Most effective use of colour in heat/contour maps It's quite common to use heat/contour maps when presenting time-frequency EEG findings. The colour scheme often chosen (and one that I like and use) is the "jet" colour scheme (see e.g., google image search time-frequency EEG). I'm wondering if there are any better colour schemes for presenting these plots, and/or guidelines for the presentation of such maps.  
e.g., from R base library
#Volcano
x <- 10*(1:nrow(volcano))
y <- 10*(1:ncol(volcano))
image(x, y, volcano, col = terrain.colors(100), axes = FALSE)

# With Jet colours
jet.colors <-  colorRampPalette(c("midnightblue","blue", "cyan","green1", "yellow","orange","red", "darkred"), space="Lab")
image(x, y, volcano, col = jet.colors(100), axes = FALSE)

 A: Rainbow color maps, as they're often called, remain popular despite documented perceptual inefficiencies. The main problems with rainbow (and other spectral) color maps are:


*

*The colors are not in a perceptual order

*The luminance bounces around: our eyes are mostly rods for luminance, not cones for color

*We see hues categorically

*Hues often have unequal presences (e.g., wide green and narrow yellow)


On the plus side:


*

*Spectral themes have high resolution (more distinguishable color values in the scale)

*There's safety in numbers; such themes are still quite common


See Rainbow Color Map (Still) Considered Harmful for discussion and alternatives, including black-body radiation and grayscale.
If a diverging scheme is suitable, I like the perceptually uniform cool-to-warm scheme derived by Kenneth Moreland in his paper, Diverging Color Maps for Scientiﬁc Visualization. It and other schemes are compared with images in the ParaView wiki, though with a perspective of coloring a 3-D surface, which means the color scheme has to survive shading effects.
Recent blog post with more links and Matlab alternatives: Rainbow Colormaps – What are they good for? Absolutely nothing!
Recommendation: First try grayscale or another monochromatic gradient. If you need more resolution, try black-body radiation. If the extremes are more important than the middle values, try a diverging scheme with gray in the middle, such as the cool-to-warm scheme.
Images from the ParaView wiki page:
Rainbow: 
Grayscale: 
Black-body: 
Cool-to-warm: 
A: I agree with @xan about the inefficiencies of rainbow color maps. Here is another paper that shows that rainbow/categorical color maps are substantially worse than diverging ones for quantitative tasks, from InfoVis '11:


*

*Michelle Borkin, Krzysztof Gajos, Amanda Peters, Dimitrios Mitsouras, Simone Melchionna, Frank Rybicki, Charles Feldman, and Hanspeter Pfister. 2011. Evaluation of Artery Visualizations for Heart Disease Diagnosis. IEEE Transactions on Visualization and Computer Graphics 17, 12 (December 2011), 2479-2488. DOI=10.1109/TVCG.2011.192 Link to PDF, Slides, and Images.

The only thing rainbow/categorical color maps are good for is to show separate values of categorical variables. However, the colors you choose matter. If you need a categorical scale, check out this excellent paper from CHI '12 that uses the XKCD survey dataset that talks about how we perceive differences in color. It allows you to rate a color scale by how well humans perceive the differences. Their web-based Color Palette Analyzer will let you evaluate your own color scale, too!


*

*Jeffrey Heer and Maureen Stone. 2012. Color naming models for color selection, image editing and palette design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '12). ACM, New York, NY, USA, 1007-1016. DOI=10.1145/2207676.2208547 Link to PDF, online demos, etc.

