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  • Periodic Reporting for period 1 - Feel your Reach (Non-invasive decoding of cortical patterns induced by goal directed movement intentions and artificial sensory feedback in humans)

Feel your Reach Report Summary

Project ID: 681231
Funded under: H2020-EU.1.1.

Periodic Reporting for period 1 - Feel your Reach (Non-invasive decoding of cortical patterns induced by goal directed movement intentions and artificial sensory feedback in humans)

Reporting period: 2016-05-01 to 2017-10-31

Summary of the context and overall objectives of the project

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 – 40% of spinal cord injured individuals are tetraplegics – 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 from the beginning of the project to the end of the period covered by the report and main results achieved so far

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, for the decoding of movement kinematics, for electrooculography (EOG) correction methods and EOG influence to movement decoding, and for the detection of error-related potentials. With respect to the movement intention detection, our findings suggest that the detection of movement intention is increased when the movement has a specific goal. Moreover, in a self-paced motor imagery (MI) task of a reaching movement, we could reliably detect in a time-locked manner the movement imagination (~80%). Within the same paradigm, we found differences in the EEG between internally-driven and externally-driven target selection. For an accurate decoding of movement kinematics it is important to ensure that EOG artifacts are not present in the data. We evaluated the stability of different EOG correction algorithms over blocks recorded 1 hour apart. We found that an algorithm, based on artifact subspace subtraction, could attenuate horizontal and vertical eye movement artifacts to chance level, and maintain resting brain activity. When the BCI misinterprets the brain signals from the user, it initiates an unintended command. For the user, the mismatch between the expected action and the received feedback leads to the generation of an error-related potential (ErrP). On average, we successfully classified ErrP trials with 80.3% accuracy and correct trials with 94.3% accuracy. We found no significant difference in terms of ErrP between the trials in which the feedback about the controlled cursor on the screen was smooth or jittered.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

With our findings from the first period we contribute to the progress of the state of the art by developing detection methods suitable for goal-directed continuous movements in healthy individuals. Moreover, we studied the stationarity of correction algorithms of eye movements’ contamination in EEG signals. Our findings have an impact on the use of a neuroprosthesis controlled by EEG activity over time. Furthermore, we found that the instability in the kinematics of the neuroprosthesis/robotic arm does not hinder the decoding of error-potentials. Our objectives for the next period envision online implementations of our detection algorithms, which would allow us to move one step closer to the functional applications in high SCI individuals.
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