Human-machine interfaces (HMIs) based on muscular and kinematic information promise intuitive real-time control of assistive devices such as grasp neuroprosthesis for persons with cervical spinal cord injury (SCI). However, interpreting this data is challenging due to high dimensionality and nested discriminative information. Hence, feature engineering and ranking are imperative to minimize computational load while maintaining high performance. In this work, we recorded electromyography (EMG) and kinematic (acceleration, orientation, angular rate) information of inertial measurement units (IMUs) during reach-and-grasp movements (uni-/bimanual palmar/lateral grasps) in groups of non-disabled people ($\mathrm{n}=12$) and of people with incomplete cervical SCI ($\mathrm{n}=3$). We extracted 12 EMG and 11 IMU feature types of 8 EMG and 45 IMU channels. We applied the feature selection approaches chi-square, maximum-relevance-minimum-redundancy (MRMR), Random Forest (RF), and Boruta for dimensionality reduction of the feature set and evaluated resulting subsets. We could show for both groups that there was no significant decrease in classification accuracy (RF model) with chi-square and Boruta subsets compared to the baseline set of all features, despite their heavily reduced dimensionalities (<25% and <74%, respectively). Accuracies peaked at 98.6 ± STD 0.9% (control group, Boruta) and 97.7 ± STD 1.1% (participants with SCI, Boruta). We found the MRMR subsets to be performing significantly worse. We could further show high information interpretability of chi-square and RF scores that indicated the importance of sensors and extracted features for reach-and-grasp classification. We plan to investigate how the approach can be implemented in real-time reach-and-grasp HMIs for persons with SCI.

Brain-computer interfaces (BCIs) could enable persons with cervical spinal cord injury (SCI) to intuitively control assistive motor devices for regaining lost grasping function. Previous studies, mostly performed in non-disabled persons, have already shown that complex upper limb movements can be decoded from the low-frequency time domain of the electroencephalogram (EEG).In this work, we attempted to translate these results to persons with cervical SCI and investigated whether executed reach and attempted grasp actions could be decoded from their EEG signals. For this, we chose three different reach-and-grasp actions, two unimanual and one bimanual, towards objects of daily life. During participants’ self-initiated, executed reach and attempted grasp actions, we recorded EEG using mobile, water-based electrodes. We measured two participants with subacute cervical SCI who had preserved shoulder movements and elbow flexion but no wrist and hand functions. Both repeated the session three times. We also recorded the EEG of 10 non-disabled persons performing the same tasks (control group). We extracted and analyzed movement-related cortical potentials (MRCPs) from the EEG’s low-frequency time domain. Consecutively, we assessed the decoding capabilities of two linear (shrinkage based linear discriminant analysis (sLDA), linear support vector machine (SVM)) and two non-linear (Random Forests (RF), naive Bayes (NBC)) classification models for the discrimination of the grasp actions.We could show that sLDA, SVM, and Random Forest yielded comparable classification results on average with 63.4% ± SD 9% for participants with SCI and 69.7% ± SD 9% for the control group (chance level 29.3%). Our results indicate that it is feasible to decode executed reach and attempted grasp actions from MRCPs of persons with subacute cervical SCI. Future measurements will provide additional data to assess the generalizability of our results in a larger group of people with cervical SCI.

In their early days, brain-computer interfaces (BCIs) were only considered as control channel for end users with severe motor impairments such as people in the locked-in state. But, thanks to the multidisciplinary progress achieved over the last decade, the range of BCI applications has been substantially enlarged. Indeed, today BCI technology cannot only translate brain signals directly into control signals, but also can combine such kind of artificial output with a natural muscle-based output. Thus, the integration of multiple biological signals for real-time interaction holds the promise to enhance a much larger population than originally thought end users with preserved residual functions who could benefit from new generations of assistive technologies. A BCI system that combines a BCI with other physiological or technical signals is known as hybrid BCI (hBCI). In this work, we review the work of a large scale integrated project funded by the European commission which was dedicated to develop practical hybrid BCIs and introduce them in various fields of applications. This article presents an hBCI framework, which was used in studies with nonimpaired as well as end users with motor impairments.

For individuals with high spinal cord injury (SCI), restoring missing grasping function is a high priority. Neuroprostheses based on functional electrical stimulation (FES) can partly compensate the loss of upper extremity function in people suffering from tetraplegia. With noninvasive, multichannel neuroprostheses a pinch and power grasp can be accomplished for everyday use. Hybrid systems combining FES with active orthoses hold promise for restoring a completely lost arm function. Novel control interfaces are needed to make full use of the many degrees of freedom of complex hybrid neuroprostheses. Motor imagery (MI)-based brain-computer interfaces (BCIs) are an emerging technology that may serve as a valuable adjunct to traditional control interfaces for neuroprosthetic control. Shared control and context-specific autonomy are most effective for reducing the users' workload. The modularity of upper extremity neuroprostheses as well as their associated control interfaces enable customization of the systems to adapt to the impairment and needs of each individual end user. This work provides an overview of the application of noninvasive hybrid BCI-controlled upper extremity neuroprostheses in individuals with high SCI with a strong focus on the results from the European Integrated Project Tools for Brain-Computer Interaction and will describe the challenges and promises for the future.