Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG-based BCI systems

Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG-based BCI systems

Phase synchronisation between different neural groups is considered an important source of information to understand the underlying mechanisms of brain cognition. This Letter investigated phase-synchronisation patterns from electroencephalogram (EEG) signals recorded from ten healthy participants pe...

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Bibliographic Details
Main Authors: Lorena Santamaria, Christopher James
Format: Article
Language:English
Published: Wiley 2018-06-01
Series:Healthcare Technology Letters
Subjects:
electroencephalography
cognition
brain-computer interfaces
medical signal processing
feature extraction
synchronisation
signal classification
neurophysiology
graph theory
learning (artificial intelligence)
brain connectivity metrics
motor imagery classification
EEG-based BCI systems
neural groups
brain cognition
phase-synchronisation patterns
electroencephalogram signals
motor imagery tasks
schematic emotional faces
cognitive task
occurrence states
connectivity network
classihcation algorithms
supervised learning techniques
optimal feature subset
discrimination rates
MI tasks
online classification
brain-computer interface systems
Online Access:https://digital-library.theiet.org/content/journals/10.1049/htl.2017.0049

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https://digital-library.theiet.org/content/journals/10.1049/htl.2017.0049

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