Abstract
Deployment-oriented compression is attractive for resource-constrained brain--computer interfaces (BCIs), but whether it changes adversarial vulnerability remains unclear. On BCI Competition IV-2a, we compare 32-bit floating-point (FP32) EEGNet and ShallowConvNet models with global magnitude pruning and simulated INT8 post training quantization (PTQ) and quantization-aware training (QAT) across nine subjects and three seeds. Simulation provides differentiable quantize--dequantize models for white-box attacks and gradient analysis, while native TensorRT deployment is used for validation. Accuracy-preserving compression does not improve direct robustness: at $ε=0.005$, EEGNet PGD accuracy remains 22--24\% across FP32, 50\% pruning (P50), PTQ, and QAT. However, P50 reduces bidirectional transfer efficiency to 0.963/0.928 (FP32$\rightarrow$P50/P50$\rightarrow$FP32), versus 0.994/0.997 for PTQ; the same trend holds for ShallowConvNet. Gradient alignment shows a corresponding separation, while native PTQ agrees with simulated clean/adversarial predictions in 95--98\% of cases. These results show that direct robustness, adversarial transfer, and deployment efficiency are distinct properties of compressed EEG decoders.
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Cite this article
APA 7
Rehman, S., & Shafique, M. (2026). AERIAL: Adversarial Evaluation of Robustness in Accuracy-Preserving Low-Precision EEG Decoders. https://omanscience.com/en/articles/aerial-adversarial-evaluation-of-robustness-in-accuracy-preserving-low-precision-eeg-decoders
MLA 9
Rehman, Saim, and Muhammad Shafique. "AERIAL: Adversarial Evaluation of Robustness in Accuracy-Preserving Low-Precision EEG Decoders." https://omanscience.com/en/articles/aerial-adversarial-evaluation-of-robustness-in-accuracy-preserving-low-precision-eeg-decoders.
Chicago (author–date)
Rehman, Saim, and Muhammad Shafique. 2026. "AERIAL: Adversarial Evaluation of Robustness in Accuracy-Preserving Low-Precision EEG Decoders." https://omanscience.com/en/articles/aerial-adversarial-evaluation-of-robustness-in-accuracy-preserving-low-precision-eeg-decoders.
Harvard
Rehman, S. and Shafique, M. (2026) 'AERIAL: Adversarial Evaluation of Robustness in Accuracy-Preserving Low-Precision EEG Decoders', Available at: https://omanscience.com/en/articles/aerial-adversarial-evaluation-of-robustness-in-accuracy-preserving-low-precision-eeg-decoders.
Vancouver
Rehman S, Shafique M. AERIAL: Adversarial Evaluation of Robustness in Accuracy-Preserving Low-Precision EEG Decoders. https://omanscience.com/en/articles/aerial-adversarial-evaluation-of-robustness-in-accuracy-preserving-low-precision-eeg-decoders
IEEE
S. Rehman, and M. Shafique, "AERIAL: Adversarial Evaluation of Robustness in Accuracy-Preserving Low-Precision EEG Decoders," https://omanscience.com/en/articles/aerial-adversarial-evaluation-of-robustness-in-accuracy-preserving-low-precision-eeg-decoders.