Brain computer interface based neuroprosthesis in limb paralysis

A new study reports the development of a brain computer interface for flexing and extending fingers towards finding a solution for limb paralysis due to brain damage.

Events such as stroke may cause brain damage leading to limb paralysis. Brain computer interface (BCI) technology has been studied by several laboratories for motor neurorehabilitation, motor replacement and assistive technologies. Normally, visual inputs like a cursor on a screen or a robotic hand moving were used as feedback. However one of the ultimate goals of the so called brain computer/machine interfaces is to close the loop between brain and paralyzed muscles.

It is an open question whether proprioceptive feedback (awareness of the limb position) could affect the regulation of brain oscillations if some sensory feedback is preserved in the paralyzed limbs. A research team in Tubingen, Germany led by Professor Niels Birbaumer has developed a brain computer interface coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers while the users are thinking of that exact movement.

brain computer interphase stroke neuroprostheses
 Brain Computer Interface. A) the participant wearing a cap with EEG channels and the hand attached to the orthosis. B) The experimental time line (C) Orthosis with the fingures and (D) the drawing of the EEG channels used in the experiments. For details see the reference cited below.

In this study, which was recently published in PLoS ONE, 24 healthy participants performed five different tasks of closing and opening the hand. Performance of the proprioceptive brain computer interface had to be defined since in this case the users obtained feedback from seeing and feeling the hand opposed to the classical visual feedback (e.g. cursor movement on screen). Ander Ramos-Murguialday, the first author in the paper, and colleagues defined performance as the difference in power of the sensorimotor rhythm during motor task and rest and calculated offline for different tasks. This performance measure would reflect the time the user moves, starts moving or moves continuously the orthosis. The hand of the participant was fixed to a hand orthosis which could be driven by the participant’s brain activity.

The  participants were asked to report which movement parameter they used as reference for their BCI performance. Participants were divided in three groups (1 experimental group and 2 control groups) depending on the feedback receiving when controlling the orthosis movement during task. The experimental group (9 participants) received contingent positive feedback (participants' sensorimotor rhythm (SMR) desynchronization was directly linked to the hand orthosis movements), control group 1 (8 participants) contingent "negative" feedback (participants' sensorimotor rhythm synchronization was directly linked to the hand orthosis movements) and control group 2 (7 participants) false feedback (no link between brain oscillations and orthosis movements).

Ramos-Murguialday and colleagues observed that proprioceptive feedback (feeling and seeing hand movements) improved BCI performance significantly. Furthermore, in the experimental group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. This demonstrated that the participants in the experimental group learned to move their hand only thinking about it. The system presented a delay of around 220 milli second and was not perceived by the users. Participants used the time moving the orthosis and the number of movement initiation as reference for feedback.

Furthermore, Niels Birbaumer team observed a priming or excitatory effect of the use of the proprioceptive BCI (in the experimental group only) in the neural networks involved in the visuomotor task resulting in stronger brain response during active and passive movements. To summarize, Ramos-Murguialday and colleagues demonstrated that the use of contingent positive proprioceptive feedback BCI can be used to control movement of exoskeletons only thinking about the same movement and this will excite the brain in such a way that posterior passive or active movements will result in stronger excitation of the brain. These results can have important consequences for motor neurorehabilitation for example stroke patients using a proprioceptive BCI as a boosting effect for posterior physiotherapy.

Science news reference: 

Ramos-Murguialday A, Schürholz M, Caggiano V, Wildgruber M, Caria A, et al. (2012) Proprioceptive Feedback and Brain Computer Interface (BCI) Based Neuroprostheses. PLoS ONE 7(10): e47048. doi:10.1371/journal.pone.0047048.

Link to Dr. Niels Birbaumer Bio

Science news source: Science News