Oct 14, 2011 · This paper presents a new classification framework for brain-computer interface (BCI) based on motor imagery.

Mar 21, 2012 · The main idea is to use spatial covariance matrices as EEG signal descriptors and to rely on Riemannian geometry to directly classify these ...

Aug 15, 2024 · The main idea is to use spatial covariance matrices as EEG signal descriptors and to rely on Riemannian geometry to directly classify these ...

Abstract—This paper presents a new classification framework for Brain Computer Interface (BCI) based on motor imagery. This framework involves the concept ...

The main idea is to use spatial covariance matrices as EEG signal descriptors and to rely on Riemannian geometry to directly classify these matrices using the ...

The main idea is to use spatial covariance matrices as EEG signal descriptors and to rely on Riemannian geometry to directly classify these matrices using the ...

May 9, 2024 · Motor imagery (MI) based brain-computer interfaces (BCIs) decode the users' intentions from electroencephalography (EEG) to achieve information ...

The main idea is to use spatial covariance matrices as EEG signal descriptors and to rely on Riemannian geometry to directly classify these matrices using the ...

Jan 25, 2021 · Multiclass brain–computer interface classification by riemannian geometry. IEEE Transactions on Biomedical Engineering, 59(4):920–. 928, 2011 ...

Using a differential geometry framework, different algorithms are proposed in order to classify covariance matrices in their native space within the ...