Enhancing the Performance of Motor Imagery EEG Classification Using Phase Features

被引:9
|
作者
Hsu, Wei-Yen [1 ,2 ]
机构
[1] Natl Chung Cheng Univ, Dept Informat Management, Minhsiung Township 621, Chiayi County, Taiwan
[2] Natl Chung Cheng Univ, Adv Inst Mfg High Tech Innovat, Minhsiung Township 621, Chiayi County, Taiwan
关键词
electroencephalogram; motor imagery; brain-computer interface; extreme learning machine; EXTREME LEARNING-MACHINE; SYNCHRONIZATION; RECOGNITION; AMPLITUDE;
D O I
10.1177/1550059414555123
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
An electroencephalogram recognition system considering phase features is proposed to enhance the performance of motor imagery classification in this study. It mainly consists of feature extraction, feature selection and classification. Surface Laplacian filter is used for background removal. Several potential features, including phase features, are then extracted to enhance the classification accuracy. Next, genetic algorithm is used to select sub-features from feature combination. Finally, selected features are classified by extreme learning machine. Compared with without phase features and linear discriminant analysis on motor imagery data from 2 data sets, the results denote that the proposed system achieves enhanced performance, which is suitable for the brain-computer interface applications.
引用
收藏
页码:113 / 118
页数:6
相关论文
共 50 条
  • [1] k-NN and LDA based Motor Imagery EEG Classification using Phase Features
    Bhatnagar, Maanvi
    Gupta, Gauri Shanker
    Sinha, Rakesh Kumar
    [J]. 2018 5TH IEEE UTTAR PRADESH SECTION INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS AND COMPUTER ENGINEERING (UPCON), 2018, : 429 - 434
  • [2] Phase synchronization for classification of motor imagery EEG
    Institute of Information and Technology, Jiangxi Blue Sky University, Nanchang 330098, China
    [J]. J. Inf. Comput. Sci., 2008, 2 (949-955):
  • [3] Enhancing Motor Imagery EEG Signal Classification with Simplified GoogLeNet
    Wang, Lu
    Wang, Junkongshuai
    Wen, Bo
    Mu, Wei
    Liu, Lusheng
    Han, Jiaguan
    Zhang, Lihua
    Jia, Jie
    Kang, Xiaoyang
    [J]. 2023 11TH INTERNATIONAL WINTER CONFERENCE ON BRAIN-COMPUTER INTERFACE, BCI, 2023,
  • [4] MOTOR IMAGERY CLASSIFICATION USING EEG SPECTROGRAMS
    Khan, Saadat Ullah
    Majid, Muhammad
    Anwar, Syed Muhammad
    [J]. 2023 IEEE 20TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, ISBI, 2023,
  • [5] EEG Classification for Motor Imagery BCI Using Phase-only Features Extracted by Independent Component Analysis
    Qureshi, Muhammad Naveed Iqbal
    Cho, Dongrae
    Lee, Boreom
    [J]. 2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2017, : 2097 - 2100
  • [6] A High Performance Approach for Classification of Motor Imagery EEG
    Abbas, Waseem
    Khan, Nadeem Ahmad
    [J]. 2018 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS): ADVANCED SYSTEMS FOR ENHANCING HUMAN HEALTH, 2018, : 215 - 218
  • [7] Classification of Motor Imagery Based on Hybrid Features of Bispectrum of EEG
    Bordoloi, Simanta
    Sharmah, Ujjal
    Hazarika, Shyamanta M.
    [J]. PROCEEDINGS OF THE 2012 INTERNATIONAL CONFERENCE ON COMMUNICATIONS, DEVICES AND INTELLIGENT SYSTEMS (CODLS), 2012, : 113 - 116
  • [8] EEG based Motor imagery classification using instantaneous phase difference sequence
    Kumar, Satyam
    Reddy, Tharun
    Behera, Laxmidhar
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2018, : 499 - 504
  • [9] Performance Evaluation of Compressed Deep CNN for Motor Imagery Classification using EEG
    Vishnupriya, R.
    Robinson, Neethu
    Reddy, Ramasubba M.
    Guan, Cuntai
    [J]. 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), 2021, : 795 - 799
  • [10] Motor Imagery EEG Classification Using Capsule Networks
    Ha, Kwon-Woo
    Jeong, Jin-Woo
    [J]. SENSORS, 2019, 19 (13)