脑-机接口
卷积(计算机科学)
卷积神经网络
计算机科学
人工智能
信号(编程语言)
模式识别(心理学)
频道(广播)
脑电图
接口(物质)
特征提取
小波变换
语音识别
人工神经网络
小波
心理学
计算机网络
气泡
精神科
最大气泡压力法
并行计算
程序设计语言
作者
Mengyu Li,Chao Ma,Weidong Dang,Ruiqi Wang,Yong Liu,Zhongke Gao
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2022-06-15
卷期号:22 (12): 12036-12043
被引量:9
标识
DOI:10.1109/jsen.2022.3173433
摘要
The steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) system has attracted a lot of attention. It is a great challenge to increase the classification accuracy of SSVEP, especially in fatigue state. In this paper, we propose a dilated shuffle convolutional neural network (DSCNN) model to realize EEG-based SSVEP signal classification. Firstly, we conduct experiments to obtain SSVEP recordings in normal and fatigue states. Then combining continuous wavelet transform (CWT) and DSCNN, we construct a framework for realizing the SSVEP detection. In DSCNN, the signals are processed by three parallel dilated convolution layers firstly, then we extract the characteristics of the signals through channel shuffle and group convolution, while reducing the computational load. For normal condition, we reach an average accuracy rate of 96.75%, and for the data under fatigue state, the average accuracy of this method increases to 77.52%. Through the comparison with the existing methods, the effectiveness and advance of our method are proved, and the effect of channel shuffle on signal extraction is also demonstrated by comparison.
科研通智能强力驱动
Strongly Powered by AbleSci AI