Brain Hemorrhage: When Brainwaves Leak Sensitive Medical Conditions and Personal Information

Neupane, Ajaya; Satvat, Kiavash; Hosseini, Mahshid; Saxena, Nitesh

doi:10.1109/pst47121.2019.8949062

Cited by 6 publications

(1 citation statement)

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“…student network, we first select the most confident samples based on entropy and then use these samples, equipped with pseudo-labels, to train the edRVFL. We also introduce a self-distillation module to enhance the model's robustness and9.1.As mentioned in Chapter 2, UDA methods require the source domain data for model training which can raise two issues: (1) privacy concerns[228,229]; (2) low calibration speed. Recent research[230] showed that EEG signals contain various kinds of private information, e.g., personal preference, and physical and mental states.…”

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confidence: 99%

Domain adaptation in biomedical engineering: unsupervised, source-free, and black box approaches

Yuan

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The remarkable advancements in deep learning methodologies over recent years can be attributed to the availability of large, high-quality labeled datasets, intricate network structures, and the swift progression of hardware technologies. However, in the biomedical engineering, the scarcity of data often poses a significant challenge in training a well-generalized model. This thesis delves into the potential of transfer learning methods as a viable solution to this data scarcity issue. The research provides a comprehensive exploration of Transfer Learning (TL), Unsupervised Domain Adaptation (UDA), Source-Free Domain Adaptation (SFDA), and Black-Box Domain Adaptation (BBDA) within the realm of biomedical engineering. It tackles the issue of data dependency in deep learning, with a particular emphasis on the application of TL in fine-tuning neural networks for specialized tasks. The study further investigates UDA techniques, specifically in the context of cross-dataset EEG classification tasks, and proposes entropy minimization-based methodologies to alleviate domain shifts. The thesis also scrutinizes SFDA in the context of medical imaging segmentation and abnormal ECG classification, introducing three innovative methodologies to overcome inherent challenges. Expanding on SFDA, the thesis evaluates the efficiency and accuracy of BBDA in real-time applications for cross-subject EEG driver drowsiness detection. This research contributes novel insights into handling data dependency, domain shifts, and privacy concerns in biomedical engineering, advocating for a nuanced approach where the scope of achievements is judiciously balanced against the challenges posed by scarce data.

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Domain adaptation in biomedical engineering: unsupervised, source-free, and black box approaches

Yuan

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A Step Closer to Becoming Symbiotic with AI through EEG: A Review of Recent BCI Technology

Dabas

Saxena

Nordlund

et al. 2020

2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)

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Position: On Potential Malware & New Attack Vectors for Internet-of-Brains (IoB)

Lahtinen,

Costin,

Suarez-Tangil

2024

2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)

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Brain Hemorrhage: When Brainwaves Leak Sensitive Medical Conditions and Personal Information

Cited by 6 publications

References 43 publications

Domain adaptation in biomedical engineering: unsupervised, source-free, and black box approaches

Domain adaptation in biomedical engineering: unsupervised, source-free, and black box approaches

A Step Closer to Becoming Symbiotic with AI through EEG: A Review of Recent BCI Technology

Position: On Potential Malware & New Attack Vectors for Internet-of-Brains (IoB)

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