EEG imaging methods applied to brain-computer interface

Nowadays best Brain Computer Interface (BCI) methods are based on invasive recording of electrical brain activity. Surface electrodes methods are not as accurate. This is partially due to the filtering of the signal by the skull and to the distance to the sources. Surprisingly methods for solving the EEG inverse problem have seldom been used to overcome these limitations. Inverse solution methods can be adapted to either pre-process the data or as a classification method. In this paper we study the application of well-known Inverse Solution methods to the BCI. Methods are the Minimum Norm method, two methods based on respectively a Laplacian and a location prior, as well as two parametric methods based on subspace correlation. Data are processed with an inverse solution method. Then the data are classified by measuring the activation in preselected areas. Processing with Inverse method can improve the classification obtained outside of the skull by more than 10%. Furthermore these methods can be used without increasing the computation time. With our simple paradigm we obtained 85% of good classification.

• [Handle]http://hdl.handle.net/2078.1/67752
• [DOI]https://doi.org/10.1117/12.654349