.png){kind=icon}
返回
A motor imagery EEG signal classification algorithm based on recurrence plot convolution neural network
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2021.03.023.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the promotion of brain-computer interface technology, it is possible to study brain control system through EEG signals in recent years. In order to solve the problem of EEG signal classification effectively, a motor imagery classification algorithm based on recurrence plot convolution neural network is proposed. Firstly, EEG signals are preprocessed to enhance the signal intensity in the exercise interval. Secondly, time-domain and frequency-domain features are extracted respectively to construct the feature mode of recurrence plot. Finally, a new neural network is established to realize the accurate recognition of left and right movements. This research can also be transferred to other research fields. ? 2021 Elsevier B.V. All rights reserved.
Keyword:
EEG signal
Recurrence plot
Convolution neural network
Classification
Motor imagery
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
机构
引用论文
.png){kind=icon}
Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors基于脑电图的脑机接口 (BMI) 的设计和优化，用于中风幸存者的上肢外骨骼
OAAI
OAAI分享收藏
查看更多内容