The ageing process may lead to cognitive and physical impairments, which may affect elderly everyday life. In recent years, the use of Brain Computer Interfaces (BCIs) based on Electroencephalography (EEG) has revealed to be particularly effective to promote and enhance rehabilitation procedures, especially by exploiting motor imagery experimental paradigms. Moreover, BCIs seem to increase patients’ engagement and have proved to be reliable tools for elderly overall wellness improvement. However, EEG signals usually present a low signal-to-noise ratio and can be recorded for a limited time. Thus, irrelevant information and faulty or insufficient samples could affect the BCI performance. Introducing a methodology that allows the extraction of informative components from the EEG signal while maintaining its intrinsic characteristics, may provide a solution to the described issues: noisy data may be avoided by having only relevant components and combining relevant components may represent a good strategy to substitute or augment the data without requiring long or repeated EEG recordings. To this end, in this work the EEG signal decomposition by means of multivariate empirical mode decomposition is proposed to obtain its oscillatory modes, called Intrinsic Mode Functions (IMFs). Subsequently, a novel procedure for relevant IMF selection based on the IMF time-frequency representation and entropy is provided. After having verified the reliability of the EEG signal reconstruction with the relevant IMFs only, the relevant IMFs are combined to produce new artificial data and provide new samples to use for BCI training.

Saibene, A., Gasparini, F., Sole-Casals, J. (2022). EEG-Based BCIs for Elderly Rehabilitation Enhancement Exploiting Artificial Data. Intervento presentato a: 20th International Conference of the Italian Association for Artificial Intelligence, AIxIA 2021 - 1 December 2021 through 3 December 2021, Virtual, Online [10.1007/978-3-031-08421-8_25].

EEG-Based BCIs for Elderly Rehabilitation Enhancement Exploiting Artificial Data

Saibene A.
;
Gasparini F.
;
2022

Abstract

The ageing process may lead to cognitive and physical impairments, which may affect elderly everyday life. In recent years, the use of Brain Computer Interfaces (BCIs) based on Electroencephalography (EEG) has revealed to be particularly effective to promote and enhance rehabilitation procedures, especially by exploiting motor imagery experimental paradigms. Moreover, BCIs seem to increase patients’ engagement and have proved to be reliable tools for elderly overall wellness improvement. However, EEG signals usually present a low signal-to-noise ratio and can be recorded for a limited time. Thus, irrelevant information and faulty or insufficient samples could affect the BCI performance. Introducing a methodology that allows the extraction of informative components from the EEG signal while maintaining its intrinsic characteristics, may provide a solution to the described issues: noisy data may be avoided by having only relevant components and combining relevant components may represent a good strategy to substitute or augment the data without requiring long or repeated EEG recordings. To this end, in this work the EEG signal decomposition by means of multivariate empirical mode decomposition is proposed to obtain its oscillatory modes, called Intrinsic Mode Functions (IMFs). Subsequently, a novel procedure for relevant IMF selection based on the IMF time-frequency representation and entropy is provided. After having verified the reliability of the EEG signal reconstruction with the relevant IMFs only, the relevant IMFs are combined to produce new artificial data and provide new samples to use for BCI training.
Si
paper
Scientifica
BCI; Data augmentation; EEG; Entropy; MEMD;
English
20th International Conference of the Italian Association for Artificial Intelligence, AIxIA 2021 - 1 December 2021 through 3 December 2021
978-3-031-08420-1
Saibene, A., Gasparini, F., Sole-Casals, J. (2022). EEG-Based BCIs for Elderly Rehabilitation Enhancement Exploiting Artificial Data. Intervento presentato a: 20th International Conference of the Italian Association for Artificial Intelligence, AIxIA 2021 - 1 December 2021 through 3 December 2021, Virtual, Online [10.1007/978-3-031-08421-8_25].
Saibene, A; Gasparini, F; Sole-Casals, J
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/390526
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