Automatic movement recognition is often used to support various fields such as clinical, sports, and security. To date, there is a lack of a classification feature that is both interpretable and not movement-specific. Previous studies on motion …
Automating the diagnostic process steps has been of interest for research grounds and to help manage the healthcare systems. Improved classification accuracies, provided by ever more sophisticated algorithms, were mirrored by the loss of …
Understanding the mechanisms of motor imagery, the mental simulation of movement without execution, is key for the development of neurotechnologies, including understanding inter-individual variability in motor imagery performance. For instance, for …
Code-modulated visual evoked-potential (c-VEP) based reactive brain-computer interfaces (BCIs) deliver high information-transfer rates with minimal calibration, yet performance often collapses when models are transferred between users. We therefore …
This paper proposes a multilayer graph model for community detection based on multiple observations. This scenario is common when different estimators are used to infer graph edges from signals at the nodes, or when various signal measurements are …
Identifying the driver nodes of a network has crucial implications in biological systems from unveiling causal interactions to informing effective intervention strategies. Despite recent advances in network control theory, results remain inaccurate …
Real-world networks typically exhibit several aspects, or layers, of interactions among their nodes. By permuting the role of the nodes and the layers, we establish a new criterion to construct the dual of a network. This approach allows to examine …