Imputing Missing Values in EEG with Multivariate Autoregressive Models

Wearable measurement for electroencephalogram (EEG) is expected to enable brain-computer interfaces, biomedical engineering, and neuroscience studies in real environments. When wearable devices are in practical use, only the user (subject) can take care of measurement, unlike laboratory- oriented experiments, where experimenters are always with the subject. As a result, measurement troubles such as artifact contamination or electrode impairment cannot be easily corrected, and EEG recordings will become incomplete, including many missing values. If the missing values are imputed (interpolated) and complete data without missing entries are available, we can employ existing signal analysis techniques that assume compete data. In this paper, we propose an EEG signal imputation method based on multivariate autoregressive (MAR) modeling and its iterative estimation and simulation, inspired by the multiple imputation procedure. We evaluated the proposed method with real data with artificial missing entries. Experimental results show that the proposed method outperforms popular baseline interpolation methods. Our iterative scheme is simple yet effective, and can be the foundation for many extensions.