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Independent Vector Analysis for Feature Extraction in Motor Imagery Classification. | LitMetric

Independent Vector Analysis for Feature Extraction in Motor Imagery Classification.

Sensors (Basel)

Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County (UMBC), Baltimore, MD 21250, USA.

Published: August 2024

AI Article Synopsis

  • Independent Vector Analysis (IVA) extends Independent Component Analysis (ICA) to analyze multiple datasets simultaneously, utilizing the statistical dependencies between them through mutual information.
  • This study focuses on improving the classification of motor imagery movements from EEG signals in brain-computer interfaces (BCIs) by proposing a novel feature extraction method using multiple datasets with IVA.
  • The proposed approach demonstrated effective performance, achieving an average accuracy of 86.7% when using various classifiers, including support vector machines, K-nearest neighbors, and deep learning models.

Article Abstract

Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information. In the context of motor imagery classification based on electroencephalogram (EEG) signals for the brain-computer interface (BCI), several methods have been proposed to extract features efficiently, mainly based on common spatial patterns, filter banks, and deep learning. However, most methods use only one dataset at a time, which may not be sufficient for dealing with a multi-source retrieving problem in certain scenarios. From this perspective, this paper proposes an original approach for feature extraction through multiple datasets based on IVA to improve the classification of EEG-based motor imagery movements. The IVA components were used as features to classify imagined movements using consolidated classifiers (support vector machines and K-nearest neighbors) and deep classifiers (EEGNet and EEGInception). The results show an interesting performance concerning the clustering of MI-based BCI patients, and the proposed method reached an average accuracy of 86.7%.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11359939PMC
http://dx.doi.org/10.3390/s24165428DOI Listing

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