Transfer Learning Enhanced Common Spatial Pattern Filtering for Brain Computer Interfaces (BCIs): Overview and a New Approach

TL;DR

This paper reviews transfer learning methods for CSP in BCIs and introduces a new approach that improves motor imagery classification, especially with limited calibration data.

Contribution

The paper presents a novel transfer learning enhanced CSP method that outperforms existing approaches in low-data scenarios.

Findings

Proposed method achieves superior accuracy with few calibration samples.

Transfer learning effectively reduces calibration time in BCIs.

Experimental results validate the approach's robustness and efficiency.

Abstract

The electroencephalogram (EEG) is the most widely used input for brain computer interfaces (BCIs), and common spatial pattern (CSP) is frequently used to spatially filter it to increase its signal-to-noise ratio. However, CSP is a supervised filter, which needs some subject-specific calibration data to design. This is time-consuming and not user-friendly. A promising approach for shortening or even completely eliminating this calibration session is transfer learning, which leverages relevant data or knowledge from other subjects or tasks. This paper reviews three existing approaches for incorporating transfer learning into CSP, and also proposes a new transfer learning enhanced CSP approach. Experiments on motor imagery classification demonstrate their effectiveness. Particularly, our proposed approach achieves the best performance when the number of target domain calibration samples is small.