Using Multiple Decomposition Methods and Cluster Analysis to Find and Categorize Typical Patterns of EEG Activity in Motor Imagery Brain–Computer Interface Experiments

Using Multiple Decomposition Methods and Cluster Analysis to Find and Categorize Typical Patterns of EEG Activity in Motor Imagery Brain–Computer Interface Experiments

In this study, the sources of EEG activity in motor imagery brain–computer interface (BCI) control experiments were investigated. Sixteen linear decomposition methods for EEG source separation were compared according to different criteria. The criteria were mutual information reduction between the s...

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Bibliographic Details
Main Authors: Alexander Frolov, Pavel Bobrov, Elena Biryukova, Mikhail Isaev, Yaroslav Kerechanin, Dmitry Bobrov, Alexander Lekin
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-07-01
Series:Frontiers in Robotics and AI
Subjects:
brain–computer interface
motor imagery
blind source separation
independent component analysis
common spatial patterns
cluster analysis
Online Access:https://www.frontiersin.org/article/10.3389/frobt.2020.00088/full

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