通过混合哈里斯·霍克斯算法对EEG冥想分类算法来优化深度CNN架构.
Soniya Shakil Usgaonkar1, Damodar Reddy Edla2, Dharavath Ramesh3
1Department of Computer Science and Engineering, National Institute of Technology, Cuncolim, 403 703, Goa, India; Information Technology Department, Goa College of Engineering, Farmagudi, Ponda, 403401, Goa, India.
Neuroscience
|February 8, 2026
概括
这项研究引入了一种新的框架,用于使用脑电图 (EEG) 信号对冥想状态进行分类. 混合哈里斯·霍克斯优化-算术优化算法-卷积神经网络 (HHO-AOA-CNN) 模型在区分冥想类型方面实现了94.20%的准确性.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 冥想可以增强认知功能,但分析EEG信号进行分类仍然具有挑战性.
- 现有的方法使用有限的功能和传统的机器学习,缺乏先进的技术.
- 需要综合方法,结合时间频率分析,深度学习和对EEG冥想分类的优化.
研究的目的:
- 开发基于EEG的混合框架来对冥想状态进行分类.
- 通过集成先进的优化和深度学习技术来提高冥想分类的准确性.
- 为了解决目前用于冥想研究的EEG信号分析的局限性.
主要方法:
- 结合哈里斯·霍克斯优化 (HHO) 和算术优化算法 (AOA) 的混合框架被开发来调整卷积神经网络 (CNN) 的参数.
- 脑电图信号经过预处理,并使用斯托克威尔变形 (S-变形) 转化为时间频率图像.
- 在HHO-AOA-CNN模型处理这些图像的超参数优化和分类Vipassana (VIP),Isha Shoonya (IS) 和控制 (CTR) 状态.
主要成果:
- 拟议的HHO-AOA-CNN框架实现了94.20%的分类准确性.
- 与独立的HHO-CNN,AOA-CNN和基线CNN模型相比,混合模型表现出更高的性能.
- 统计分析证实了混合优化方法的稳定性和稳定性.
结论:
- 开发的HHO-AOA-CNN框架为基于EEG的冥想分类提供了一个强大而准确的方法.
- 这种方法有效地整合了先进的信号处理,深度学习和优化技术.
- 这些发现有助于通过EEG分析更好地理解和客观地测量冥想状态.
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