As above, so below? Towards understanding inverse models in BCI - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Journal of Neural Engineering Year : 2017

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


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.
Fichier principal
Vignette du fichier
lindgren2017asabove-preprint.pdf (650.13 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01669325 , version 1 (20-12-2017)



Jussi Lindgren. As above, so below? Towards understanding inverse models in BCI. Journal of Neural Engineering, 2017, 15 (1), ⟨10.1088/1741-2552/aa86d0⟩. ⟨hal-01669325⟩
217 View
344 Download



Gmail Facebook X LinkedIn More