2 citations · 2 across the 4 of their papers we have counts for
4 papers
Enhancing Computational Efficiency of Motor Imagery BCI Classification with Block-Toeplitz Augmented Covariance Matrices and Siegel Metric
Igor Carrara, Theodore Papadopoulo
Electroencephalographic signals are represented as multidimensional datasets. We introduce an enhancement to the augmented covariance method (ACM), exploiting more thoroughly its m…
Pseudo-online framework for BCI evaluation: A MOABB perspective
Igor Carrara, Théodore Papadopoulo
Objective: BCI (Brain-Computer Interface) technology operates in three modes: online, offline, and pseudo-online. In the online mode, real-time EEG data is constantly analyzed. In…
An embedding for EEG signals learned using a triplet loss
Pierre Guetschel, Théodore Papadopoulo, Michael Tangermann
Neurophysiological time series recordings like the electroencephalogram (EEG) or local field potentials are obtained from multiple sensors. They can be decoded by machine learning…
Classification of BCI-EEG based on augmented covariance matrix
Igor Carrara, Théodore Papadopoulo
Objective: Electroencephalography signals are recorded as a multidimensional dataset. We propose a new framework based on the augmented covariance extracted from an autoregressive…