Riemannian methods have established themselves as stateof-the-art approaches in Brain-Computer Interfaces (BCI) in terms of performance. However, their adoption by experimenters is often hindered by a lack of interpretability. In this work, we …
Ocular artifacts can significantly impact electroencephalography (EEG) signals, potentially compromising the performance of neurofeedback (NF) and brain-computer interfaces (BCI) based on EEG. This study investigates if the Approximate Joint …
textlessdivtextgreatertextlessptextgreaterCode modulated Visually Evoked Potentials (cVEP) is an emerging paradigm for Brain-Computer Interfaces (BCIs) that offers reduced calibration times. However, cVEP-based BCIs still encounter challenges related …
Current features used in motor imagery-based Brain-Computer Interfaces (BCI) rely on local measurements that miss the interactions among brain areas. Such interactions can manifest as bursts of activations, called neuronal avalanches. To track their …
Brain computer interfaces rely on cognitive tasks easy at first sight but that reveal to be complex to perform. In this context, providing engaging feedback and subject’s embodiment is one of the keys for the overall system performance. However, …
EEG signals acquired at different electrodes can be modelled as Signals on Graph, where the graph structure reflects the underlying brain Functional Connectivity (FC), representing brain region interactions. FC gives crucial information to detect …
In the recent years, brain computer interfaces (BCI) using motor imagery have shown some limitations regarding the quality of control. In an effort to improve this promising technology, some studies intended to develop hybrid BCI with other …
Context Passive Brain-Computer Interface (pBCI) has recently gained in popularity through its applications, e.g. workload and attention assessment. Nevertheless, one of the main limitations remains the important intra-and inter-subject variability. …
Riemannian BCI based on EEG covariance have won many data competitions and achieved very high classification results on BCI datasets. To increase the accuracy of BCI systems, we propose an approach grounded on Riemannian geometry that extends this …
This abstract describes a novel approach for handling brain-computer interfaces (BCI), that could be used for robotic applications. State-of-the-art approaches rely on the classification of covariance matrices in the manifold of symmetric …