Recruiting neural field theory for data augmentation in a motor imagery brain–computer interface

Recruiting neural field theory for data augmentation in a motor imagery brain–computer interface

We introduce a novel approach to training data augmentation in brain–computer interfaces (BCIs) using neural field theory (NFT) applied to EEG data from motor imagery tasks. BCIs often suffer from limited accuracy due to a limited amount of training data. To address this, we leveraged a corticothala...

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Bibliographic Details
Main Authors: Daniel Polyakov, Peter A. Robinson, Eli J. Muller, Oren Shriki
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-04-01
Series:Frontiers in Robotics and AI
Subjects:
brain-computer interface (BCI)
EEG
motor imagery
data augmentation
neural field theory
common spatial pattern (CSP)
Online Access:https://www.frontiersin.org/articles/10.3389/frobt.2024.1362735/full

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