T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs

TL;DR

This paper introduces T-TIME, a novel online transfer learning method for EEG-based BCIs that enables immediate, calibration-free classification by adapting classifiers in real-time as new data arrives.

Contribution

T-TIME is the first test-time adaptation approach for EEG BCIs, allowing real-time classifier updates without prior calibration, improving usability and performance.

Findings

Outperformed 20 classical and state-of-the-art transfer learning methods.

Effective in online, real-time EEG classification scenarios.

Demonstrated on three public motor imagery BCI datasets.

Abstract

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming and user-unfriendly. Transfer learning (TL) has been proposed to shorten or eliminate this calibration, but existing TL approaches mainly consider offline settings, where all unlabeled EEG trials from the new user are available. Methods: This paper proposes Test-Time Information Maximization Ensemble (T-TIME) to accommodate the most challenging online TL scenario, where unlabeled EEG data from the new user arrive in a stream, and immediate classification is performed. T-TIME initializes multiple classifiers from the aligned source data. When an unlabeled test EEG trial arrives, T-TIME first predicts its labels using ensemble learning, and then updates each classifier by conditional entropy minimization and adaptive marginal distribution regularization. Our code is publicized. Results: Extensive experiments on three public motor imagery based BCI datasets demonstrated that T-TIME outperformed about 20 classical and state-of-the-art TL approaches. Significance: To our knowledge, this is the first work on test time adaptation for calibration-free EEG-based BCIs, making plug-and-play BCIs possible.