Chess Meeting 2016/12/09

The next CHESS meeting will take place on Monday, December 12, at 10:00 am in room Chartreuse.

There will be 3 talks:

Victor MAURANDI
Title: Multimodal Approach to Remove Ocular Artifacts from EEG Signals Using Multiple Measurement Vectors
Abstract: This presentation deals with the extraction of eye-movement artifacts from EEG data using a multimodal approach. The gaze signals, recorded by an eye-tracker, share a similar temporal structure with the artifacts induced in EEG recordings by ocular movements. The proposed approach consists in estimating this specific common structure using Multiple Measurement Vectors which is then used to denoise the EEG data. This method can be used on single trial data and can be extended to multitrial data subject to some additional preprocessing. Finally, the proposed method is applied to gaze and EEG experimental data and is compared with some popular algorithms for eye movement artifact correction from the literature.

Florent BOUCHARD
Title: Approximate Joint Diagonalization according to the Natural Riemannian Distance”
Abstract: Here, we propose for the first time an approximate joint diagonalization (AJD) method based on the natural Riemannian distance of Hermitian positive definite matrices. We turn the AJD problem into an optimization problem with a Riemannian criterion and we developp a framework to optimize it. The originality of this criterion arises from the diagonal form it targets. We compare the performance of our Riemannian criterion to the classical ones based on the Frobenius norm and the log-det divergence, on both simulated data and real electroencephalographic (EEG) signals. Simulated data show that the Riemannian criterion is more accurate and allows faster convergence in terms of iterations. It also performs well on real data, suggesting that this new approach may be useful in other practical applications.

Pierre PIGNEDE
Title: Interface for codes and data public access.

The last CHESS meeting of 2016 will be followed at 11:30 by a friendly drink.

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