Deep Transfer Learning for EEG-based Brain Computer Interface

TL;DR

This paper introduces a deep transfer learning method for EEG-based brain-computer interfaces that leverages multimodal data and adversarial training to improve classification robustness and accuracy.

Contribution

It proposes a novel deep transfer learning framework using EEG optical flow and adversarial networks to enhance EEG classification performance.

Findings

Improved robustness and accuracy in EEG classification tasks.

Effective transfer of knowledge across different EEG datasets.

Preservation of multimodal EEG information through EEG optical flow.

Abstract

The electroencephalography classifier is the most important component of brain-computer interface based systems. There are two major problems hindering the improvement of it. First, traditional methods do not fully exploit multimodal information. Second, large-scale annotated EEG datasets are almost impossible to acquire because biological data acquisition is challenging and quality annotation is costly. Herein, we propose a novel deep transfer learning approach to solve these two problems. First, we model cognitive events based on EEG data by characterizing the data using EEG optical flow, which is designed to preserve multimodal EEG information in a uniform representation. Second, we design a deep transfer learning framework which is suitable for transferring knowledge by joint training, which contains a adversarial network and a special loss function. The experiments demonstrate that our approach, when applied to EEG classification tasks, has many advantages, such as robustness and accuracy.