Privacy-Preserving EEG Signal Analysis with Electrode Attention for Depression Diagnosis: Joint FHE and CNN Approach-Reference-Cited by-同舟云学术

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Privacy-Preserving EEG Signal Analysis with Electrode Attention for Depression Diagnosis: Joint FHE and CNN Approach

Published:2023-12-04 Issue: Volume:32 Page:4265-4270
ISSN:
Container-title:GLOBECOM 2023 - 2023 IEEE Global Communications Conference
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Author:

Dong Huanze¹,Wu Jun¹,Bashir Ali Kashif²,Pan Qianqian²,Omar Marwan³,Al-Dulaimi Anwer⁴

Affiliation:

1. Graduate School of Information, Production and Systems, Waseda University,Japan

2. Manchester Metropolitan University,Department of Computing and Mathematics,Manchester,U.K.

3. Illinois Institute of Technology,Information Technology and Management,USA

4. 5G Center of Excellence, EXFO,Canada

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10436708/10436716/10436783.pdf?arnumber=10436783

Reference19 articles.

1. Feature-level fusion approaches based on multimodal EEG data for depression recognition

2. Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features

3. Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals

4. DepHNN: A novel hybrid neural network for electroencephalogram (EEG)-based screening of depression

5. Efficacy of novel attention-based gated recurrent units transformer for depression detection using electroencephalogram signals

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