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

Huanze Dong, Jun Wu*, Ali Kashif Bashir, Qianqian Pan*, Marwan Omar, Anwer Al-Dulaimi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Artificial intelligence has been utilized to analyze patients' electroencephalograms (EEG) to diagnose depression. However, attackers can deduce patients' privacy after analyzing patients' EEG time series. Therefore, researchers propose to operate ciphertext calculation in depression diagnosis models based on homomorphic encryption. Nevertheless, homomorphic encryption requires consistent private keys during training, which could result in other participants decrypting the ci-phertexts. Additionally, existing EEG-based models neglect the relationship among electrode positions during EEG acquisition. To address these issues, we propose a novel training strategy for the depression diagnosis model based on fully homomorphic en-cryption (FHE) and electrode topology. Specifically, we establish a training strategy that prioritizes the privacy of patients' EEG data without compromising the cost-effectiveness of the diagnosis model. Furthermore, we incorporate the attention mechanism of electrode topology into our model to improve its performance and verify the relationship among topology locations. Our proposed model outperforms the original convolution neural network model, achieving higher accuracy in depression diagnosis and identifying virtual electrode channels for the first time.

Original languageEnglish
Title of host publicationGLOBECOM 2023 - 2023 IEEE Global Communications Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4265-4270
Number of pages6
ISBN (Electronic)9798350310900
DOIs
Publication statusPublished - 2023
Event2023 IEEE Global Communications Conference, GLOBECOM 2023 - Kuala Lumpur, Malaysia
Duration: 2023 Dec 42023 Dec 8

Publication series

NameProceedings - IEEE Global Communications Conference, GLOBECOM
ISSN (Print)2334-0983
ISSN (Electronic)2576-6813

Conference

Conference2023 IEEE Global Communications Conference, GLOBECOM 2023
Country/TerritoryMalaysia
CityKuala Lumpur
Period23/12/423/12/8

Keywords

  • Artificial Intelligence
  • Depression Diagnosis
  • Electroencephalogram
  • Fully Homomorphic En-cryption
  • Privacy Preservation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing

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