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AERIAL: Adversarial Evaluation of Robustness in Accuracy-Preserving Low-Precision EEG Decoders

arXiv cs.CRby Saim Rehman ()1 min read1 · 1 read
Read at arxiv.org
AI summary · glm-5.3Summaries by Vani AI

A research paper on low-precision EEG decoders examines adversarial robustness across compression techniques.

This research paper evaluates adversarial robustness in low-precision EEG decoders using BCI Competition IV-2a simulations, comparing models with different compression techniques.

Key facts
  • Research on adversarial robustness in low-precision EEG decoders
  • Comparisons of FP32 and quantized EEGNet/ShallowConvNet models
  • Results show direct robustness, transfer efficiency, and deployment efficiency are distinct
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Tags
adversarialai-researchbcicompressioneeglow-precisionquantization
Products
BCIEEGNetShallowConvNet
Countries
China
AI models
EEGNetShallowConvNet

From the original article

1 min read at arxiv.org

Deployment-oriented compression is attractive for resource-constrained brain--computer interfaces (BCIs), but whether it changes adversarial vulnerability remains unclear.

arXiv cs.CR

By Saim Rehman · arxiv.org

Read the full article at arXiv cs.CR

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.