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There are many open-source solutions for BCI projects like BCI2000 (http://www.schalklab.org/research/bci2000), OpenVibe (http://openvibe.inria.fr/) and EEGLab (https://sccn.ucsd.edu/eeglab/index.php). That's based on the kinds of tools that our customers use. Most of these, aren't as pretty as tool like Neurovis, but in reality most of the information that we make use of to control prosthetics or signal intention involve looking at the temporal-frequency relationships between and within broad regions of the brain. There isn't a lot that you gain from just looking at the brain light up like this for BCI — the main use for a visualisation like this, is as the docs say, for diagnosis and determination of epilepsy since in epilepsy the activity you'd see is much higher than usual. EEG has better temporal resolution than FMRI; you are measuring the electrical activity rather than vascular changes, the former changes more rapidly than the other. EEG, however, is just the surface activity of the brain, so you don't get information about 'deeper' (physically) brain processes; this is where FMRI is invaluable. EEG is also limited to the size of the electrodes and how many electrodes you can physically place in one location. 256 electrodes on an EEG cap is about the limit you can get to. Electrocorticography (ECog) involves implanting electrodes on the dura, this can substantially improve the density of electrodes in a given area, however this doesn't measure deep brain activity and we have no way of leaving the electrode grid in place for long periods of time without risking infection. For BCI, we've been able to classify more classes of data using ECog than EEG — research by Kyousuke Kamada and Gerwin Schalk are informative. It's a very promising area if we can work out how to implant the electrodes, seal the skull and telemeter data out. Magnetoencephalography (MEG) can help with measuring deep brain activity, but there are other tradeoffs to consider. Essentially, the point where we are now is combining multiple techniques to get the best temporal, spatial and frequential compromises. Thus in answer to your question; no FMRI is not great for realtime responses, measuring the electrical activity has better temporal properties so EEG and ECog work better here. Sensor density is part of the problem, but once you solve density you then need to consider how you'll deal with deep brain measurements. Background; I own a company that distributes BCI equipment for g.tec medical engineering in the UK. We've been operating in this space for 8 years. I have a PhD in Pharmacology and a speciality in electrophysiology. |
The reason I ask about this is because my long term thinking is that while motion controlled virtual reality is fun and going to be good for exercise games and training, I think human-brain-computer interface represents the most promising of the interaction methods (nothing says you can't be hooked up and still typing, using a mouse, or motioncontrolling either).
You mentioned the medical desire of seeing deeper brain waves, but I just want to control a computer, so given the current state of the EEG's are they useful yet as a practical operating system control input?