Positive Technology Journal

 

Electrodes attached to the patient's scalp measure the brain's electrical signals, which are amplified and transmitted to a computer. Highly efficient algorithms analyze these signals using a self-learning technique. The software is capable of detecting changes in brain activity that take place even before a movement is carried out. It can recognize and distinguish between the patterns of signals that correspond to an intention to raise the left or right hand, and extract them from the pulses being fired by millions of other neurons in the brain. These neural signal patterns are then converted into control instructions for the computer. "The aim of the project is to help people with severe motor disabilities to carry out everyday tasks. The advantage of our technology is that it is capable of translating an intended action directly into instructions for the computer," says team leader Florin Popescu. The Brain2Robot project has been granted around 1.3 million euros in research funding under the EU's sixth Framework Programme (FP6). Its focus lies on developing medical applications, in particular control systems for prosthetics, personal robots and wheelchairs. The researchers have also developed a "thought-controlled typewriter", a communication device that enables severely paralyzed patients to pick out letters of the alphabet and write texts. The robot arm could be ready for commercialization in just a few years' time.

Press release:Brain2Robot

Project page:Brain2Robot

Quasi-movements: A novel motor-cognitive phenomenon.

Neuropsychologia. 2007 Oct 22;

Authors: Nikulin VV, Hohlefeld FU, Jacobs AM, Curio G

We introduce quasi-movements and define them as volitional movements which are minimized by the subject to such an extent that finally they become undetectable by objective measures. They are intended as overt movements, but the absence of the measurable motor responses and the subjective experience make quasi-movements similar to motor imagery. We used the amplitude dynamics of electroencephalographic alpha oscillations as a marker of the regional involvement of cortical areas in three experimental tasks: movement execution, kinesthetic motor imagery, and quasi-movements. All three conditions were associated with a significant suppression of alpha oscillations over the sensorimotor hand area of the contralateral hemisphere. This suppression was strongest for executed movements, and stronger for quasi-movements than for motor imagery. The topography of alpha suppression was similar in all three conditions. Proprioceptive sensations related to quasi-movements contribute to the assumption that the "sense of movement" can originate from central efferent processes. Quasi-movements are also congruent with the postulated continuity between motor imagery and movement preparation/execution. We also show that in healthy subjects quasi-movements can be effectively used in brain-computer interface research leading to a significantly smaller classification error ( approximately 47% of relative decrease) in comparison to the errors obtained with conventionally used motor imagery strategies.

 


(Massey University)

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