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As above, so below? Towards understanding inverse models in BCI

Jussi Lindgren 1, *
* Corresponding author
1 Hybrid - 3D interaction with virtual environments using body and mind
Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS
Abstract : In Brain-Computer Interfaces (BCI), measurements of the users brain activity are classified into commands for the computer. With EEG-based BCIs, the origins of the classified phenomena are often considered to be spatially localized in the cortical volume and mixed in the EEG. Does the reconstruction of the source activities in the volume help in building more accurate BCIs? The answer remains inconclusive despite previous work. In this paper, we study the question by contrasting the physiology-driven source reconstruction with data-driven representations obtained by statistical machine learning. Our analysis suggests that accuracy improvement from physiological source reconstruction in BCI may be expected mainly when machine learning cannot be used or where it produces suboptimal models. However, we argue that despite the use of physiology-based source reconstruction, data-driven techniques remain necessary to attain accurate BCI systems. Finally, we observe that many difficulties of the surface EEG classification remain challenges in the reconstructed volume.
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Submitted on : Wednesday, December 20, 2017 - 5:48:52 PM
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Jussi Lindgren. As above, so below? Towards understanding inverse models in BCI. Journal of Neural Engineering, IOP Publishing, 2017, 15 (1), ⟨10.1088/1741-2552/aa86d0⟩. ⟨hal-01669325⟩



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