LPOA-DRN: Deep learning based feature fusion and optimization enabled Deep Residual Network for classification of Motor Imagery EEG signals

被引：0
|
作者：
Gouri M.S
K. S. Vijula Grace
机构：
[1] Noorul Islaam University,Department of Electronics and Communication Engineering
来源：
Signal, Image and Video Processing | 2023年 / 17卷
关键词：
Electroencephalogram (EEG); Motor Imagery (MI); Brain computer interface (BCI); Signal classification; Deep learning;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this research, the classification method for MI-based EEG signals has been developed using the Lion Political Optimization Algorithm-based Deep Residual Network (LPOA-based DRN) to address these issues. The proposed model employs the technique of data augmentation to generate the best classification outcomes with additional training examples. By altering the training data, the developed strategy improves efficiency in terms of specificity, accuracy, and sensitivity with values of 0.921, 0.904, and 0.866.
引用
收藏
页码：2167 / 2175
页数：8
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