.png){kind=icon}
返回
Dynamic Convolution With Multilevel Attention for EEG-Based Motor Imagery Decoding
.png){kind=icon}
PRE
AI
DOI：10.1109/JIOT.2023.3281911.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Brain-computer interface (BCI) is an innovative technology that utilizes artificial intelligence (AI) and wearable electroencephalography (EEG) sensors to decode brain signals and enhance the quality of life. EEG-based motor imagery (MI) brain signal is used in many BCI applications, including smart healthcare, smart homes, and robotics control. However, the restricted ability to decode brain signals is a major factor preventing BCI technology from expanding significantly. In this study, we introduce a dynamic attention temporal convolutional network (D-ATCNet) for decoding EEG-based MI signals. The D-ATCNet model uses dynamic convolution (Dy-conv) and multilevel attention to enhance the performance of MI classification with a relatively small number of parameters. D-ATCNet has two main blocks: 1) dynamic and 2) temporal convolution. Dy-conv uses multilevel attention to encode low-level MI-EEG information and temporal convolution uses shifted window with self-attention to extract high-level temporal information from the encoded signal. The proposed model performs better than the existing methods with an accuracy of 71.3% for subject independent and 87.08% for subject dependent using the BCI competition IV-2a data set.
Keyword:
Convolution
Electroencephalography
Convolutional neural networks
Brain modeling
Decoding
Kernel
Feature extraction
Attention mechanism
classification
convolutional neural network (CNN)
dynamic convolution (Dy-conv)
electroencephalography (EEG)
motor imagery (MI)
multihead self-attention (MSA)
smart healthcare
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
引用论文
Attention-Inception and Long- Short-Term Memory-Based Electroencephalography Classification for Motor Imagery Tasks in Rehabilitation用于康复运动图像任务的注意力开始和基于长短期记忆的脑电图分类
PREAI
分享收藏Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning.png)
SENSORS
OAAI
SENSORSIF3.5
OAAI分享收藏Digital Audio Forensics: Microphone and Environment Classification Using Deep LearningIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep LearningIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Deep Learning With Convolutional Neural Networks for EEG Decoding and VisualizationHUMAN BRAIN MAPPINGOAAI
HUMAN BRAIN MAPPINGIF3.3
OAAI分享收藏
查看更多内容