Classification of EEG Signals Using Quantum Neural Network and Cubic Spline

被引：0
|
作者：
Raheem, Mariam Abdul-Zahra [1 ]
Hussein, Ehab AbdulRazzaq [1 ]
机构：
[1] Univ Babylon, Dept Elect Engn, Coll Engn, Hillah, Iraq
来源：
INTERNATIONAL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS | 2016年 / 62卷 / 04期
关键词：
EEG Signals; ERP Signals; Cubic Spline; Neural Networks; Quantum Neural Networks;
D O I：
10.1515/eletel-2016-0055
中图分类号：
TN [电子技术、通信技术];
学科分类号：
0809 ;
摘要：
The main aim of this paper is to propose Cubic Spline Quantum Neural Network (CS-QNN) model for analysis and classification of Electroencephalogram (EEG) signals. Experimental data used here were taken from seven different electrodes. The work has been done in three stages, normalization of the signals, extracting the features by Cubic Spline Technique (CST) and classification using Quantum Neural Network (QNN). The simulation results showed that five types of EEG signals were classified with an average accuracy for seven electrodes that is 94.3% when training 70% of the features while with an average accuracy of 92.84% when training 50% of the features.
引用
收藏
页码：401 / 408
页数：8
相关论文
共 50 条
  • [31] A Comparison of Genetic Algorithm & Neural Network (MLP) In Patient Specific Classification of Epilepsy Risk Levels from EEG Signals
    Sukanesh, R.
    Harikumar, R.
    ENGINEERING LETTERS, 2007, 14 (01)
  • [32] Personality traits classification from EEG signals using EEGNet
    Guleva, Veronika
    Calcagno, Alessandra
    Reali, Pierluigi
    Bianchi, Anna Maria
    2022 IEEE 21ST MEDITERRANEAN ELECTROTECHNICAL CONFERENCE (IEEE MELECON 2022), 2022, : 590 - 594
  • [33] Hybrid hunt-based deep convolutional neural network for emotion recognition using EEG signals
    Wankhade, Sujata Bhimrao
    Doye, Dharmpal Dronacharya
    COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING, 2022, 25 (12) : 1311 - 1331
  • [34] Dimensional Emotion Recognition Using EEG Signals via 1D Convolutional Neural Network
    Kaur, Sukhpreet
    Kulkarni, Nilima
    THIRD CONGRESS ON INTELLIGENT SYSTEMS, CIS 2022, VOL 1, 2023, 608 : 627 - 641
  • [35] Classification of EEG Signals Using Hybrid Feature Extraction and Ensemble Extreme Learning Machine
    Ren, Weijie
    Han, Min
    NEURAL PROCESSING LETTERS, 2019, 50 (02) : 1281 - 1301
  • [36] A Learnable and Explainable Wavelet Neural Network for EEG Artifacts Detection and Classification
    Yu, Yifei
    Li, Yuanxiang
    Zhou, Yunqing
    Wang, Yingyan
    Wang, Jiwen
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2024, 32 : 3358 - 3368
  • [37] Feature Extraction of Brain Event-Related Potentials Using Cubic Spline Technique
    Raheem, Mariam Abdul-Zahra
    Hussein, Ehab AbdulRazzaq
    2016 AL-SADIQ INTERNATIONAL CONFERENCE ON MULTIDISCIPLINARY IN IT AND COMMUNICATION TECHNIQUES SCIENCE AND APPLICATIONS (AIC-MITCSA), 2016,
  • [38] Classification of spontaneous EEG signals in migraine
    Bellotti, R.
    De Carlo, F.
    de Tommaso, M.
    Lucente, M.
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2007, 382 (02) : 549 - 556
  • [39] Functional Connectivity Evaluation for Infant EEG Signals Based on Artificial Neural Network
    Sharif, Mhd Saeed
    Naeem, Usman
    Islam, Syed
    Karami, Amin
    INTELLIGENT SYSTEMS AND APPLICATIONS, INTELLISYS, VOL 2, 2019, 869 : 426 - 438
  • [40] MIDNN- a classification approach for the EEG based motor imagery tasks using deep neural network
    Smita Tiwari
    Shivani Goel
    Arpit Bhardwaj
    Applied Intelligence, 2022, 52 : 4824 - 4843
12345