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...
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...
1 * /stereo_inertial_publisher/linearAccelCovariance: 0 * /stereo_inertial_publisher/lrcheck: True * /stereo_inertial_publisher/mode: depth * /stereo_inertial_publisher/monoResolution: 720p * /stereo_inertial_publisher/mxId: * /stereo_inertial_publisher/nnName: x * /stereo_inertial_publisher/po...
Consider the following practical application using Python, where we have two variables ‘A’ and ‘B’ with a non-linear relationship: importnumpyasnpfromscipy.statsimportspearmanrfromscipy.statsimportpearsonrimportmatplotlib.pyplotasplt# Generating non-linear datanp.random.seed(42)A=np.linspace(-...
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...