming reflectance spectra into the Munsell color space using cone sensitivity functions and opponent process weights. Proc. Natl. Acad. Sci. U S A 100(10), 6281–6286 (2003). https://doi.org/10.1073/PNAS.1031827100/ASSET/19338334-3275-4800-A5F5-BC9D084FB470/ASSETS/GRAPHIC/PQ1031827006.JPEG
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Teixeira, A.R., Gomes, A. (2023). Analysis of Visual Patterns Through the EEG Signal: Color Study. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Augmented Cognition. HCII 2023. Lecture Notes in Computer Science(), vol 14019. Springer, Cham. https://doi.org/10.1007/978-3-031-35017-7_4
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