在Android智能手机上使用轻量级机器学习模型进行设备上单通道EEG分类
Doli Hazarika1, Sanjay Chhaba1, Ramdas Ransing2
1Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Neural Engineering Lab, Block N, Academic Complex, IIT Guwahati, Amingaon, Guwahati, Assam, 781039, INDIA.
Biomedical physics & engineering express
|January 23, 2026
概括
本研究介绍了一种机器学习管道,用于对Android设备上的脑电图 (EEG) 信号进行分类,在有限的数据中实现90%的眼睛状态检测准确度. 这使得可用于各种应用的设备上可访问的EEG监控成为可能.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 移动健康服务提供者
背景情况:
- 电脑电图 (EEG) 信号对于认知和医疗应用至关重要,但传统的深度学习模型需要大量的数据和计算能力.
- 开发用于设备上EEG分类的轻量级模型对于移动部署和可访问性至关重要.
研究的目的:
- 介绍一条机器学习管道,用于在有限数据的Android设备上高效的设备内EEG信号分类.
- 为了实现离线EEG分类,用于诸如眼睛状态检测等应用.
主要方法:
- 从十名参与者收集了EEG数据,他们执行了眼睛打开和眼睛关闭的任务.
- 利用嵌入式工件子空间重建 (E-ASR) 进行工件移除和功率光谱特征提取.
- 在单通道头电极上训练了一个支持向量机 (SVM) 分类器,并将其部署在Android应用程序上.
主要成果:
- 在使用单通道模型对眼睛状态 (开放或关闭) 进行分类时,获得了90%的准确性.
- 通过精度,灵敏度,特异性,F1得分和MCC证实了模型的稳定性.
- 在谷歌Pixel 7 Pro和三星S22.22等Android设备上成功展示了EEG信号分类.
结论:
- 这条管道使安卓设备上的有限数据能够进行EEG分类,从而增强在自然环境中的生理测量.
- 这种方法可以扩展到认知工作量监测,发作检测和心理健康评估.
- 证明了基于智能手机的可扩展和可访问的EEG监测在研究和临床应用中的可行性.
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