Convolutional and Recurrent Neural Networks for Physical Action Forecasting by Brain-Computer Interface

Kostiukevych, Kostiantyn; Stirenko, Sergii; Gordienko, Nikita; Rokovyi, Oleksandr; Alienin, Oleg; Gordienko, Yuri

doi:10.1109/idaacs53288.2021.9660880

2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applicat 2021

DOI: 10.1109/idaacs53288.2021.9660880

|View full text |Cite

Convolutional and Recurrent Neural Networks for Physical Action Forecasting by Brain-Computer Interface

Kostiantyn Kostiukevych

Sergii Stirenko

Nikita Gordienko

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

Publication Types

Select...

Book2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Convolutional and Recurrent Neural Networks for Physical Action Forecasting by Brain-Computer Interface

Cited by 2 publications

References 17 publications

Hybrid Convolutional, Recurrent and Attention-Based Architectures of Deep Neural Networks for Classification of Human-Computer Interaction by Electroencephalography

Hybrid Convolutional, Recurrent and Attention-Based Architectures of Deep Neural Networks for Classification of Human-Computer Interaction by Electroencephalography

Implementation of Personalized Medicine by Artificial Intelligence Platform

Contact Info

Product

Resources

About