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Updated: Jun 14, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
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在规模上注释神经生理学数据,并优化人类输入.

Zhongchuan Xu1, Brittany H Scheid1, Erin Conrad2

  • 1Department of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, Pennsylvania, 19104-6321, UNITED STATES.

Journal of neural engineering
|June 12, 2025
PubMed
概括
此摘要是机器生成的。

标注 Co-pilot,一个循环中的人类解决方案,可显著减少83%的内EEG (iEEG) 数据的人类标注时间. 这种人工智能工具以更少的数据实现了专家级发作检测性能,加速了神经科学研究.

关键词:
积极学习是积极学习.标注注释 标注注释是一种病.人类在循环中的人类发作检测检测器可以检测到自主监督学习学习欧洲的电力.

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科学领域:

  • 神经科学是一个神经科学.
  • 生物医学工程 生物医学工程
  • 人工智能的人工智能

背景情况:

  • 神经科学研究产生了大量的数据集,这给注释带来了挑战.
  • 专家注释是耗时的,缺乏可复制性.
  • 当前的自动化方法需要大量的标记数据,限制了可扩展性.

研究的目的:

  • 为内EEG (iEEG) 数据开发一种半自动化的注释方法.
  • 为了提高iEEG注释的效率和可扩展性.
  • 介绍 评论 副驾驶员,一个人-在-循环 (HITL) 解决方案.

主要方法:

  • 利用深度主动学习和自我监督学习 (SwAV) 来进行iEEG注释.
  • 在1500小时的未标记iEEG录音上训练了一个深度神经网络.
  • 采用主动学习来优先考虑专家审查的信息数据时代.

主要成果:

  • 分析了超过8万张iEEG剪辑 (1176小时).
  • 实现了与使用1/6的人类注释 (AUC 0.9628 ± 0.015) 的顶部探测器相当的性能.
  • 与人类注释者相比,已被证明具有更高的一致性 (科恩的卡帕0.95 ± 0.04).

结论:

  • 标注 Co-pilot 在iEEG标注中实现了专家级别的性能.
  • 该方法可将注释时间缩短83%,提高效率.
  • 显示了加速电生理学研究和AI的临床翻译的前景.