Think fast!!

In extreme, technology has recently made it possible to actually control systems with the brain. This field, widely known as BCI, is extensive, but has offered irregular results. However, there have been successful experiments in the wheelchair field lately, most related to EEG, with some work on magneto-encephalography, near-infrared spectroscopy, and functional magnetic resonance imaging as well. EEG is a favorite because it is non-invasive and fairly comfortable to use. The main problem of EEG, though, is that it provides a large amount of data with a very poor signal to noise ratio, meaning that it is actually as difficult to extract patterns from captured signals as to find the proverbial needle in a haystack. Consequently, rather than looking for very precise commands, researchers mostly quantify a reduced number of bins -sometimes labeled as mental states- to choose among a limited number of options. A typical example is trying to move a square in a screen in any of the four dominant sides -right, left, up or down-. These commands could be translated into motion directives to the wheelchair.

However, in order to fit clearly in one of these bins, the user must keep a state of continuous awareness to adequately maneuver the wheelchair. Think, for example, about juggling with several balls while following a lively conversation at the same time. Obviously, fine control here is analogous to voice-based fine control, only harder, something that could lead to excessive mental load and exhaustion. Assuming that a person can not be concentrated 24/7, some researchers found a different technique that might do the trick: rather than clustering existing signals, it is also possible to provoke a strong one and detect it. The chosen one is usually the P300 evoked potential. This natural, involuntary response of the brain to infrequent stimuli is coherent to an oddball paradigm, where a random sequence of stimuli is presented, only one of which interests the subject. Around 300ms after the target flashes, there is a positive potential peak in the EEG signal, which can be reliably detected and related to the interesting stimulus. The P300-based BCI requires almost no user training and only a few minutes to calibrate the detection algorithm param and has been successfully used to control a wheelchair.

Here I want to outline the work of the young, spanish BitBrain company, not just because they are friends, but also because they are really good at what they do.

BTW, we've gone multiligual today, but videos are pretty self-explanatory (I hope)


Post a Comment

Newer Post Older Post Home
There was an error in this gadget

Recent News

-Biometrically adapted wheelchair control paper accepted in IEEE Trans. on NSRE :) -New paper on collaborative navigation in hospitals accepted in Autonomous Robots
There was an error in this gadget


Recent Comments