Review of solutions for the application of example of machine learning methods for motor imagery in correlation with brain-computer interfaces

PASZKIEL, S., ROJEK, R., LEI, N. and CASTRO, M.A. (2021). Review of solutions for the application of example of machine learning methods for motor imagery in correlation with brain-computer interfaces. Przeglad Elektrotechniczny, 97 (11), 111-116.

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Abstract

Presently, numerous public databases presenting the collected EEG signals, including the ones in the scope of Motor Imagery (MI), are available. Simultaneously, machine-learning methods, which enable effective and fast discovering of information, also in the sets of biomedical data, are constantly being developed. In this paper, a set of 30 of some of the latest scientific publications from the years 2016-2021 has been analyzed. The analysis covered, among others: public data repositories in the form of EEG signals as input data; numbers and types of the analyzed tasks in the scope of MI in the above-mentioned databases; and Deep Learning (DL) architectures.

Item Type:	Article
Uncontrolled Keywords:	Electrical & Electronic Engineering
Identification Number:	https://doi.org/10.15199/48.2021.11.20
Page Range:	111-116
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	28 Mar 2023 11:15
Last Modified:	28 Mar 2023 11:15
URI:	https://shura.shu.ac.uk/id/eprint/30671

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