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Multiclass Brain-Computer Interface Classification by Riemannian Geometry


I . Introduction:

The studied article is titled “Multiclass Brain-Computer Interface Classification by Riemannian Geometry”. It was published on 21 March 2012, by the Institute of Electrical and Electronics Engineers (IEEE) Transaction on biomedical journal. IEEE is the world’s largest technical professional organization, dedicated to advancing technology for the benefit of humanity. It publishes the leading journals, transactions, letters and magazines in electrical engineering, computing, biotechnology, power, telecommunications and other technologies. The article is written by Alexandre Barachant, Stéphane Bonnet, Marco Congedo, and Christian Jutten.
Alexandre Barachant is a postdoctoral research follow in Burke-Cornell Medical Research Institute from New York. Stéphane Bonnet is working for CEA LETI in Grenoble . Marco Congedo is for CNRS. And Christian Jutten is a professor at University of Grenoble, and institut Universitaire de France (IUF).
The authors had presented, in this article, two new methods using Riemannian Geometry, to classify the BCIs applications based on Motor Imagery (MI). First, it introduces by presenting the classical method to do a classification of BCIs applications using the spatial filters. Then, it explains the use of covariance matrices in BCI. Obviously we have a short introduction of the Riemannian Geometry. Next, the two methods and the algorithms used are well explained, the Minimum Distance to Riemannian Mean (MDRM) and the Tangent Space Mapping (TSLDA). Then, we had some figures to illustrate the different results obtained using the two method of classification. Finally, it concludes by focusing on the advantages and constraints of the two methods, and presented the different issues for further theories and improvements. The study of the article is made of several parts, first in the introduction, a short presentation of the different authors, the journal and the idea of the article. Then, the background of the article. Next, a short state-of-the-art and the different contributions of the method are presented. I conclude by giving a short contrast about the article; contributions and further improvement and inconvenient.

II. Background of the article:

We need to know: what’s a BCI? Why we use it?
The Motor Imagery (MI) is a cognitive representation of a physical action which aims to improve or stabilize the real execution of a given hand’s movement.

General representation of BCI's applications