TASER: Trial-Aware Shift Estimation and Re-Centering for Causal EEG Test-Time Adaptation in Brain-Computer Interfaces

Electroencephalography (EEG) decoders are often adapted one window at a time. Yet overlapping windows are repeated views of the same trial, so one atypical trial can move the adapter many times. We introduce TASER, a causal and label-free method that adapts once per brain–computer interface (BCI) acquisition unit: a trial for motor imagery and SSVEP, or a stimulus group for ERP/P300. A frozen encoder maps the signal into a latent space, where source prototypes define the class geometry. TASER records the current prediction before adaptation, pools the associated embeddings into one observation, and estimates a shared shift between source and target representations. Window redundancy and within-trial dispersion determine the update strength. The shift then re-centers the prototypes for the next trial, without target labels, pseudo-labels, replay, or backpropagation. Across 13 datasets, 153 participants, and four encoder seeds, TASER reaches $70.00$ dataset-balanced accuracy, compared with $68.61$ for the closest baseline. It ranks first on eight datasets and second on three, with a median online latency of $1.25$ ms per decision. Updating after every window instead of once per trial reduces accuracy by $5.29$ points; removing the shift costs $5.53$ points. The ranking persists across three temporal resolutions. Therefore, these results indicate that respecting the BCI acquisition unit can make causal EEG adaptation both more accurate and less expensive.