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汇总内在信息,通过联合学习来提高BCI绩效

Rui Liu¹, Yuanyuan Chen¹, Anran Li¹

¹School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore.

Neural networks : the official journal of the International Neural Network Society

|January 17, 2024

概括

联合学习EEG解码 (FLEEG) 能够使各种大脑-计算机接口数据集进行协作,克服设备异质性. 这提高了深度学习模型的性能,使知识共享成为可能,特别是对于较小的数据集.

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) 研究.
机器学习用于信号处理.

背景情况:

大脑计算机接口 (BCI) 的高性能深度学习模型受到电脑脑学 (EEG) 数据不足和异质的阻碍.
现有的BCI研究往往侧重于单一数据集培训,忽视了由于设备多样性的多站点数据的潜力.
数据多样性对于开发强大的BCI模型至关重要,但共享多站点EEG数据仍然是一个重大挑战.

研究的目的:

提出一个新的框架,解决训练深度学习模型的挑战,使用来自多个来源的异质EEG数据.
通过跨不同EEG数据集进行协作模型培训,增强BCI中的数据多样性和模型稳定性.
为BCI引入一种新的学习范式,克服设备异质性,促进知识交换.

主要方法:

开发一个层次化的个性化联邦学习EEG解码 (FLEEG) 框架.
每个客户端数据集都会训练一个个性化的层次模型来管理各种数据格式并实现信息交换.
一个中央服务器协调培训,汇总来自所有数据集的知识,以提高整体性能.

主要成果:

在FLEEG框架中,在9个不同的EEG数据集中,运动图像 (MI) 分类性能提高了8.4%.
通过FLEEG实现的知识共享显著有利于较小的数据集,增强它们对模型培训的贡献.
可视化证实,经过FLEEG训练的模型保持了对与任务相关的神经区域的稳定关注,从而提高了分类准确性.

结论:

拟议的FLEEG框架提供了一个有效的端到端解决方案,用于利用BCI中的多站点,异构的EEG数据.
具有层次个性化的联合学习可以在BCI模型开发中成功解决数据异质性挑战.
这种方法代表了BCI研究的重大进步,为更强大,更普遍的深度学习模型铺平了道路.

关键词:

大脑计算机接口联合学习是联合学习.不同质的数据集.运动图像中的运动图像.

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