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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Dalin Zhang

The University of New South Wales


Lina Yao

The University of New South Wales


Xiang Zhang

The University of New South Wales


Sen Wang

Griffith University


Weitong Chen

The University of Queensland


Robert Boots

Royal Brisbane and Women's Hospital; The University of Queensland


Boualem Benatallah

The University of New South Wales


DOI:

10.1609/aaai.v32i1.11496


Abstract:

Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG) based BCIs are promising solutions due to their convenient and portable instruments. Despite the extensive research of EEG in recent years, it is still challenging to interpret EEG signals effectively due to the massive noises in EEG signals (e.g., low signal-noise ratio and incomplete EEG signals), and difficulties in capturing the inconspicuous relationships between EEG signals and certain brain activities. Most existing works either only consider EEG as chain-like sequences neglecting complex dependencies between adjacent signals or requiring pre-processing such as transforming EEG waves into images. In this paper, we introduce both cascade and parallel convolutional recurrent neural network models for precisely identifying human intended movements and instructions effectively learning the compositional spatio-temporal representations of raw EEG streams. Extensive experiments on a large scale movement intention EEG dataset (108 subjects,3,145,160 EEG records) have demonstrated that both models achieve high accuracy near 98.3% and outperform a set of baseline methods and most recent deep learning based EEG recognition models, yielding a significant accuracy increase of 18% in the cross-subject validation scenario. The developed models are further evaluated with a real-world BCI and achieve a recognition accuracy of 93% over five instruction intentions. This suggests the proposed models are able to generalize over different kinds of intentions and BCI systems.

Topics: AAAI

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HOW TO CITE:

Dalin Zhang||Lina Yao||Xiang Zhang||Sen Wang||Weitong Chen||Robert Boots||Boualem Benatallah Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Dalin Zhang||Lina Yao||Xiang Zhang||Sen Wang||Weitong Chen||Robert Boots||Boualem Benatallah Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface AAAI 2018, .

Dalin Zhang||Lina Yao||Xiang Zhang||Sen Wang||Weitong Chen||Robert Boots||Boualem Benatallah (2018). Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Dalin Zhang||Lina Yao||Xiang Zhang||Sen Wang||Weitong Chen||Robert Boots||Boualem Benatallah. Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Dalin Zhang||Lina Yao||Xiang Zhang||Sen Wang||Weitong Chen||Robert Boots||Boualem Benatallah. 2018. Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Dalin Zhang||Lina Yao||Xiang Zhang||Sen Wang||Weitong Chen||Robert Boots||Boualem Benatallah. (2018) "Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Dalin Zhang||Lina Yao||Xiang Zhang||Sen Wang||Weitong Chen||Robert Boots||Boualem Benatallah, "Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface", AAAI, p., 2018.

Dalin Zhang||Lina Yao||Xiang Zhang||Sen Wang||Weitong Chen||Robert Boots||Boualem Benatallah. "Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Dalin Zhang||Lina Yao||Xiang Zhang||Sen Wang||Weitong Chen||Robert Boots||Boualem Benatallah. "Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Dalin Zhang||Lina Yao||Xiang Zhang||Sen Wang||Weitong Chen||Robert Boots||Boualem Benatallah. Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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