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
相关论文
共 50 条
  • [1] LPOA-DRN: Deep learning based feature fusion and optimization enabled Deep Residual Network for classification of Motor Imagery EEG signals
    Gouri, M. S.
    Grace, K. S. Vijula
    SIGNAL IMAGE AND VIDEO PROCESSING, 2023, 17 (05) : 2167 - 2175
  • [2] Optimization enabled deep residual neural network for motor imagery EEG signal classification
    Kumar, T. Rajesh
    Mahalaxmi, U. S. B. K.
    Ramakrishna, M. M.
    Bhatt, Dhowmya
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2023, 80
  • [3] Classification of Motor Imagery EEG Signals with Deep Learning Models
    Shen, Yurun
    Lu, Hongtao
    Jia, Jie
    INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING, ISCIDE 2017, 2017, 10559 : 181 - 190
  • [4] Classification of motor imagery EEG signals using deep learning
    Rahma, Boungab
    Aicha, Reffad
    Kamel, Mebarkia
    PROGRAM OF THE 2ND INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND AUTOMATIC CONTROL, ICEEAC 2024, 2024,
  • [5] Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion
    Amin, Syed Umar
    Alsulaiman, Mansour
    Muhammad, Ghulam
    Mekhtiche, Mohamed Amine
    Hossain, M. Shamim
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2019, 101 : 542 - 554
  • [6] Deep neural network with harmony search based optimal feature selection of EEG signals for motor imagery classification
    Nakra A.
    Duhan M.
    International Journal of Information Technology, 2023, 15 (2) : 611 - 625
  • [7] A novel deep learning approach for classification of EEG motor imagery signals
    Tabar, Yousef Rezaei
    Halici, Ugur
    JOURNAL OF NEURAL ENGINEERING, 2017, 14 (01)
  • [8] A Novel Simplified Convolutional Neural Network Classification Algorithm of Motor Imagery EEG Signals Based on Deep Learning
    Li, Feng
    He, Fan
    Wang, Fei
    Zhang, Dengyong
    Xia, Yi
    Li, Xiaoyu
    APPLIED SCIENCES-BASEL, 2020, 10 (05):
  • [9] Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network
    Miao, Minmin
    Hu, Wenjun
    Yin, Hongwei
    Zhang, Ke
    COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2020, 2020
  • [10] Classification of Motor Imagery EEG Signals Based on Deep Autoencoder and Convolutional Neural Network Approach
    Hwaidi, Jamal F.
    Chen, Thomas M.
    IEEE ACCESS, 2022, 10 : 48071 - 48081
12345