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@ARTICLE{Hu:272344,
      author       = {Hu, Xin and Emery, Brett Addison and Khanzada, Shahrukh and
                      Amin, Hayder},
      title        = {{DENOISING}: {D}ynamic enhancement and noise overcoming in
                      multimodal neural observations via high-density {CMOS}-based
                      biosensors.},
      journal      = {Frontiers in Bioengineering and Biotechnology},
      volume       = {12},
      issn         = {2296-4185},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {DZNE-2024-01161},
      pages        = {1390108},
      year         = {2024},
      abstract     = {Large-scale multimodal neural recordings on high-density
                      biosensing microelectrode arrays (HD-MEAs) offer
                      unprecedented insights into the dynamic interactions and
                      connectivity across various brain networks. However, the
                      fidelity of these recordings is frequently compromised by
                      pervasive noise, which obscures meaningful neural
                      information and complicates data analysis. To address this
                      challenge, we introduce DENOISING, a versatile data-derived
                      computational engine engineered to adjust thresholds
                      adaptively based on large-scale extracellular signal
                      characteristics and noise levels. This facilitates the
                      separation of signal and noise components without reliance
                      on specific data transformations. Uniquely capable of
                      handling a diverse array of noise types (electrical,
                      mechanical, and environmental) and multidimensional neural
                      signals, including stationary and non-stationary oscillatory
                      local field potential (LFP) and spiking activity, DENOISING
                      presents an adaptable solution applicable across different
                      recording modalities and brain networks. Applying DENOISING
                      to large-scale neural recordings from mice hippocampal and
                      olfactory bulb networks yielded enhanced signal-to-noise
                      ratio (SNR) of LFP and spike firing patterns compared to
                      those computed from raw data. Comparative analysis with
                      existing state-of-the-art denoising methods, employing SNR
                      and root mean square noise (RMS), underscores DENOISING's
                      performance in improving data quality and reliability.
                      Through experimental and computational approaches, we
                      validate that DENOISING improves signal clarity and data
                      interpretation by effectively mitigating independent noise
                      in spatiotemporally structured multimodal datasets, thus
                      unlocking new dimensions in understanding neural
                      connectivity and functional dynamics.},
      keywords     = {high-density microelectrode arrays (Other) / large-scale
                      neural recordings (Other) / neural circuits/networks (Other)
                      / neural dynamics (Other) / spike sorting (Other) / waveform
                      clustering (Other)},
      cin          = {AG Amin},
      ddc          = {570},
      cid          = {I:(DE-2719)1710010},
      pnm          = {351 - Brain Function (POF4-351)},
      pid          = {G:(DE-HGF)POF4-351},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39301177},
      pmc          = {pmc:PMC11411565},
      doi          = {10.3389/fbioe.2024.1390108},
      url          = {https://pub.dzne.de/record/272344},
}