Characteristics based on signal processing

In Motor-Imagery based Brain-Computer Interfaces (MI-BCIs) MI tasks modulate EEG activity in the α and β frequency bands (8-30 Hz). Data driven methods are often used to select features in those bands during the system calibration, with little consideration for the resulting human performances. Indeed, the system is learning without taking into account any neurophysiological constraints or user related constraints.

One of these methods is the one that selects the frequency band that better discriminate which task the user is doing (B. Blankertz, 2007). Our study suggests that online MI-BCI performances correlate with the characteristics of the frequency band, selected using machine learning. This raises questions regarding the causality link direction: could we use this frequency band to predict online performances? Could we improve machine learning algorithms with constraints on the band to be selected? Is this correlation due to a covert confounding factor (e.g., mental strategy)?

We are currently running experiments using a modified version of the frequency band selection algorithm (i.e, a version with constraints) in order to better understand the relation between the algorithm and online BCI performances. In addition to the previous algorithm, we are also using common spatial pattern (CSP), a spatial method to extract features from the EEG signals. We are currently identifying characteristics of those CSP such as the number of foci or their position.

Publications in scientific journals

When should MI-BCI feature optimization include prior knowledge, and which one? (Accepted with revision in Brain-Computer Interfaces

>C. Benaroch, M.S. Yamamoto, A. Roc, P. Dreyer, C. Jeunet, F. Lotte

In Brain-Computer Interfaces (Accepter with revisions)

International conferences

MI-BCI Performances correlate with subject-specific frequency band characteristics.

C. Benaroch, C. Jeunet, F. Lotte

In 8th International BCI Meeting, Virtual BCI meeting, 2021

Student award


National conferences

Proposition of an optimization of the selection of a discriminative frequency algorithm for Motor-Imagery based BCI

C. Benaroch, C. Jeunet, F. Lotte

In Journées CORTICO 2020-COllectif pour la Recherche Transdisciplinaire sur les Interfaces Cerveau-Ordinateur, 2020


Should frequency band selection algorithms include neurophysiological constraints?

M.S. Yamamoto, C. Benaroch, A. Roc, T. Monseigne, F. Lotte

In Journées CORTICO 2021-COllectif pour la Recherche Transdisciplinaire sur les Interfaces Cerveau-Ordinateur, 2021


Characteristics based on the classifier

The objective of classification is to translate the extracted features into commands to best evaluate the user’s intent. Among the various classification algorithms that exist, we are using the linear discriminant analysis (LDA). We believe that it can also be interesting to extract characteristics of the LDA classifier to better understand BCI performances. One of those characteristics could be the sparsity of the LDA features weight. Finally, as BCIs are “communication” systems, we found it interesting to try to extract features from the feedback the user is given.