Independent Vector Analysis for Feature Extraction in Motor Imagery Classification

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 16; p. 5428
• Main Authors: Moraes, Caroline Pires Alavez, dos Santos, Lucas Heck, Fantinato, Denis Gustavo, Neves, Aline, Adali, Tülay
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.08.2024; MDPI
• Subjects: Accuracy; Algorithms; autoregressive model; Brain-Computer Interfaces; brain–computer interface; Classification; Datasets; Dependence; electroencephalogram; Electroencephalography; Electroencephalography - methods; Entropy; Humans; Imagination - physiology; independent vector analysis; Methods; motor imagery; Movement - physiology; Signal Processing, Computer-Assisted; Support Vector Machine; Wavelet transforms; brain–computer interface; motor imagery; independent vector analysis; electroencephalogram; autoregressive model

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 (EE...