Factorization Approach for Sparse Spatio-Temporal Brain-Computer Interface

TL;DR

This paper introduces a factorization approach with dual feature extractors and orthogonal constraints to improve brain-computer interface performance on sparse EEG data, especially in high similarity tasks.

Contribution

It proposes a novel factorization method with adversarial and classification-based feature extractors, enhancing feature diversity in sparse BCI signals.

Findings

Effective separation of class-common and class-specific features

Improved feature richness under sparse conditions

Demonstrated superior performance on motor imagery dataset

Abstract

Recently, advanced technologies have unlimited potential in solving various problems with a large amount of data. However, these technologies have yet to show competitive performance in brain-computer interfaces (BCIs) which deal with brain signals. Basically, brain signals are difficult to collect in large quantities, in particular, the amount of information would be sparse in spontaneous BCIs. In addition, we conjecture that high spatial and temporal similarities between tasks increase the prediction difficulty. We define this problem as sparse condition. To solve this, a factorization approach is introduced to allow the model to obtain distinct representations from latent space. To this end, we propose two feature extractors: A class-common module is trained through adversarial learning acting as a generator; Class-specific module utilizes loss function generated from classification so that features are extracted with traditional methods. To minimize the latent space shared by the class-common and class-specific features, the model is trained under orthogonal constraint. As a result, EEG signals are factorized into two separate latent spaces. Evaluations were conducted on a single-arm motor imagery dataset. From the results, we demonstrated that factorizing the EEG signal allows the model to extract rich and decisive features under sparse condition.