五类机动图像BCI分类及其对脑控制轮椅的应用
Hongguang Pan1,2, Bingyang Teng1,2, Zesheng Liu1,2
1College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an, 710054 Shaanxi China.
Cognitive neurodynamics
|February 6, 2026
概括
这项研究介绍了EHT-CSP,这是使用运动图像脑电脑接口 (MI-BCI) 控制大脑轮椅 (BCW) 的先进算法. 新方法显著提高了用于轮椅直观导航的分类准确性.
科学领域:
- 神经科学和生物医学工程
- 大脑与计算机接口 (BCI)
- 康复技术 康复技术 康复技术
背景情况:
- 脑控制轮椅 (BCW) 技术为身体障碍的人提供了移动性.
- 基于运动图像的BCI (MI-BCI) 提供非侵入性神经控制,但在分类准确性方面面临挑战.
- 可靠的多类分类对于先进的BCW功能至关重要.
研究的目的:
- 为了提高MI-BCI系统在BCW应用中的性能.
- 开发一个先进的特征提取算法,以提高分类准确度.
- 在现实世界BCW导航任务中验证拟议的系统.
主要方法:
- 提出了一个EHT-CSP算法,将集体实证模式分解希尔伯特-黄变形 (EEMD-HHT) 与时间频率共同空间模式 (TFCSP) 集成在一起.
- 使用EEMD-HHT提取边际频谱和能量频谱,与TFCSP特征相结合.
- 使用光梯度增强机 (LightGBM) 用于在定制的五类MI-EEG数据集上进行分类.
主要成果:
- 为拟议的MI-BCI系统实现了78.45%的平均分类准确度.
- 所有参与者都成功完成了BCW避障任务.
- 与现有方法相比,EHT-CSP算法显示出更高的性能.
结论:
- 拟议的EHT-CSP算法显著提高了BCW应用的MI-BCI性能.
- 开发的系统可以对轮椅导航进行可靠和直观的控制.
- 这一进步有助于增强运动残疾人辅助技术的前景.
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