# Online and Offline Domain Adaptation for Reducing BCI Calibration Effort

## TL;DR

This paper introduces online and offline weighted adaptation regularization algorithms to reduce calibration effort in EEG-based BCI systems, demonstrating significant performance improvements and computational efficiency for real-time applications.

## Contribution

The paper presents novel online and offline wAR algorithms that effectively minimize the need for labeled data in BCI calibration, enhancing practicality and efficiency.

## Key findings

- Both algorithms outperform existing methods in classification accuracy.
- Source domain selection reduces computational cost by about 50%.
- Algorithms are effective across different EEG headsets.

## Abstract

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.