EEG-based workload estimation across affective contexts

Christian MÃ¼hl, Camille Jeunet, Fabien Lotte
Front. Neurosci.. 2014-06-12; 8:
DOI: 10.3389/fnins.2014.00114

PubMed

1. Front Neurosci. 2014 Jun 12;8:114. doi: 10.3389/fnins.2014.00114. eCollection
2014.

EEG-based workload estimation across affective contexts.

Mühl C(1), Jeunet C(2), Lotte F(3).

Author information:
(1)Institut National de Recherche en Informatique et en Automatique, Bordeaux
Sud-Ouest Talence, France.
(2)Institut National de Recherche en Informatique et en Automatique, Bordeaux
Sud-Ouest Talence, France ; Laboratoire Handicap et Système Nerveux, University
of Bordeaux Bordeaux, France.
(3)Institut National de Recherche en Informatique et en Automatique, Bordeaux
Sud-Ouest Talence, France ; Laboratoire Bordelais de Recherche en Informatique
(LaBRI) Talence, France.

Workload estimation from electroencephalographic signals (EEG) offers a highly
sensitive tool to adapt the human-computer interaction to the user state. To
create systems that reliably work in the complexity of the real world, a
robustness against contextual changes (e.g., mood), has to be achieved. To study
the resilience of state-of-the-art EEG-based workload classification against
stress we devise a novel experimental protocol, in which we manipulated the
affective context (stressful/non-stressful) while the participant solved a task
with two workload levels. We recorded self-ratings, behavior, and physiology
from 24 participants to validate the protocol. We test the capability of
different, subject-specific workload classifiers using either frequency-domain,
time-domain, or both feature varieties to generalize across contexts. We show
that the classifiers are able to transfer between affective contexts, though
performance suffers independent of the used feature domain. However,
cross-context training is a simple and powerful remedy allowing the extraction
of features in all studied feature varieties that are more resilient to
task-unrelated variations in signal characteristics. Especially for
frequency-domain features, across-context training is leading to a performance
comparable to within-context training and testing. We discuss the significance
of the result for neurophysiology-based workload detection in particular and for
the construction of reliable passive brain-computer interfaces in general.