Backpropagation-Free Test-Time Adaptation for Lightweight EEG-Based Brain-Computer Interfaces

2026-01-12

AI Summary

This work addresses the performance degradation of EEG-based brain–computer interfaces in real-world deployment caused by inter-subject variability, signal non-stationarity, and limited computational resources. To this end, the authors propose BFT, the first test-time adaptation method that operates without backpropagation. BFT integrates knowledge-guided data augmentation with approximate Bayesian inference to generate diverse predictions, which are then aggregated via a learned ranking module. This approach effectively mitigates prediction uncertainty while avoiding the computational overhead, privacy concerns, and noise sensitivity associated with backpropagation. Extensive experiments across five EEG datasets demonstrate that BFT significantly outperforms existing test-time adaptation methods in both motor imagery classification and driver fatigue regression tasks, offering high efficiency, robustness, and lightweight plug-and-play deployability.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, signal non-stationarity, and computational constraints. While test-time adaptation (TTA) mitigates distribution shifts under online data streams without per-use calibration sessions, existing TTA approaches heavily rely on explicitly defined loss objectives that require backpropagation for updating model parameters, which incurs computational overhead, privacy risks, and sensitivity to noisy data streams. This paper proposes Backpropagation-Free Transformations (BFT), a TTA approach for EEG decoding that eliminates such issues. BFT applies multiple sample-wise transformations of knowledge-guided augmentations or approximate Bayesian inference to each test trial, generating multiple prediction scores for a single test sample. A learning-to-rank module enhances the weighting of these predictions, enabling robust aggregation for uncertainty suppression during inference under theoretical justifications. Extensive experiments on five EEG datasets of motor imagery classification and driver drowsiness regression tasks demonstrate the effectiveness, versatility, robustness, and efficiency of BFT. This research enables lightweight plug-and-play BCIs on resource-constrained devices, broadening the real-world deployment of decoding algorithms for EEG-based BCI.

Problem

• EEG-based BCI
• test-time adaptation
• backpropagation
• inter-subject variability
• signal non-stationarity

Innovation

• Backpropagation-Free
• Test-Time Adaptation
• EEG-based BCI
• Knowledge-Guided Augmentation
• Learning-to-Rank