Abstract
Deep learning based electroencephalography (EEG) signal processing methods are known to suffer from poor test-time generalization due to the changes in data distribution. This becomes a more challenging problem when privacy-preserving representation learning is of interest such as in clinical settings. To that end, we propose a multi-source learning architecture where we extract domain-invariant representations from dataset-specific private encoders. Our model utilizes a maximum-mean-discrepancy (MMD) based domain alignment approach to impose domain-invariance for encoded representations, which outperforms state-of-the-art approaches in EEG-based emotion classification. Furthermore, representations learned in our pipeline preserve domain privacy as dataset-specific private encoding alleviates the need for conventional, centralized EEG-based deep neural network training approaches with shared parameters.
Original language | English |
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Title of host publication | 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Pages | 1236-1240 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-6654-0540-9 |
DOIs | |
Publication status | Published - 2022 |
Event | 47th IEEE International Conference on Acoustics, Speech and Signal Processing: ICASSP 2022 - Virtual, Online, Singapore Duration: 22 May 2022 → 27 May 2022 |
Conference
Conference | 47th IEEE International Conference on Acoustics, Speech and Signal Processing |
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Abbreviated title | ICASSP 2022 |
Country/Territory | Singapore |
City | Virtual, Online |
Period | 22/05/22 → 27/05/22 |
ASJC Scopus subject areas
- Artificial Intelligence
- Signal Processing
- Biomedical Engineering
Fields of Expertise
- Human- & Biotechnology
- Information, Communication & Computing