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.
Originalsprache | englisch |
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Titel | 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Seiten | 1236-1240 |
Seitenumfang | 5 |
ISBN (elektronisch) | 978-1-6654-0540-9 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2022 |
Veranstaltung | 47th IEEE International Conference on Acoustics, Speech and Signal Processing: ICASSP 2022 - Virtual, Online, Singapur Dauer: 22 Mai 2022 → 27 Mai 2022 |
Konferenz
Konferenz | 47th IEEE International Conference on Acoustics, Speech and Signal Processing |
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Kurztitel | ICASSP 2022 |
Land/Gebiet | Singapur |
Ort | Virtual, Online |
Zeitraum | 22/05/22 → 27/05/22 |
ASJC Scopus subject areas
- Artificial intelligence
- Signalverarbeitung
- Biomedizintechnik
Fields of Expertise
- Human- & Biotechnology
- Information, Communication & Computing