On the effects of data normalization for domain adaptation on EEG data

In Machine Learning (ML), a well-known problem is the Dataset Shift problem where the data in the training and test sets can follow different probability distributions, leading ML systems toward poor generalization performances. This problem is intensely felt in Brain-Computer Interfaces (BCIs), where bio-signals as Electroencephalographic (EEG) are often used. Indeed, EEG signals are highly non-stationary both over time and between different subjects. To overcome this problem, several solutions are based on transfer learning approaches such as Domain Adaption (DA). In several cases, however, the actual causes of the improvements remain ambiguous. This paper focuses on the impact of data normalization strategies applied together with DA methods. In particular, using SEED, DEAP, and BCI Competition IV 2a EEG datasets, we experimentally evaluated the impact of different normalization strategies applied with and without several well-known DA methods. It results that the choice of the normalization strategy plays a key role on the classifier performances in DA scenarios, and, often, the use of only an appropriate normalization schema outperforms the DA technique. For SEED and BCI Competition IV 2a, a proper normalization strategy alone in a cross-subject context allows to reach accuracy of 81.52±7.26% and 68.52±11.35%, respectively. In a cross-session context, the accuracy of 86.56±8.15% and 67.82±12.48% for SEED and BCI Competition can be reached, respectively. For DEAP, the best cross-subject performance achieved using only normalization was 39.33±14.08%. All these results are comparable with the performance obtained by several well-known DA strategies.