Online and Offline Domain Adaptation for Reducing BCI Calibration Effort
This work addresses the need for less subject-specific labeled data in BCI calibration, which is crucial for improving utility in real-world applications, though it is incremental as it builds on existing adaptation methods.
The paper tackles the problem of reducing calibration effort in brain-computer interfaces (BCIs) by proposing online and offline weighted adaptation regularization algorithms, which significantly outperform other methods and reduce computational cost by about 50% through source domain selection.
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.