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Report

Teaser, summary, work performed and final results

Periodic Reporting for period 2 - Feel your Reach (Non-invasive decoding of cortical patterns induced by goal directed movement intentions and artificial sensory feedback in humans)

Teaser

Summary

In Europe estimated 300,000 people are suffering from a spinal cord injury (SCI) with 11,000 new injuries every year. The consequences of spinal cord injury affect both these individuals and the society. The loss of arm motor functions â€“ 40% of spinal cord injured individuals are tetraplegics â€“ leads to a life-long dependency on care-givers and therefore to a dramatic decrease in the quality of life in these often young individuals. With the help of neuroprostheses, grasping and elbow function can be substantially improved. However, remaining body movements often do not provide enough degrees of freedom to control naturally the neuroprosthesis. The ideal solution for a natural control of an upper extremity neuroprosthesis would be to directly record motor commands from the corresponding cortical areas and convert them into control signals. This would allow bypassing the interrupted nerve fiber tracts in the spinal cord.
A brain-computer interface transforms voluntarily induced changes of brain signals into control signals and serves as a promising human-machine interface. In the last decade, we showed first results in EEG-based control of a neuroprosthesis in several individuals with SCI; however the control is not yet intuitive enough and somewhat cumbersome. The objective of FeelYourReach is to develop a novel control framework that incorporates goal directed movement intention, movement decoding, error processing and sensory feedback processing to allow a more natural control of a neuroprosthesis. We believe that such a framework would enable individuals with high SCI to move independently, improving their quality of life.

Work performed

The main objective of the first reporting period was to investigate in healthy individuals basic aspects for the setup of an intuitive, non-invasive electroencephalography (EEG)-based movement decoding system. Specifically, we carried out several studies for the investigation and detection of goal-directed movement intentions and for the decoding of movement kinematics. In addition, we studied several electrooculography (EOG) correction methods and the EOG influence on movement decoding. We also investigated the detection of error-related potentials under several movement conditions and built a device to deliver kinesthetic feedback in real-time closed-loop brain-computer interface (BCI) paradigms.

Within the second reporting period we published several papers in high impact journals, in which we disseminated our achievements towards the delivery of an intuitive control command of upper-limb neuroprosthesis based on EEG signals. So far our findings have been derived from multiple studies in healthy individuals. Specifically, in a second study on the neural correlates of goal-directed movements, we found out that it is possible to detect self-paced movement imaginations of a reach-and-grasp based on low-frequency time-domain features. Additionally, we showed that the event-related cortical potentials differ depending on whether the target selection process was internally-driven by the participant or externally-cued by the paradigm. In other studies, we investigated the decoding of movement covariates such as position and velocity from low frequency EEG signals in visuomotor and oculomotor tracking tasks, and found a better encoding of velocity in sensorimotor areas during the visuomotor task. In another study, we explored the relation between neural and behavioural (muscle and kinematic signals) correlates of a large variety (33) grasping movements in a large population (31) of healthy subjects. We found that EEG activity reflected different movement covariates in different stages of grasping. During the pre-shaping stage, centro-parietal EEG in the lower beta frequency band reflected the objectâ€™s shape and size, whereas during the finalization and holding stages, contralateral parietal EEG in the mu frequency band reflected muscle activity. Next, we studied the detection of error-related potentials and the influence of feedback during continuous motor control. We could asynchronously detect the erroneous events with a high accuracy, and found that the type of feedback (jittered or smooth movement trajectories during the continuous control) is not influencing the detection of error-related potentials. More recently, we also started to investigate different strategies to deliver kinesthetic feedback.

Final results

All our findings are beyond the state of the art. With our findings from the first period we contribute to the progress of the state of the art by introducing innovative BCI paradigms that accommodate the userâ€™s intention to move towards self-chosen targets, we were able to predict movement covariates such as velocity, from the low-frequency EEG signals in visuomotor and oculomotor tasks. Moreover, we gathered a large multimodal dataset (simultaneous acquired neural, muscle and kinematic information) consisting of 33 different grasp types and we explored the relation between the neural and behavioral representation of the grasps in different stages of the movement (hand-preshaping, reaching the final grasping posture and holding). Next, we will conduct confirmatory studies to evaluate follow-up hypotheses. We also showed the impact of jittered visual feedback in a movement task on error-potentials and built a novel stimulation device for kinesthetic feedback delivery.

Our findings from the second reporting period are published in high impact journals, in which we disseminated our achievements towards the delivery of an intuitive control command of upper-limb neuroprosthesis based on EEG signals. Specifically, our most recent findings are in the detection of goal-directed movements, the decoding of movement covariates from low frequency EEG signals and their relation with neural activity, the detection of error-potentials and the influence of feedback during continuous motor control, and different strategies to deliver kinesthetic feedback to spinal-cord injured individuals.

In the remaining time of the project we are going to incorporate people (very) with high spinal cord injury in our EEG experiments, transfer methods and knowledge gained in the first half of the project. Also, we are starting to combine and integrate methodologies already established in the first half of the project to build up a single system.