EEGDiR: Electroencephalogram denoising network for temporal information storage and global modeling through Retentive Network

arXiv:2404.15289v1 Announce Type: cross
Abstract: Electroencephalogram (EEG) signals play a pivotal role in clinical medicine, brain research, and neurological disease studies. However, susceptibility to various physiological and environmental artifacts introduces noise in recorded EEG data, impeding accurate analysis of underlying brain activity. Denoising techniques are crucial to mitigate this challenge. Recent advancements in deep learningbased approaches exhibit substantial potential for enhancing the signal-to-noise ratio of EEG data compared to traditional methods. In the realm of large-scale language models (LLMs), the …

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