使用神经可塑性激增神经网络即时识别心理压力
Mahima Milinda Alwis Weerasinghe1,2, Grace Wang3,4, Jacqueline Whalley5
1School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand. mahimarcrc@gmail.com.
Scientific reports
|September 11, 2023
概括
一个新的受大脑启发的人工神经网络,在线神经可塑性尖端神经网络 (O-NSNN),准确地从EEG数据中检测心理压力. 这种新的方法提供了高效的,个性化的压力识别,用于可穿戴技术的潜在用途.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 心理压力显著影响人类的认知和福祉,需要非侵入性检测方法.
- 全球日益复杂的生活加剧了与压力相关的健康问题和认知表现下降.
- 当前的压力检测方法往往缺乏广泛应用所需的个性化和效率.
研究的目的:
- 介绍和评估一种新的人工尖端神经网络模型,用于分类心理压力.
- 用个性化的脑电图 (EEG) 数据来评估模型的性能.
- 探索大脑灵感模型的潜力,以实现强大而高效的压力识别.
主要方法:
- 在线神经可塑性尖端神经网络 (O-NSNN) 模型的开发.
- 利用受大脑神经可塑性启发的学习概念来进行压力分类.
- 通过音频评论在诱导急性压力时收集的EEG数据上测试O-NSNN模型.
- 对O-NSNN模型进行个性化培训和评估.
主要成果:
- 在O-NSNN模型实现了90.76% (σ=2.09) 的平均准确度,从EEG信号分类精神压力.
- 这些以大脑为灵感的模型在实时学习 ("即时") 中表现出了强度和效率.
- 探索性分析揭示了对感知和急性精神压力之间的联系的见解.
结论:
- 与现有方法相比,O-NSNN算法提供了优越的个性化压力识别.
- 该模型的准确性,效率和可解释性使其适合整合到可穿戴技术中.
- 这种由大脑启发的方法推进了非侵入性精神压力检测和监测.
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