EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning

TL;DR

This paper introduces a novel EEG-based method for classifying cognitive load by leveraging transfer learning from emotion-related datasets using a transformer architecture with self-supervised pre-training.

Contribution

It presents a new approach combining masked autoencoding and transfer learning for cognitive load classification from EEG data, outperforming traditional supervised methods.

Findings

Proposed method achieves superior accuracy over conventional models.

Self-supervised pre-training enhances cross-domain transfer learning.

Detailed ablation studies validate the effectiveness of each component.

Abstract

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.