一个深度学习模型,结合了卷积神经网络和SSVEP基础BCI的选择性内核机制
1Department of Information and Communication Engineering, School of Information Engineering, Nanchang University, Nanchang, 330031, China.
Computers in biology and medicine
|July 2, 2025
概括
一个新的深度学习模型,FBCNN-TKS,通过使用稳定状态视觉唤起潜力 (SSVEPs) 改进脑计算机接口 (BCI). 它增强了特征提取并减少了过拟合,从而提高了BCI的准确性和信息传输率.
科学领域:
- 神经科学和人工智能 人工智能
- 大脑与计算机接口 (BCI) 技术
背景情况:
- 现有的稳定状态视觉唤起潜力 (SSVEP) 大脑-计算机接口 (BCI) 的深度学习模型在训练数据不足的情况下扎,导致过度拟合.
- 当前模型中的受体场有限,阻碍SSVEP信号有效捕获全球时间特征.
研究的目的:
- 引入一个新的深度学习模型,FBCNN-TKS,旨在克服现有的SSVEP-BCI方法的局限性.
- 增强特征提取能力,提高SSVEP-BCI中的分类准确性和信息传输速率 (ITR).
主要方法:
- 在FBCNN-TKS模型中,使用过器银行从SSVEP信号中提取波组件.
- 功能提取是使用卷积神经网络 (CNN) 进行的,该网络与时间内核选择 (TKS) 模块集成.
- 交叉损失和中心损失的组合目标函数优化了模型,在TKS模块中使用扩展和分组的卷积来减少参数并防止过拟.
主要成果:
- 与公共数据集Benchmark和BETA的最先进方法相比,FBCNN-TKS模型表现出更高的性能.
- 在0.4秒的数据长度下,最高精度为83.10%和72.98%,信息传输速率 (ITR) 分别为251.54bpm和203.47bpm.
- 通过提供更广泛的受体场,TKS模块显著改善了特征提取.
结论:
- FBCNN-TKS模型有效地解决了过问题,并增强了SSVEP-BCI中的时间特征提取.
- 拟议的模型显示出开发高性能SSVEP-BCI系统的巨大潜力,特别是用于字符拼写应用程序.
- 整合TKS模块,扩展和分组卷积为未来的BCI研究提供了一个有希望的方向.
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