AUTHOR=La Fisca Luca , Vandenbulcke Virginie , Wauthia Erika , Miceli Aurélie , Simoes Loureiro Isabelle , Ris Laurence , Lefebvre Laurent , Gosselin Bernard , Pernet Cyril R. 

TITLE=Biases in BCI experiments: Do we really need to balance stimulus properties across categories?

JOURNAL=Frontiers in Computational Neuroscience

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2022.900571

DOI=10.3389/fncom.2022.900571

ISSN=1662-5188

ABSTRACT=Brain Computer Interfaces (BCIs) consist in an interaction between humans and computers
with a specific mean of communication, such as voice, gestures, or even brain signals that are
usually recorded by an Electroencephalogram (EEG). To ensure an optimal interaction, the BCI
algorithm typically involves the classification of the input signals into predefined task-specific
categories. However, a recurrent problem is that the classifier can easily be biased by uncontrolled
experimental conditions, namely covariates, that are unbalanced across the categories. This
issue led to the current solution of forcing the balance of these covariates across the different
categories which is time consuming and drastically decreases the dataset diversity. The purpose
of this research is to evaluate the need for this forced balance in BCI experiments involving EEG
data.
A typical design of neural BCIs involves repeated experimental trials using visual stimuli to
trigger the so-called Event-Related Potential ( ERP ). The classifier is expected to learn spatio-
temporal patterns specific to categories rather than patterns related to uncontrolled stimulus
properties, such as psycho-linguistic variables (e.g., phoneme number, familiarity and age of
acquisition) and image properties (e.g., contrast, compactness and homogeneity). The challenges
are then to know how biased the decision is, which features affect the classification the most,
which part of the signal is impacted and what is the probability to perform neural categorization
per se. To address these problems, this research has two main objectives: 1) modeling and
quantifying the covariate effects to identify spatio-temporal regions of the EEG allowing maximal
classification performance while minimizing the biasing effect, and 2) evaluating the need to
balance the covariates across categories when studying brain mechanisms.
To solve the modeling problem, we propose using a linear parametric analysis applied to some
observable and commonly studied covariates to identify the part of the EEG signal related to
them. The biasing effect is quantified by comparing the regions highly influenced by the covariates
with the regions of high categorical contrast, i.e. parts of the ERP allowing a reliable classification.