.png){kind=icon}
返回
An efficient EEG signal classification technique for Brain-Computer Interface using hybrid Deep Learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.bspc.2022.104005.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Differently-abled individuals always need support from others for their day-to-day activities. Brain Computer Interface (BCI) has the potential to help those people in carrying out the basic activities. Artificial Intelligence (AI) and Internet of Things (IoT) ecosystem make the BCI technique more useful and efficient. Deep Learning (DL) algorithms can be used for proper identification of body activities from the brain signal. In this paper, we have developed a hybrid DL architecture for the efficient analysis of the EEG signal. To extract the important multi-domain features, we have efficiently selected and used the CNN filters in the proposed architecture. For further improvement of the system performance, the redundant features were removed using the feature reduction technique. In our experiment, we used real-time EEG datasets collected from the physionet biosignal database. The obtained results established that the proposed method outperformed the existing techniques in terms of accuracy, precision, recall, F1-score, and Matthew Correlation Coefficient. Finally, we developed an IoT-based BCI prototype and successfully conducted all the experiments to validate the scheme in a real-time environment.
Keyword:
Brain Computer Interaction
EEG
Deep learning
IoT
期刊
IF:
4.9
论文数:
1.0W
被引数:
2.4W
机构
引用论文
分享收藏TLR9 Promoter Polymorphism Is Associated with Both an Increased Susceptibility to Gastric Carcinoma and Poor Prognosis.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏A Deep Transfer Convolutional Neural Network Framework for EEG Signal ClassificationIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Structural study of lanthanides(III) in aqueous nitrate and chloride solutions by EXAFS通过EXAFS对硝酸盐和氯化物水溶液中镧系元素 (III) 的结构研究
PREAI
分享收藏EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classificationEeg-inception: 一种用于基于EEG的运动图像分类的准确且鲁棒的端到端神经网络
OAAI
OAAI分享收藏分享收藏A Model-Driven Scheme to Compensate the Strain-Based Non-Intrusive Dynamic Pressure Measurement for Hydraulic Pipe基于应变补偿的液压管道非侵入式动态压力测量模型驱动方案
PREAI
分享收藏
查看更多内容