# Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization

## TL;DR

This paper introduces an active weighted adaptation regularization method that reduces calibration effort when switching EEG headsets by leveraging prior data and active learning, improving accuracy with fewer new samples.

## Contribution

It proposes a novel AwAR approach combining weighted adaptation regularization and active learning for efficient EEG headset transfer learning.

## Key findings

- Significantly improves classification accuracy with fewer labeled samples
- Reduces calibration time for new EEG headsets
- Enhances transfer learning across different hardware systems

## Abstract

Electroencephalography (EEG) headsets are the most commonly used sensing devices for Brain-Computer Interface. In real-world applications, there are advantages to extrapolating data from one user session to another. However, these advantages are limited if the data arise from different hardware systems, which often vary between application spaces. Currently, this creates a need to recalibrate classifiers, which negatively affects people's interest in using such systems. In this paper, we employ active weighted adaptation regularization (AwAR), which integrates weighted adaptation regularization (wAR) and active learning, to expedite the calibration process. wAR makes use of labeled data from the previous headset and handles class-imbalance, and active learning selects the most informative samples from the new headset to label. Experiments on single-trial event-related potential classification show that AwAR can significantly increase the classification accuracy, given the same number of labeled samples from the new headset. In other words, AwAR can effectively reduce the number of labeled samples required from the new headset, given a desired classification accuracy, suggesting value in collating data for use in wide scale transfer-learning applications.