稳态视觉唤起潜在驱动四旋翼控制使用深度残余CNN进行短时间信号分类
Jiannan Chen1, Chenju Yang1, Rao Wei1
1School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究介绍了EEGResNet,这是一种用于分类稳态视觉唤起潜能 (SSVEPs) 的新型深度学习模型. 通过使用过器银行和残留连接,EEGResNet提高了脑计算机接口的分类准确性.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 生物医学工程 生物医学工程
背景情况:
- 由于难以区分频域特征,因此对短时间窗口稳定状态视觉唤起潜力 (SSVEP) 的分类具有挑战性.
- 现有的方法可能会在短时间窗口内对SSVEP信号的微妙差异进行斗争.
研究的目的:
- 提出一个新的深度卷积网络,EEGResNet,以改进SSVEP分类.
- 为了提高性能,利用剩余连接和时间域特征提取.
主要方法:
- 开发了EEGResNet,这是一个包含剩余连接的深度卷积网络.
- 使用过器银行 (FB) 模块与四个Butterworth过器 (19-50 Hz, 14-38 Hz, 9-26 Hz, 3-14 Hz) 进行时间域特征提取.
- 聚合了FB特征并通过一个六层卷积神经网络与残余连接来处理它们.
主要成果:
- 通过对两个大型公共数据集的实验证明了EEGResNet的有效性和优越性.
- 实现了对短时间窗口SSVEP的更好的分类性能.
结论:
- EEGResNet为准确的SSVEP分类提供了一个有希望的方法,特别是在短时间窗口.
- 经过训练的网络显示了诸如脑电脑接口 (BCI) 控制的四旋翼系统等应用的潜力,通过虚拟模拟进行验证.
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