EDAPT: towards calibration-free BCIs with continual online adaptation

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: 8.75% relative improvement in balanced accuracy

• Primary Outcome: Balanced accuracy improvement from 0.80 to 0.87

• Nnt Or Sensitivity: Consistent improvement across nearly all subjects and datasets

• Confidence Interval: Not reported in abstract

Clinical Application

High - demonstrated real-time operation on consumer hardware with minimal latency Relevant for TGA-approved BCI devices and research programs in Australian rehabilitation centres and universities Patients requiring BCI systems for communication, motor control, or rehabilitation

Abstract

Objective.Brain-computer interfaces (BCIs) suffer from accuracy degradation as neural signals drift over time and vary across users, requiring frequent recalibration that limits practical deployment. Our goal is to develop a framework that eliminates the need for separate calibration phases by enabling continual, real-time model adaptation to new users and changing signal characteristics.Approach.We propose EDAPT, a task- and model-agnostic framework for continual online learning. EDAPT first establishes a robust baseline decoder through population-level pretraining (PRE) on data from multiple users. It then personalizes this model during deployment using supervised continual finetuning on a trial-by-trial basis. Due to its modular design, EDAPT can be composed with unsupervised domain adaptation (UDA) techniques to further address distribution shifts.Main results.We validate EDAPT across nine datasets, three BCI paradigms, and four deep learning architectures. EDAPT consistently improves decoding accuracy over static models for nearly all subjects and datasets, raising mean balanced accuracy from 0.80 to 0.87 on representative datasets (table3). Ablation studies confirm that the combination of PRE and online finetuning is the primary driver of this performance gain, with further improvements on some datasets when using UDA techniques. We demonstrate real-time feasibility of the framework, with adaptation latencies under 200 ms on consumer-grade hardware. Our scaling analysis further reveals that decoding accuracy is primarily determined by the total pretraining data budget, rather than its specific allocation between subjects and trials.Significance.These findings demonstrate that continual online learning is a practical and effective strategy for creating high-performance, user-adaptive BCIs. By systematically addressing the bottleneck of model recalibration, EDAPT reduces a major barrier to the widespread adoption of BCI technology and helps advance neurotechnology toward robust, user-friendly, real-world applications.

References

1. 1.Haxel, L., Kapoor, J., Ziemann, U., & Macke, J. H. (2026). EDAPT: towards calibration-free BCIs with continual online adaptation. Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/ae5689