带功率特征基于部分的卷积神经网络与非洲的优化促进了对EEG分类的通道选择
Vairaprakash Selvaraj1, Manjunathan Alagarsamy2, Kavitha Datchanamoorthy3
1Department of Electronics and Communication Engineering, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India.
概括
这项研究引入了使用优化通道选择用于脑电图 (EEG) 数据的脑电脑接口的新方法. 这种方法提高了运动图像分类的准确性,并减少了现实应用的计算时间.
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
- 机器学习 机器学习
背景情况:
- 基于脑电图 (EEG) 的运动图像 (MI-EEG) 分类对于脑电脑接口 (BCI) 至关重要.
- 采集具有多个通道的EEG信号对现实世界的BCI应用提出了挑战.
- 在不影响分类性能的情况下选择最佳的EEG通道子集是一个重大问题.
研究的目的:
- 提出一种有效的频道选择方法,用于在BCI中对EEG进行分类.
- 为了提高MI-EEG分类的准确性和降低计算成本.
- 为了提高实时BCI应用的可行性.
主要方法:
- 开发了一种新的PCNNC-AVOACS-EEG方法,将带功率特征基于部分的卷积神经网络 (PCNNC) 与非洲优化 (AVO) 结合起来,用于通道选择.
- 来自BCI Competition IV,数据集1的EEG信号使用对比度有限的自适应基因图平衡和通过十六进制局部自适应二进制模式 (HLABP) 提取的特征进行了预处理.
- HLABP提取了alpha和beta频段特征,带功率数据作为PCNNC的输入,而AVO优化了频道选择.
主要成果:
- 与现有方法相比,拟议的PCNNC-AVOACS-EEG技术实现了更高的分类精度和曲线下的面积.
- 观察到计算时间的显著减少,在不同的实验环境中改善了70%,60%和65.714%.
- 该方法证明了测试组的增强分类准确性,这是实时BCI的关键指标.
结论:
- PCNNC-AVOACS-EEG方法有效地解决了MI-EEG分类中通道选择的挑战.
- 这种方法为开发更实用,更有效的实时BCI系统提供了有希望的解决方案.
- 优化技术显著提高了分类性能,并减少了计算负载.
关键词:
非洲的优化算法对比度有限的适应性直方图等效过器过.一个电脑电图 (electroencephalogram) 是一个电脑电图.十六进制局部自适应二进制模式 (HLABP) 方法.基于部分的卷积神经网络.更多相关视频
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