They trained a deep neural network to reconstruct what a patient was seeing from fMRI signals and were mildly successful. Cool!
Things get really awesome in Figure 4. They had patients imagine an image/symbol that that they would then try to reconstruct from fMRI signals. They had a few successes and were able to operate at better than chance when humans evaluated the reconstructed shapes. That's awesome!
Cool results. Just want to point out that there's a time lag between "thoughts" and the appearance of a signal through fMRI, which explains why the image needed to be imagined for a long period of time (8 seconds).
Most thoughts are more fleeting than this, and couldn't be read with fMRI. So there's no need to get /too/ concerned about the privacy of our thoughts. Not yet :)
> Each imagery block consisted of a 4-s cue period, an 8-s imagery period, a 3-s evaluation period and a 1-s rest period.
I don't know how your brain works, but for most people just hearing/reading the words "pink elephant" will cause their System 1 to immediately bring that concept into their consciousness.
This isn't "simple minded" it's just how brains work, and the alternative of having to actively think about a word to remember what it means, would be much slower.
I wonder what would happen if you could feed back the output into the visual system using VR in near real time. Would you be able “see” anything you could think of?
You give the brain a set of images to fit/train the original model.
Then you show the brain a set of images similar to the ones used to train the model.
... you will never really understand what the brain sees in novel situations unrelated to the training task.
Often, first author is considered the primary contributor, while last author might be considered a principal or adviser. By adding a disclaimer and listing authors alphabetically, you can try and dispel that misconception if this isn't the case for your paper.
Typically most credit goes to first and last author. However, there are cases where one person needs to be first author, but multiple people are equally deserving.
This strongly depends on what field you are in and can vary even between minor sub-disciplines. Also that statement is frequently meaningless and authors get added for politics target than contribution.
Theoretically, you could do the same thing with EEG via source localization techniques[0] on the EM spectrum.
In my experience, barely any labs have the know how to implement them in a performative manor to pull something like this off whereas a lot of the fmri software is made from the manufacture (GE, Siemens, etc) that transforms the raw data into the voxels that were used in this setup.
Depends on which brain regions you want to acquire signals from (usually based on the literature), but generally yes, you need to sample from a "sufficient" number of places (one of my fmr colleges has a lead reduction paper coming out this year that will talk about this).
But keep in mind here, using eeg (and beamforming) is still way cheaper and accessible than any fmri machine, though as far as having the skills to implement such, I think it is out of the reach of most labs who do this sort of research.
Electrodes are even getting better[0] wrt cost and actual ease of real world usage (dry/non gel needed/comfort).
Things get really awesome in Figure 4. They had patients imagine an image/symbol that that they would then try to reconstruct from fMRI signals. They had a few successes and were able to operate at better than chance when humans evaluated the reconstructed shapes. That's awesome!