基于特征融合AEBGNet的运动想象脑电分类算法
Motor imagery EEG classification algorithm using feature fusion based AEBGNet
戴亮宙 1王娆芬 1王海玲1
作者信息
- 1. 上海工程技术大学电子电气工程学院,上海 201620
- 折叠
摘要
针对机器学习方法在对脑电特征进行分类时无法同时兼顾脑电信号的时-空域特征的问题,利用添加注意力机制的卷积神经网络提取空间特征和双向门控循环单元提取时间特征,提出一种基于特征融合的运动想象(Motor Imagery,MI)脑电分类算法(Attention-EEGNet-BiGRU,AEBGNet),AEBGNet可将时、空域两类特征相融合,得到更具表征性的时-空域特征,最终构建的AEBGNet分类模型在BCI competition IV 2b数据集上取得80.37%的平均正确率,比标准的EEGNet方法提高6.09%.结果表明,本文方法可以有效提高MI脑电信号的分类正确率,为MI脑电信号的分类提供新的思路.
Abstract
To address the inability of the existing machine learning methods to simultaneously consider both the temporal and spatial domain features of electroencephalogram(EEG)signals in classifying EEG features,a feature fusion based Attention-EEGNet-BiGRU(AEBGNet)is presented for classifying motor imagery(MI)EEG signals.AEBGNet is capable of fusing the temporal domain features extracted by convolutional neural network with attention mechanism and spatial domain features extracted by a bidirectional gated recurrent unit to obtain more distinctive spatiotemporal features.The constructed AEBGNet classification model achieves an average accuracy of 80.37%on the BCI competition IV 2b dataset,and there is an improvement of 6.09%over the standard EEGNet method.The results demonstrate the effectiveness of the proposed method in enhancing the classification accuracy of MI EEG signals,providing a new idea for MI EEG signal classification.
关键词
脑机接口/运动想象/卷积神经网络/双向门控循环单元/注意力机制Key words
brain-computer interface/motor imagery/convolutional neural network/bidirectional gated recurrent unit/attention mechanism引用本文复制引用
基金项目
国家自然科学基金(62173222)
国家自然科学基金青年基金(62001284)
出版年
2024
NETL
NSTL
万方数据