Adaptive machine learning methods for event related potential-based brain computer interfaces

Nathalie Gayraud 1
1 ATHENA - Computational Imaging of the Central Nervous System
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Non-invasive Brain Computer Interfaces (BCIs) allow a user to control a machine using only their brain activity. The BCI system acquires electroencephalographic (EEG) signals, characterized by a low signal-to-noise ratio and an important variability both across sessions and across users. Typically, the BCI system is calibrated before each use, in a process during which the user has to perform a predefined task. This thesis studies of the sources of this variability, with the aim of exploring, designing, and implementing zero-calibration methods. We review the variability of the event related potentials (ERP), focusing mostly on a late component known as the P300. This allows us to quantify the sources of EEG signal variability. Our solution to tackle this variability is to focus on adaptive machine learning methods. We focus on three transfer learning methods: Riemannian Geometry, Optimal Transport, and Ensemble Learning. We propose a model of the EEG takes variability into account. The parameters resulting from our analyses allow us to calibrate this model in a set of simulations, which we use to evaluate the performance of the aforementioned transfer learning methods. These methods are combined and applied to experimental data. We first propose a classification method based on Optimal Transport. Then, we introduce a separability marker which we use to combine Riemannian Geometry, Optimal Transport and Ensemble Learning. Our results demonstrate that the combination of several transfer learning methods produces a classifier that efficiently handles multiple sources of EEG signal variability.
Complete list of metadatas

Cited literature [119 references]  Display  Hide  Download

https://tel.archives-ouvertes.fr/tel-02100593
Contributor : Abes Star <>
Submitted on : Tuesday, April 16, 2019 - 9:56:08 AM
Last modification on : Wednesday, April 17, 2019 - 1:32:51 AM

File

2018AZUR4231.pdf
Version validated by the jury (STAR)

Identifiers

  • HAL Id : tel-02100593, version 1

Collections

Citation

Nathalie Gayraud. Adaptive machine learning methods for event related potential-based brain computer interfaces. Signal and Image processing. Université Côte d'Azur, 2018. English. ⟨NNT : 2018AZUR4231⟩. ⟨tel-02100593⟩

Share

Metrics

Record views

146

Files downloads

153