Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

TL;DR

This paper introduces a deep recurrent-convolutional neural network that transforms EEG data into multi-spectral images to improve cognitive load classification by capturing spatial, spectral, and temporal features more effectively.

Contribution

The novel approach converts EEG signals into multi-spectral images and employs a deep recurrent-convolutional network inspired by video classification to learn invariant representations.

Findings

Significant accuracy improvements over state-of-the-art methods

Effective preservation of spatial, spectral, and temporal EEG features

Robustness to inter- and intra-subject variability

Abstract

One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as well as to inherent noise associated with such data. Herein, we propose a novel approach for learning such representations from multi-channel EEG time-series, and demonstrate its advantages in the context of mental load classification task. First, we transform EEG activities into a sequence of topology-preserving multi-spectral images, as opposed to standard EEG analysis techniques that ignore such spatial information. Next, we train a deep recurrent-convolutional network inspired by state-of-the-art video classification to learn robust representations from the sequence of images. The proposed approach is designed to preserve the spatial, spectral, and temporal structure of EEG which leads to finding features that are less sensitive to variations and distortions within each dimension. Empirical evaluation on the cognitive load classification task demonstrated significant improvements in classification accuracy over current state-of-the-art approaches in this field.