Towards Adaptive Classification using Riemannian Geometry approaches in Brain-Computer Interfaces

Abstract : The omnipresence of non-stationarity and noise in Electroencephalogram signals restricts the ubiquitous use of Brain-Computer interface. One of the possible ways to tackle this problem is to adapt the computational model used to detect and classify different mental states. Adapting the model will possibly help us to track the changes and thus reducing the effect of non-stationarities. In this paper, we present different adaptation strategies for state of the art Riemannian geometry based classifiers. The offline evaluation of our proposed methods on two different datasets showed a statistically significant improvement over baseline non-adaptive classifiers. Moreover, we also demonstrate that combining different (hybrid) adaptation strategies generally increased the performance over individual adaptation schemes. Also, the improvement in average classification accuracy for a 3-class mental imagery BCI with hybrid adaption is as high as around 17% above the baseline non-adaptive classifier.
Complete list of metadatas

Cited literature [9 references]  Display  Hide  Download

https://hal.inria.fr/hal-01924646
Contributor : Fabien Lotte <>
Submitted on : Friday, November 16, 2018 - 10:26:35 AM
Last modification on : Thursday, May 9, 2019 - 4:16:17 PM
Long-term archiving on : Sunday, February 17, 2019 - 1:27:59 PM

File

Winter_Conference_BCI.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01924646, version 1

Citation

Satyam Kumar, Florian Yger, Fabien Lotte. Towards Adaptive Classification using Riemannian Geometry approaches in Brain-Computer Interfaces. BCI 2019 - IEEE International Winter Conference on Brain-Computer Interfaces, Feb 2019, Jeonseong, South Korea. ⟨hal-01924646⟩

Share

Metrics

Record views

543

Files downloads

496