SPARK:一个高效的黑子域名适应框架,用于源码隐私保护和嗜睡检测
IEEE journal of biomedical and health informatics
|March 14, 2024
概括
基于脑电图 (EEG) 的睡眠检测的新隐私保护方法使用黑盒域调整. 这种方法提高了道路安全,使得精确的昏昏欲睡监测,而无需访问敏感的源数据.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 道路交通安全 道路交通安全
背景情况:
- 基于脑电图 (EEG) 的嗜睡监测对于道路安全至关重要.
- 对于EEG分析而言,现有的域调整方法通常需要源数据或模型,这引发了隐私问题.
- 跨主体评估对于可通用的EEG嗜睡检测至关重要.
研究的目的:
- 为基于EEG的嗜睡检测引入一种新的黑子域适应 (BBDA) 框架.
- 为解决与传统无监督和无源代码域名适应方法相关的隐私风险.
- 开发一个高效和强大的系统,用于现实世界的嗜睡监测.
主要方法:
- 提出了基于集体深度随机向量功能链路 (edRVFL) 的黑盒知识适应 (SPARK) 框架的自蒸和伪标签.
- 使用基于的选择来进行高保证率样本伪标签.
- 雇员集体自我蒸,以提取知识和提炼学生模型.
主要成果:
- 在两个公共司机昏昏欲睡的数据集上,SPARK与强大的基线相比表现优越.
- 该框架显著减少了培训时间,提高了计算效率.
- edRVFL的功能有助于提高效率,特别是对于小型数据集.
结论:
- 该SPARK框架为基于EEG的嗜睡检测提供了一种有效且保护隐私的解决方案.
- SPARK显示了实际集成到现实世界的监控系统的潜力.
- 该研究强调了BBDA在敏感生理数据分析中的可行性.
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