To predict brain age, harmonised and scaled ROIrelfrom the train set were inputted to a linear support vector regression (SVR) model as implemented in the Python package scikit-learn [69] with a similar approach as described previously [60]. A systematic hyperparameter search for C was conduct...
making assumptions about a possible diagnosis of a neurological disorder. This hinders the use of existing tools in psychiatry, where disorders are characterised by a heterogeneous, subtle and widespread pattern of abnormalities across the brain. Here we presentNeurofind, a new user-friendly and freel...
In this contribution , we propose a scheme to adapt data augmentation in EEG-based BCI with a Riemannian standpoint : geometrical properties of EEG covariance matrix are taken into account to generate new training samples. Neural network are good candidates to benefit from such training scheme and...
In this contribution , we propose a scheme to adapt data augmentation in EEG-based BCI with a Riemannian standpoint : geometrical properties of EEG covariance matrix are taken into account to generate new training samples. Neural network are good candidates to benefit from such training scheme and...
In this contribution , we propose a scheme to adapt data augmentation in EEG-based BCI with a Riemannian standpoint : geometrical properties of EEG covariance matrix are taken into account to generate new training samples. Neural network are good candidates to benefit from such training scheme and...
In this contribution , we propose a scheme to adapt data augmentation in EEG-based BCI with a Riemannian standpoint : geometrical properties of EEG covariance matrix are taken into account to generate new training samples. Neural network are good candidates to benefit from such training scheme and...