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基于数据挖掘技术的EEG信号的计算眼睛状态分类模型:比较分析.

Subhash Mondal1,2, Amitava Nag3

  • 1Computer Science & Engineering (AI & ML), Dayananda Sagar University, Bengaluru, Karnataka, India. ph22cse1001@cit.ac.in.

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概括
此摘要是机器生成的。

这项研究使用脑电图 (EEG) 信号和机器学习准确地分类眼睛状态. k-最近邻居模型的准确度超过97%,证明了实时脑机接口应用程序的潜力.

关键词:
分类模型的分类模型.一个共同的空间模式.电脑电图 (EEG) 是一个电脑电图.眼睛状态检测检测器k-最近的邻居 (KNN)k-平均准确度 准确度 k-平均准确度

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科学领域:

  • 神经科学是一个神经科学.
  • 生物医学工程 生物医学工程
  • 人工智能的人工智能

背景情况:

  • 人工智能 (AI) 在使用生物信号的非侵入性医疗诊断方面表现有前途.
  • 电脑电图 (EEG) 信号对于理解大脑活动至关重要.
  • 大脑计算机接口 (BCI) 利用生物信号进行人机交互.

研究的目的:

  • 使用EEG信号对眼睛的状态进行分类 (打开/关闭).
  • 评估各种机器学习模型对此分类任务的有效性.
  • 探索信号处理和特征提取技术对分类准确性的影响.

主要方法:

  • 利用了14980个EEG信号实例的公开数据集.
  • 应用的预处理步骤:Z-score异常值去除,SMOTETomek用于类不平衡,以及带程过.
  • 使用独立的t测试进行特征选择,并使用共同空间模式 (CSP) 进行特征提取.
  • 通过十倍交叉验证评估了14个经典的机器学习模型.

主要成果:

  • 几种分类器的准确度达到了90%以上.
  • k-最近的邻居 (k-NN) 获得了最高的准确性 (97.92%与CSP,97.75%没有CSP).
  • CSP增强了多层感知器 (95.30%) 和支持矢量机 (93.93%) 的性能.

结论:

  • 整合统计验证,信号处理和ML可实现基于EEG的精确眼睛状态分类.
  • 这些发现支持实时BCI的实际应用.
  • 这种方法为医疗保健可穿戴设备提供了一种轻量级的解决方案.