DataVXApplied SciencesPaper
journal article·2023-02-25Open access

Feasibility of Magneto-Encephalography Scan under Color-Tailored Illumination

Charitha Weerasuriya, Soon Hock Ng, William Woods, Tom Johnstone, Pranciškus Vitta, Laila Hugrass, Saulius Juodkazis

Applied Sciences· Volume 13· Issue 5
Publisher pageOpen full text
Source abstract

Abstract

Color plays an important part in human activities, and it also affects circadian cycle and the decision making process. Therefore, it is important to investigate human judgment under different color of illumination, especially because color perception is subjective. In this study, we developed required instrumentation to control the red (R), green (G), blue (B), and amber (A) colored light emitting diode (LED) lamp for carrying out magneto-encephalography (MEG) brain scans. We developed a software to generate all the colors in the visual spectrum, predefined white light combinations and saturation of an illuminated objects by using RGBA color pallet. The lamp is required to control from outside of the MEG electromagnetically shielded room with the LED lamp located inside the MEG room. Hence, USB is used for the communication mode. The feasibility of using LED lamp with MEG brain scanner has been validated for bio-medical and psychological MEG experiments.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Color perception and design · Multisensory perception and integration · Visual perception and processing mechanisms

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Optical Sciences Centre and ARC Training Centre in Surface Engineering for Advanced Materials (SEAM), School of Science, Swinburne University of Technology, Hawthorn, VIC 3122, Australia

Melbourne Centre for Nanofabrication, 151 Wellington Road, Clayton, VIC 3168, Australia

School of Health Sciences, Department of Psychological Sciences, Swinburne University of Technology, Hawthorn, VIC 3122, Australia

Institute of Photonics and Nanotechnology, Faculty of Physics, Vilnius University, Saulėtekio al. 3, LT-10257 Vilnius, Lithuania

School of Psychological Science, La Trobe University, Melbourne, VIC 3086, Australia

WRH Program International Research Frontiers Initiative (IRFI), Tokyo Institute of Technology, Nagatsuta-cho, Midori-ku, Yokohama 226-8503, Kanagawa, Japan

Funding

ARC Linkage · Horticulture Innovation Australia

References (24)

Show source-provided references
1. An ergonomic study of visual optimization by light color managements10.1002/col.21935 ↗
2. Correlations between color attributes and children’s color preferences10.1002/col.21801 ↗
3. Color Psychology: Effects of Perceiving Color on Psychological Functioning in Humans10.1146/annurev-psych-010213-115035 ↗
4. Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform10.1186/s43593-022-00019-x ↗
5. Remotely mind-controlled metasurface via brainwaves10.1186/s43593-022-00016-0 ↗
6. Color rendition engine10.1364/OE.20.005356 ↗
7. A theory of colors in combination—A descriptive model related to the NCS color-order system10.1002/1520-6378(200102)26:1<4::AID-COL3>3.0.CO;2-T ↗
8. Ferguson, I.T., Narendran, N., DenBaars, S.P., and Park, Y.S. (2002). Solid State Lighting, Spie-Int Soc Optical Engineering.
9. Colour-rendition properties of solid-state lamps10.1088/0022-3727/43/35/354006 ↗
10. Magnetoencephalography10.1007/s00247-009-1451-y ↗
11. Steven, K.S. (2003). The Science of Color, Elsevier Science. [2nd ed.].
12. Bubak, M., van Albada, G., Sloot, P., and Dongarra, J. (2004, January 6–9). A comparison of C, Matlab and Python as teaching languages in engineering. Proceedings of the Computational Science—ICCS 2004: 4th International Conference, Kraków, Poland.10.1007/b97987 ↗
13. Kness, M. (2022, December 01). ColorPy—A Python Package for Handling Physical Descriptions of Color and Light Spectra. Available online: http://markkness.net/colorpy/ColorPy.html.
14. Brain noise estimation from MEG response to flickering visual stimulation10.1016/j.csfx.2019.100005 ↗
15. Comparison of the EMI Performance of LED PWM Dimming Techniques for LED Video Display Application10.1109/JDT.2011.2175362 ↗
16. MEG-based decoding of the spatiotemporal dynamics of visual category perception10.1016/j.neuroimage.2013.07.075 ↗
17. Pearce, B., Crichton, S., Mackiewicz, M., Finlayson, G., and Hurlbert, A. (2014). Chromatic Illumination Discrimination Ability Reveals that Human Colour Constancy Is Optimised for Blue Daylight Illuminations. PLoS ONE, 9.10.1371/journal.pone.0087989 ↗
18. Decoding visual consciousness from human brain signals10.1016/j.tics.2009.02.004 ↗
19. Viola, I., Kanitsar, A., and Groller, M. (2004, January 10–15). Importance-driven volume rendering. Proceedings of the IEEE Visualization 2004, Austin, TX, USA.
20. Decrease in Nitrate Concentration in Leafy Vegetables under a Solid-state Illuminator10.21273/HORTSCI.44.7.1857 ↗
21. “Light-box” accelerated growth of poinsettias: LED-only illumination
22. Wright, W.D. (1964). The Measurement of Colour, Hilger & Watts. [3rd ed.].
23. Wyszecki, G. (1967). Color Science: Concepts and Methods, Quantitative Data and Formulas, Wiley. [1st ed.].
24. How the CIE 1931 Color-Matching Functions Were Derived from Wright–Guild Data10.1002/(SICI)1520-6378(199702)22:1<11::AID-COL4>3.0.CO;2-7 ↗