跨会话SSVEP脑印识别使用专注的多子频段深度身份嵌入学习网络
Chengxian Gu1,2, Xuanyu Jin1,2, Li Zhu1,2
1School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang China.
Cognitive neurodynamics
|January 13, 2025
概括
这项研究引入了一种新的深度学习网络,用于使用稳定状态视觉唤起潜力 (SSVEP) 电脑电图 (EEG) 信号进行稳定的脑印识别. 该方法提高了跨会话的准确性,为生物识别系统提供了有前途的进步.
科学领域:
- 生物识别信息 生物识别信息
- 神经科学是一个神经科学.
- 机器学习 机器学习
背景情况:
- 使用脑电图 (EEG) 进行脑印识别,由于信号变化和信号噪声比较低而面临挑战.
- 稳态视觉唤起潜能 (SSVEP) 提供更高的信号噪声比和频率锁定,使它们适合于脑印识别.
- 从SSVEPEEG信号中提取时间不变的身份信息对于可靠的生物识别系统至关重要.
研究的目的:
- 开发一种稳定的跨会话SSVEP脑印识别的强大方法.
- 为了应对不同记录会话中识别精度降低的挑战.
- 提出一种新的深度学习架构,用于从SSVEP信号中提取增强的身份信息.
主要方法:
- 提出了一个专注的多子频段深度身份嵌入式学习网络.
- 引入了一个子频段注意频率机制,以整合频域特征并探索深度频率身份信息.
- 员工注意力统计聚合,以提高频率域特征分布在会话中的稳定性.
主要成果:
- 与最先进的模型相比,拟议的方法在2秒的SSVEP样本上实现了跨会话的优越性能.
- 在跨会话脑印识别中表现出增强的稳定性和准确性.
- 在两个多会话SSVEP基准数据集上验证了方法.
结论:
- 专注的多子频段深度身份嵌入学习网络提供稳定的跨会话SSVEP脑印识别.
- 拟议的机制有效地解决了低信号噪声比率和时间变化的脑信号的局限性.
- 该方法显示了作为多主体生物识别系统的基准的潜力.
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