Multimodal EEG and Eye Tracking Feature Fusion Approaches for Attention Classification in Hybrid BCIs

Multimodal EEG and Eye Tracking Feature Fusion Approaches for Attention Classification in Hybrid BCIs

Often, various modalities capture distinct aspects of particular mental states or activities. While machine learning algorithms can reliably predict numerous aspects of human cognition and behavior using a single modality, they can benefit from the combination of multiple modalities. This is why hyb...

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Bibliographic Details
Main Authors: Lisa-Marie Vortmann, Simon Ceh, Felix Putze
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-03-01
Series:Frontiers in Computer Science
Subjects:
feature fusion
convolutional neural networks
attention
eye tracking
EEG
Markov Transition Fields
Online Access:https://www.frontiersin.org/articles/10.3389/fcomp.2022.780580/full

Internet

https://www.frontiersin.org/articles/10.3389/fcomp.2022.780580/full

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