Yep, only EEG gives you that kind of information. fMRI takes a lot of post-processing with complex statistics to produce any kind of useful information. I worked on a study that used fMRI to study the involvement of early visual areas in the cortex during reading tasks, and when processing long runs we'd get on with other stuff while the data was being processed.

There isn't any existing tech to take surface data and "map" it to internal states because there isn't a simple relationship between on and the other. For real-time recording of activity deeper in the brain than a few centimetres you need to implant electrodes during surgery, which is obviously only something that happens in humans when that person needs brain surgery already.

There's some interesting stuff being done around decision-making with implanted electrodes in monkeys, but that's understandably not to everyone's taste.

EEG can do real time imaging because the sampling rate is much better. EEG also requires fairly intensive data processing, but usually it can be done faster than fMRI because: 1. There is often less/different information in EEG. If you have 64 nodes, you will have less information to process than the >100000 voxels in fMRI. 2. There have been more reasons to look at EEG in real time. fMRI analyses are generally done with group level statistics, so usually you have to run many participants which takes weeks-months anyways.

>There isn't any existing tech to take surface data and "map" it to internal states

Eh, sort of. Algorithms like LORETA which do source reconstruction have been around for a while, and do an OK job of finding the general area of the source of a signal.

> fMRI takes a lot of post-processing with complex statistics to produce any kind of useful information.

So does EEG. The reason EEG is closer to real-time than fMRI is because of the sampling rate. With fMRI, .9 Hz is considered quite good. For EEG, 2000 Hz is considered standard.

Standard? 2000 Hz sampling rate may be used with intracranial electrodes.

If the EEG is measured from the scalp 200-250 Hz sampling rate is usually good enough. It enables EEG recordings up to 80-90 Hz. Most clinical uses are interested EEG below 40 Hz.

My mistake: I got mixed up and reported standard sampling rates for MEG.

You are, of course, correct.

Thanks for the clarification. I've only worked with fMRI, so wasn't aware of the ins and outs of EEG processing despite being aware of the spacial / temporal resolution difference. It makes this method even more suspicious!

> when processing long runs we'd get on with other stuff while the data was being processed.

The main reason why fMRI processing takes so long is the reluctance of the major neuroimaging suites to embrace GPUs. A newer suite, BROCCOLI [1], is substantially faster but has much less adoption and isn't quite as full-featured as the other suites.

[1] https://www.frontiersin.org/articles/10.3389/fninf.2014.0002...

Not just GPUs, most labs don't embrace parallelism in general. I managed to get a 55x speedup between serial and parallel versions using the same program (SPM) by using several mid-grade computers and caching (Sorry I don't have anything written on this, I've been putting it off).

It's hard to get researchers to switch using the algorithms that they know and are comfortable with, which is why I think stuff like BROCCOLI, or PSOM haven't taken off.

There are real questions regarding the degree of locality information from eeg. The temporal resolution though is measured in ms.

There are real time fMRI research...particularly in the use of classifiers. The temporal resolution tends to be around 2-7 seconds (2 seconds for each acquisition, but the hemodynamic response is over the course of 7+ seconds..

You can do LORETA inverse solution to go from EEG to voxels which approximate internal sources. The problem is that this model does several suppositions about the brain structure that may or may not be true. In my opinion, surface currents are only good for surface sources, as the neurons in cortex are aligned and thus generate relatively strong currents in comparison to neurons inside the brain which are positioned "randomly".

You can do real-time fMRI for relatively simple tasks. Saying a word, tapping a finger, moving the tongue. “Real-time” is the wrong phrasing as the data builds up over several runs but you see it as it builds and get something useful a minute of two into acquisition.