使用Swin变压器进行多尺度EEG特征解码,用于主体独立的运动图像BCI
Wasi Ur Rehman Qamar1, Berdakh Abibullaev2
1Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Astana, 010000, Kazakhstan.
本研究介绍了用于脑计算机接口 (BCI) 的紧卷积旋转变压器 (CCST). CCST提高了独立于主体的BCI性能和对用户的概括性,克服了EEG信号变异性的挑战.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 生物医学工程 生物医学工程
背景情况:
- 大脑计算机接口 (BCI) 面临着对象变化和非静止EEG信号的挑战,阻碍了模型的概括.
- 神经活动,电极放置和噪声的差异降低了BCI的性能,需要进行广泛的重新校准以实现可靠的跨用户操作.
研究的目的:
- 为主体独立的BCI开发一种新的紧卷积旋转变压器 (CCST) 模型.
- 为了提高模型的概括性和可靠性在不同的用户之间,而无需进行广泛的重新校准.
主要方法:
- 在CCST模型中利用了基于窗口的等级自我注意力与卷积特征提取相结合.
- 采用多尺度特征表示来捕捉局部电极相互作用和全球时间依赖.
- 在BCI竞争IV (2a,2b) 和PhysioNet MI数据集上使用Leave-One-Subject-Out (LOSO) 交叉验证评估了CCST.
主要成果:
- 实现了最先进的分类准确率:68.27% (BCI Comp IV 2a),76.61% (BCI Comp IV 2b) 和71.70% (PhysioNet MI).这些分类的准确率均为68.27%和71.70%.
- 通过威尔科克森签名等级测试与邦费罗尼校正,与基准模型相比,经过统计学显著的性能改进.
- 与完全自我注意模型相比,CCST显示了较低的参数和FLOP,提高了实时应用的效率.
结论:
- CCST为适应性,主题独立的BCI提供了一个可扩展和高效的框架.
- 该模型的增强泛化能力对于真实世界的BCI部署至关重要.
- 对于神经康复,辅助技术和认知训练的应用,CCST显示出前景.
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