  Cognitive load classification is the task of automatically determining an
individual's utilization of working memory resources during performance of a
task based on physiologic measures such as electroencephalography (EEG). In
this paper, we follow a cross-disciplinary approach, where tools and
methodologies from speech processing are used to tackle this problem. The
corpus we use was released publicly in 2021 as part of the first passive
brain-computer interface competition on cross-session workload estimation. We
present our approach which used i-vector-based neural network classifiers to
accomplish inter-subject cross-session EEG transfer learning, achieving 18%
relative improvement over equivalent subject-dependent models. We also report
experiments showing how our subject-independent models perform competitively on
held-out subjects and improve with additional subject data, suggesting that
subject-dependent training is not required for effective cognitive load
determination.
