One of the hopes for physically-handicapped people are brain-computer interfaces. Apart from giving amputees more control over artificial limbs, it can improve quality of life in other ways by allowing people without sufficient control over their arms or digits to interact more fully with computers—something that now has a significant impact on quality of life in the developed world. In addition, these interfaces have the potential to allow those who suffer from neurological conditions to control computers for communication and physical mobility. Last year, several papers were published that showed some very promising results, but then it all seemed to just stop.
A recent Journal of Neural Engineering article shows why progress seems to have slowed and gives an impression of the hurdles to be overcome. The basic premise of these systems are all the same: first, record the electrical brain activity using a skullcap of electrodes, then these signals are processed and fed into a genetic algorithm, which evolves to associate particular signals as instructions to move. This is then tested, with the result that only about 41 percent of attempts to move are followed, and once every 12 seconds or so, the computer decides you wanted to move when you really didn't. To put this in perspective, imagine that you were using this interface to control an artificial knee. Six in every ten steps would result in you tripping and once every 12 seconds you would randomly kick someone (actually, I can see a benefit there). Clearly, a lot of effort is going into reducing the rate of false positives and increasing the rate of true positives.
The problem is that the skullcap records a very generalized and quite noisy signal, from which useful information must be extracted. The current approach seems to be using increasingly sophisticated filtering techniques to extract certain known signals, such as movement-related potentials, beta rhythms, and Mu rhythms. Changes to these signals are then compared and correlated to actual movement events. Using these more sophisticated methods, the true positive rate remains at about 50 percent. However, the false positive result has been substantially reduced to about 0.1 percent.
Overall, this still isn't that good because the system doesn't pick up true positives very well. Although I was quite enthusiastic about the progress when this was first reported, it is becoming apparent that there is a lot of hard engineering work to do. The pessimist in me suggests that this approach will simply never be sensitive enough to pick up all the intended movements while still retaining a low false positive rate. This is because the skullcaps record a general, unlocalized signal from the brain and, from that, try to infer localized information.
This doesn't mean the approach will never yield good results though. For instance, if proven safe, more invasive methods could be used to extract a more localized signal. Or perhaps neurological studies will allow researchers to use filtering techniques that result in localized information. However, pure filtering and statistical association may never yield enough specificity by itself.
Journal of Neural Engineering, 2007, DOI: 10.1088/1741-2560/5/1/002