Motor imagery EEG signal classification with a multivariate time series approach.

ABSTRACT:

Background

Electroencephalogram (EEG) signals record electrical activity on the scalp. Measured signals, especially EEG motor imagery signals, are often inconsistent or distorted, which compromises their classification accuracy. Achieving a reliable classification of motor imagery EEG signals opens the door to possibilities such as the assessment of consciousness, brain computer interfaces or diagnostic tools. We seek a method that works with a reduced number of variables, in order to avoid overfitting and to improve interpretability. This work aims to enhance EEG signal classification accuracy by using methods based on time series analysis. Previous work on this line, usually took a univariate approach, thus losing the possibility to take advantage of the correlation information e

SUBMITTER: Velasco I 

PROVIDER: S-EPMC10035287 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature