Objective. The objective of this work is to use the capability of spiking neural networks to capture the spatio-temporal information encoded in timeseries signals and decode them without the use of hand-crafted features and vectorbased learning and the realization of the spiking model on low-power neuromorphic hardware. Approach. The NeuCube spiking model was used to classify different grasp movements directly from raw surface electromyography signals (sEMG), the estimations of the applied finger forces as well as the classification of two motor imagery movements from raw electroencephalography (EEG). In a parallel investigation, the designed spiking decoder was implemented on SpiNNaker neuromorphic hardware, which allows low-energy real-time processing. Results. Experimental results reveal a better classification accuracy using the NeuCube model compared to traditional machine learning methods. For sEMG classification, we reached a training accuracy of 85% and a test accuracy of 84.8%, as well as less than 19% of relative root mean square error (rRMSE) when estimating finger forces from six subjects. For the EEG classification, a mean accuracy of 75% was obtained when tested on raw EEG data from nine subjects from the existing 2b dataset from "BCI Competition IV". Significance. This work provides a proof of concept for a successful implementation of the NeuCube spiking model on the SpiNNaker neuromorphic platform for raw sEMG and EEG decoding, which could chart a route ahead for a new generation of portable closedloop and low-power neuroprostheses.

No abstract

The more dynamic and unpredictable the development constraints, the more agile the development project should be to cope with and utilize inherent change. Especially in such contexts, aligning with the project's mission and vision, committing to next steps, and documenting the development path is challenging. To support the decision making process of self-organized agile development teams with an overview, a recent research paper proposes the Progress Map. The investigation at hand applies the Progress Map in semi-industrial development projects to empirically validate its applicability and performance in the form of a multi-case study. The results indicate that, given future refinements, this instrument can be valuable to plan, document, and communicate the progress of an agile development project.

No abstract