使用循环神经网络对事件相关潜力的盲源分离
Jamie A O'Reilly1, Hassapong Sunthornwiriya-Amon2, Naradith Aparprasith2
1School of International & Interdisciplinary Engineering Programs, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
bioRxiv : the preprint server for biology
|May 7, 2024
概括
一种新的循环神经网络 (RNN) 方法用于盲源分离,可以改进与事件相关的潜力 (ERP) 分析. 与ICA等传统方法相比,这种方法提供了更清晰,更具体的神经信号分解.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 信号处理 信号处理
背景情况:
- 与事件相关的潜能 (ERP) 反映了与特定事件相关的神经活动.
- 在ERP中准确地定位源至关重要,但在传统方法中具有挑战性.
- 现有的ERP源分离技术可以产生复杂的,难以解释的结果.
结论:
- 拟议的RNN方法有效地分解了大平均ERP差异波.
- 源自RNN的来源显示了振幅,极性和双极方向的模糊性减少.
- 这种RNN方法显示了作为事件相关神经信号的计算模型的前景.
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