Marie‐Constance Corsi

This paper develops a longitudinal BCI framework using neuronal avalanches and graph theory to predict future brain states during motor-imagery tasks, improving decoding accuracy and reducing calibration needs.

This paper introduces a two-phase deep learning framework for EEG-MEG decoding in BCI, showing improved classification accuracy and transferability across participants.

This study compares Gabor-based and classical flicker for SSVEP BCIs, finding Gabor flicker more comfortable at low frequencies and plain flicker better at high frequencies, with optimal performance when matched.

This paper introduces a network-based approach for movement recognition using kinematic data, showing that the kinectome outperforms PCA in classification accuracy and interpretability.

This study shows that motor imagery modulates cardiac-cortical and cerebellar networks, linking brain-heart interactions to motor learning and performance.

This paper identifies neuronal avalanches as biomarkers to predict BCI performance and guide personalized training protocols.

This study uses a biophysical model to investigate how neural mechanisms change during BCI training, finding training-induced excitatory-inhibitory reconfiguration in sensorimotor areas.

This study uses neuronal avalanches to improve brain-computer interfaces by showing their task-specific differences can be decoded better than current methods.

This paper identifies subject-specific brain features by analyzing non-linear neuronal avalanches in magnetoencephalographic data, showing improved identifiability compared to linear methods.

This study shows that increased task-related activity and reduced connectivity between non-targeted brain regions predict better performance during BCI learning.