基于融合卷积神经网络与注意力阻断的多类运动图像分类的最佳道选择
Joharah Khabti1, Saad AlAhmadi1, Adel Soudani1
1Department of Computer Science, College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11543, Saudi Arabia.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
这项研究介绍了用于脑计算机接口 (BCI) 的具有注意力的融合卷积神经网络 (FCNNA). FCNNA模型提高了运动图像 (MI) 分类的准确性,并减少了通道噪声,增强了人机通信.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
背景情况:
- 大脑-计算机接口 (BCI) 通常使用运动图像 (MI) 来进行人机通信.
- 来自MI的EEG信号存在分类挑战,原因是噪音和多余的通道,增加了成本.
- 最佳的通道选择对于高效准确的BCI性能至关重要.
研究的目的:
- 提出一种最佳的道选择方法,用于使用融合卷积神经网络与注意力 (FCNNA) 进行多类MI分类.
- 增强EEG信号特征提取并降低BCI系统中的计算成本.
- 提高基于MI的BCI的准确性和效率.
主要方法:
- 开发了一种具有空间和时间过器的CNN模型,以捕获EEG信号特征.
- 集成了一个卷积块注意模块 (CBAM) 进行增强的特征提取.
- 采用遗传算法进行最佳的通道选择,提供固定和可变的通道选项.
主要成果:
- 与基线模型相比,FCNNA模型在多类分类中显示了6.41%的改进.
- 在二进制分类 (左手/右手移动) 中获得了93.09%的准确性.
- 跨主题多类分类达到68.87%的准确性,在道选择后提高到84.53%.
结论:
- 建议使用最佳通道选择的FCNNA模型显著提高了基于EEG的MI分类准确性.
- 该方法在道选择和分类方面都很有效,在减少的道集中提供了卓越的结果.
- 这种方法为BCI应用提供了具有成本效益和高性能的解决方案.
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