# Online and Offline Domain Adaptation for Reducing BCI Calibration Effort

arXiv: https://arxiv.org/abs/1702.02897 · HTML: https://mlsanity.com/paper/1702.02897/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2017.

## Built on by (most cited first, 10 of 10)

- [Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2502.09203/) (2025)
- [Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification](https://mlsanity.com/paper/2503.05349/) (2025)
- [User-wise Perturbations for User Identity Protection in EEG-Based BCIs](https://mlsanity.com/paper/2411.10469/) (2024)
- [Towards physiology-informed data augmentation for EEG-based BCIs](https://mlsanity.com/paper/2203.14392/) (2022)
- [Burn After Reading: Online Adaptation for Cross-domain Streaming Data](https://mlsanity.com/paper/2112.04345/) (2021)
- [Residual Feature Integration is Sufficient to Prevent Negative Transfer](https://mlsanity.com/paper/2505.11771/) (2025)
- [MultiDiffNet: A Multi-Objective Diffusion Framework for Generalizable Brain Decoding](https://mlsanity.com/paper/2511.18294/) (2025)
- [Online Continual Domain Adaptation for Semantic Image Segmentation Using Internal Representations](https://mlsanity.com/paper/2401.01035/) (2024)
- [EDAPT: Towards Calibration-Free BCIs with Continual Online Adaptation](https://mlsanity.com/paper/2508.10474/) (2025)
- [ERP-XTTN: Interpretable Prototype-Guided Cross-Attention for Cross-Subject ERP Classification](https://mlsanity.com/paper/2606.02939/) (2026)

## Abstract (arXiv)

Many real-world brain-computer interface (BCI) applications rely on single-trial classification of event-related potentials (ERPs) in EEG signals. However, because different subjects have different neural responses to even the same stimulus, it is very difficult to build a generic ERP classifier whose parameters fit all subjects. The classifier needs to be calibrated for each individual subject, using some labeled subject-specific data. This paper proposes both online and offline weighted adaptation regularization (wAR) algorithms to reduce this calibration effort, i.e., to minimize the amount of labeled subject-specific EEG data required in BCI calibration, and hence to increase the utility of the BCI system. We demonstrate using a visually-evoked potential oddball task and three different EEG headsets that both online and offline wAR algorithms significantly outperform several other algorithms. Moreover, through source domain selection, we can reduce their computational cost by about 50%, making them more suitable for real-time applications.