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风的愉快度的分类通过电脑电图

Yasuhisa Maruyama¹, Ryuto Nakamura², Shota Tsuji³

¹School of Computing, Tokyo Institute of Technology, Yokohama, Japan.

|February 27, 2024

概括

电脑电图 (EEG) 可以从空气流中检测到人类的愉悦感. 这种大脑信号分析有助于通过机器学习来个性化环境舒适性,提高生产力.

科学领域:

环境科学环境科学
神经科学是一个神经科学.
人与计算机的交互

背景情况:

人类的热舒适性对生产力至关重要,并受到空气流等环境因素的影响.
目前用于控制热舒适度的方法依赖于一般环境设置.
需要使用生理信号进行客观测量,以实现个性化的舒适性.

研究的目的:

调查电脑电图 (EEG) 信号是否可以识别与环境空气流相关的愉悦感.
探索使用机器学习来分析EEG数据以应对风的波动.
评估EEG在自动控制个性化热环境中的潜力.

主要方法:

参与者在受控气候室中暴露于各种各样的空气流速.
在暴露期间记录了脑电图 (EEG) 信号.
使用机器学习模型 (SVM,ANN) 来从EEG源活动中分类愉悦度.

主要成果:

脑电图信号包含了预测与空气流相关的愉悦度的信息.
对愉悦感检测的分类准确度超过了机会水平.
线性/非线性SVM和人工神经网络都是有效的.

结论:

脑电图是一种可行的工具,可以客观地测量人类对风的暂时愉悦性.
这项研究支持使用脑计算机接口开发个性化的舒适系统.
这些发现为适应性环境铺平了道路,从而提高了福祉和生产力.

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