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This study presents a comprehensive framework to isolate and validate brain signals in hospital environments, where interference from medical devices, radio transmissions, and ambient noise complicates measurement. We focus on 6–26 MHz rectangular bursts repeated at 50–60 kHz, measured from anesthetized patients’ foreheads that appear during unconsciousness and disappeared upon waking, as confirmed by Dodecanogram or DDG, and BIS metrics from 40 gastroenteric patients under propofol anesthesia. Our noise-signal interaction simulator integrates advanced signal processing, statistical modeling, and multidimensional analyses to differentiate genuine neural activity from artifacts. Central to this effort is a specialized database capturing intricate noise–signal interactions, which informs an automated filter for isolating pure brain signals. By mapping signal variability across transitions into and out of unconsciousness, we find that pulsed signal can generate EEG like signals by interacting with brown noise. Experimental validations confirm that the rectangular nature of megahertz bursts saves them against diverse sinusoidal interference sources. Our simulator-database combo is potent for reliable diagnoses and neuroscientific research, where even minor noise artifacts can obscure crucial physiological processes. This unified framework thus sets a new standard for noise-robust brainwave analysis, paving the way for advanced clinical tools and deeper scientific understanding of the brain’s consciousness profile. ​

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Rectangular Nature of Megahertz Bursts Naturally Eliminates Operation Theater’s Sinusoidal Noises Affecting Anesthetized Subjects: A Clinical Study

  • 会议论文

  • 发表日期：

    2026年

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  • Shanthi Banishetty,
  • Sarika Katiyar,
  • Saifullah Tipu,
  • Pushpendra Singh,
  • Tanusree Dutta,
  • Rupesh Ranjan,
  • Stuart Hameroff,
  • Anirban Bandyopadhyay

摘要

This study presents a comprehensive framework to isolate and validate brain signals in hospital environments, where interference from medical devices, radio transmissions, and ambient noise complicates measurement. We focus on 6–26 MHz rectangular bursts repeated at 50–60 kHz, measured from anesthetized patients’ foreheads that appear during unconsciousness and disappeared upon waking, as confirmed by Dodecanogram or DDG, and BIS metrics from 40 gastroenteric patients under propofol anesthesia. Our noise-signal interaction simulator integrates advanced signal processing, statistical modeling, and multidimensional analyses to differentiate genuine neural activity from artifacts. Central to this effort is a specialized database capturing intricate noise–signal interactions, which informs an automated filter for isolating pure brain signals. By mapping signal variability across transitions into and out of unconsciousness, we find that pulsed signal can generate EEG like signals by interacting with brown noise. Experimental validations confirm that the rectangular nature of megahertz bursts saves them against diverse sinusoidal interference sources. Our simulator-database combo is potent for reliable diagnoses and neuroscientific research, where even minor noise artifacts can obscure crucial physiological processes. This unified framework thus sets a new standard for noise-robust brainwave analysis, paving the way for advanced clinical tools and deeper scientific understanding of the brain’s consciousness profile. ​