Subject-Independent Brain-Computer Interfaces with Open-Set Subject Recognition

TL;DR

This paper proposes a novel open-set subject recognition framework for EEG-based BCIs that uses style information encoding to improve generalization across unseen subjects, reducing calibration needs.

Contribution

It introduces a style information encoder as an auxiliary task for open-set recognition, enhancing BCI generalization to new subjects without calibration.

Findings

Style information encoding improves cross-subject BCI performance

Open-set recognition methods enhance generalization to unseen subjects

The proposed framework outperforms baseline models in experiments

Abstract

A brain-computer interface (BCI) can't be effectively used since electroencephalography (EEG) varies between and within subjects. BCI systems require calibration steps to adjust the model to subject-specific data. It is widely acknowledged that this is a major obstacle to the development of BCIs. To address this issue, previous studies have trained a generalized model by removing the subjects' information. In contrast, in this work, we introduce a style information encoder as an auxiliary task that classifies various source domains and recognizes open-set domains. Open-set recognition method was used as an auxiliary task to learn subject-related style information from the source subjects, while at the same time helping the shared feature extractor map features in an unseen target. This paper compares various OSR methods within an open-set subject recognition (OSSR) framework. As a result of our experiments, we found that the OSSR auxiliary network that encodes domain information improves generalization performance.