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On the effects of data normalization for domain adaptation on EEG data by Andrea Apicella; Francesco Isgrò; Andrea Pollastro; Roberto Prevete is a Computer Science article available to read on EtoBox.

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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 Compe

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Author

Andrea Apicella; Francesco Isgrò; Andrea Pollastro; Roberto Prevete

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)