Removing Neural Signal Artifacts with Autoencoder-Targeted Adversarial Transformers (AT-AT)

• URL: http://arxiv.org/abs/2502.05332v1
• Date: Fri, 07 Feb 2025 21:13:31 GMT
• Title: Removing Neural Signal Artifacts with Autoencoder-Targeted Adversarial Transformers (AT-AT)
• Authors: Benjamin J. Choi,
• Abstract summary: We present a new machine learning-based system for filtering EMG interference from EEG data using an autoencoder-targeted adversarial transformer (AT-AT) We trained AT-AT using published neural data from 67 subjects and found that the system was able to achieve comparable test performance to larger models.
• Score: 0.0
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• Abstract: Electromyogenic (EMG) noise is a major contamination source in EEG data that can impede accurate analysis of brain-specific neural activity. Recent literature on EMG artifact removal has moved beyond traditional linear algorithms in favor of machine learning-based systems. However, existing deep learning-based filtration methods often have large compute footprints and prohibitively long training times. In this study, we present a new machine learning-based system for filtering EMG interference from EEG data using an autoencoder-targeted adversarial transformer (AT-AT). By leveraging the lightweight expressivity of an autoencoder to determine optimal time-series transformer application sites, our AT-AT architecture achieves a >90% model size reduction compared to published artifact removal models. The addition of adversarial training ensures that filtered signals adhere to the fundamental characteristics of EEG data. We trained AT-AT using published neural data from 67 subjects and found that the system was able to achieve comparable test performance to larger models; AT-AT posted a mean reconstructive correlation coefficient above 0.95 at an initial signal-to-noise ratio (SNR) of 2 dB and 0.70 at -7 dB SNR. Further research generalizing these results to broader sample sizes beyond these isolated test cases will be crucial; while outside the scope of this study, we also include results from a real-world deployment of AT-AT in the Appendix.

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