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.

翻译：基于脑电图（EEG）的脑机接口（BCI）在部署中面临显著挑战，包括被试间变异性、信号非平稳性以及计算资源限制。测试时自适应（TTA）方法能够在不依赖每次使用的校准会话的情况下，缓解在线数据流中的分布偏移，但现有的TTA方法严重依赖需要反向传播来更新模型参数的显式损失目标，这带来了计算开销、隐私风险以及对噪声数据流的敏感性。本文提出免反向传播变换（BFT），一种用于脑电解码的TTA方法，以消除上述问题。BFT对每个测试试次应用多种基于知识引导增强或近似贝叶斯推断的样本级变换，为单个测试样本生成多个预测分数。一个学习排序模块增强了这些预测的权重，从而在理论依据下实现鲁棒的聚合以抑制推理过程中的不确定性。在五个脑电数据集上针对运动想象分类和驾驶员睡意回归任务进行的大量实验，证明了BFT的有效性、通用性、鲁棒性和高效性。这项研究使得在资源受限设备上实现轻量级即插即用脑机接口成为可能，拓宽了基于脑电的脑机接口解码算法在现实世界中的部署。