使用变压器进行对比表示学习,以实现可靠的听觉EEG解码
Lies Bollens1,2, Bernd Accou3,4, Hugo Van Hamme2
1Dept. Neurosciences, ExpORL, KU Leuven, Leuven, Belgium.
Scientific reports
|August 6, 2025
概括
对比式学习增强了电脑电图 (EEG) 解码语音,改善了听觉处理洞察力和听觉诊断. 这种自我监督的技术在分类听觉刺激和解码语音包裹方面取得了最先进的结果.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 从脑电图 (EEG) 解码连续语音对于理解听觉处理和开发听觉诊断至关重要.
- 深度学习已经推进了EEG解码,但低信号噪声比仍然是一个挑战.
研究的目的:
- 探索对比式学习,用于强大的EEG信号的潜在表示.
- 引入一种新型模型,将对比学习和变压器网络结合起来,用于听觉EEG解码.
- 评估听觉刺激分类和信封解码任务的模型.
主要方法:
- 利用对比学习,一种自我监督的技术,来学习EEG信号表示.
- 开发了一个新的架构,将对比学习与变压器网络集成在一起.
- 评估了ICASSP 2023审计EEG解码挑战任务上的模型:匹配-不匹配分类和刺激信封解码.
主要成果:
- 在听觉EEG解码任务中实现了最先进的性能.
- 在匹配-不匹配分类中达到87%的准确性,在信封回归中达到0.176的皮尔森相关性.
- 在挑战中表现优于以前的领先型号.
结论:
- 对比式学习显示了提高听觉EEG解码的巨大潜力.
- 这些发现强调了拟议模型在捕捉听觉刺激和EEG反应之间的关系方面的有效性.
- 该研究提供了对影响模型通用性和临床应用准确性的因素的见解.
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