Common Spatial Patterns (CSP) are widely used in motor imagery (MI)-based braincomputer interfaces (BCIs). While classification pipelines combine multiple components to optimize performance, MI-based neurofeedback (MI-NF) requires selecting a single …
Electroencephalography (EEG) provides precise neural measures for brain-computer interface (BCI) applications monitoring cognitive states. This empirical study evaluates the sensitivity of established EEG spectral markers to distinguish sustained …
Decoding electroencephalography (EEG) signals with deep learning remains challenging due to strong interand intra-individual variability, low signal-to-noise ratio, and the absence of an optimal architecture. While deep neural networks have …
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 …
The brain is a complex system requiring multimodal approaches to better understand cognitive or motor functions. Thus, different and complementary electroencephalographic (EEG) neurophysiological features are available at various spatial, frequency, …
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 …