EEG-based Cognitive Load Classification using Feature Masked
  Autoencoding and Emotion Transfer Learning
Dustin Pulver Prithila Angkan Paul Hungler Ali Etemad
Cognitive load, the amount of mental effort required for task completion,
plays an important role in performance and decision-making outcomes, making its
classification and analysis essential in various sensitive domains. In this
paper, we present a new solution for the classification of cognitive load using
electroencephalogram (EEG). Our model uses a transformer architecture employing
transfer learning between emotions and cognitive load. We pre-train our model
using self-supervised masked autoencoding on emotion-related EEG datasets and
use transfer learning with both frozen weights and fine-tuning to perform
downstream cognitive load classification. To evaluate our method, we carry out
a series of experiments utilizing two publicly available EEG-based emotion
datasets, namely SEED and SEED-IV, for pre-training, while we use the CL-Drive
dataset for downstream cognitive load classification. The results of our
experiments show that our proposed approach achieves strong results and
outperforms conventional single-stage fully supervised learning. Moreover, we
perform detailed ablation and sensitivity studies to evaluate the impact of
different aspects of our proposed solution. This research contributes to the
growing body of literature in affective computing with a focus on cognitive
load, and opens up new avenues for future research in the field of cross-domain
transfer learning using self-supervised pre-training.