Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces

TL;DR

This paper introduces a prototype-based domain generalization framework for EEG-based brain-computer interfaces that improves subject-independent classification by leveraging open-set recognition to learn style-invariant features, reducing calibration needs.

Contribution

It proposes a novel EEG classification framework utilizing open-set recognition to enhance domain generalization across unseen subjects in BCI systems.

Findings

Improved classification accuracy on unseen subjects.

Enhanced generalization performance with domain information.

Reduced calibration time for BCI applications.

Abstract

Brain-computer interface (BCI) is challenging to use in practice due to the inter/intra-subject variability of electroencephalography (EEG). The BCI system, in general, necessitates a calibration technique to obtain subject/session-specific data in order to tune the model each time the system is utilized. This issue is acknowledged as a key hindrance to BCI, and a new strategy based on domain generalization has recently evolved to address it. In light of this, we've concentrated on developing an EEG classification framework that can be applied directly to data from unknown domains (i.e. subjects), using only data acquired from separate subjects previously. For this purpose, in this paper, we proposed a framework that employs the open-set recognition technique as an auxiliary task to learn subject-specific style features from the source dataset while helping the shared feature extractor with mapping the features of the unseen target dataset as a new unseen domain. Our aim is to impose cross-instance style in-variance in the same domain and reduce the open space risk on the potential unseen subject in order to improve the generalization ability of the shared feature extractor. Our experiments showed that using the domain information as an auxiliary network increases the generalization performance.