Multiscale entropy analysis to quantify the dynamics of motor movement signals with fist or feet movement using topographic maps

被引:2
|
作者
Hussain, Lal [1 ]
Aziz, Wajid [1 ,2 ]
Alshdadi, Abdulrahman A. [2 ]
Abbasi, Adeel Ahmed [1 ]
Majid, Abdul [1 ]
Marchal, Ali Raza [1 ]
机构
[1] Univ Azad Jammu & Kashmir, Dept Comp Sci & IT, City Campus, Muzaffarabad 13100, Pakistan
[2] Univ Jeddah, Coll Comp Sci & Engn, Jeddah 23890, Saudi Arabia
关键词
Electroencephalography (EEG); multiscale sample entropy (MSE); multiscale wavelet entropy (MWE); TIME-SERIES ANALYSIS; EYE-OPEN; APPROXIMATE ENTROPY; COMPLEXITY ANALYSIS; EPILEPTIC SEIZURE; STRIDE INTERVAL; EEG; CLASSIFICATION; COHERENCE; MACHINES;
D O I
10.3233/THC-191803
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
BACKGROUND: Brain neural activity is measured using electroencephalography (EEG) recording from the scalp. The EEG motor/imagery tasks help disabled people to communicate with the external environment. OBJECTIVE: In this paper, robust multiscale sample entropy (MSE) and wavelet entropy measures are employed using topographic maps' analysis and tabulated form to quantify the dynamics of EEG motor movements tasks with actual and imagery opening and closing of fist or feet movements. METHODS: To distinguish these conditions, we used the topographic maps which visually show the significance level of the brain regions and probes for dominant activities. The paired t-test and Posthoc Tukey test are used to find the significance levels. RESULTS: The topographic maps results obtained using MSE reveal that maximum electrodes show the significance in frontpolar, frontal, and few frontal and parietal brain regions at temporal scales 3, 4, 6 and 7. Moreover, it was also observed that the distribution of significance is from frontoparietal brain regions. Using wavelet entropy, the significant results are obtained at frontpolar, frontal, and few electrodes in right hemisphere. The highest significance is obtained at frontpolar electrodes followed by frontal and few central and parietal electrodes.
引用
收藏
页码:259 / 273
页数:15
相关论文
共 9 条
  • [1] Performance Analysis of HybridA- BCI Signals Using CNN for Motor Movement Classification
    Shelishiyah, R.
    Thiyam, Deepa Beeta
    TRAITEMENT DU SIGNAL, 2024, 41 (04) : 2143 - 2152
  • [2] Complexity analysis of EEG motor movement with eye open and close subjects using multiscale permutation entropy (MPE) technique.
    Hussain, Lal
    Aziz, Wajid
    Saeed, Sharjil
    Shah, Saeed Arif
    Nadeem, Malik Sajjad A.
    Awan, Imtiaz Ahmed
    Abbas, Ali
    Majid, Abdul
    Kazmi, Syed Zaki Hassan
    BIOMEDICAL RESEARCH-INDIA, 2017, 28 (16): : 7104 - 7111
  • [3] Using Non-linear Dynamics of EEG Signals to Classify Primary Hand Movement Intent Under Opposite Hand Movement
    Wang, Jiarong
    Bi, Luzheng
    Fei, Weijie
    FRONTIERS IN NEUROROBOTICS, 2022, 16
  • [4] A Neural Analysis on Motor Imagery and Passive Movement Using a Haptic Device
    Kang, Hagil
    Park, Wanjoo
    Kang, Jae-Hwan
    Kwon, Gyu-Hyun
    Kim, Sung-Phil
    Kim, Laehyun
    2012 12TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS), 2012, : 1536 - 1541
  • [5] Analysis of EEG Signals Contaminated With Motion Artifacts Using Multiscale Modified-Distribution Entropy
    Aung, Si Thu
    Wongsawat, Yodchanan
    IEEE ACCESS, 2021, 9 : 33911 - 33921
  • [6] EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks
    Liu, Quan
    Chen, Yi-Feng
    Fan, Shou-Zen
    Abbod, Maysam F.
    Shieh, Jiann-Shing
    COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2015, 2015
  • [7] Movement intention detection from SEMG signals using time-domain features and discriminant analysis classifiers
    Herle, S.
    2018 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION, QUALITY AND TESTING, ROBOTICS (AQTR), 2018,
  • [8] Decoding Voluntary Movement o f Single Hand Based on Analysis of Brain Connectivity by Using EEG Signals
    Li, Ting
    Xue, Tao
    Wang, Baozeng
    Zhang, Jinhua
    FRONTIERS IN HUMAN NEUROSCIENCE, 2018, 12
  • [9] Inter-Trial Analysis of Post-Movement Beta Activities in EEG Signals Using Multivariate Empirical Mode Decomposition
    Chang, Hsiang-Chih
    Lee, Po-Lei
    Lo, Men-Tzung
    Wu, Yu-Te
    Wang, Kuo-Wei
    Lan, Gong-Yau
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2013, 21 (04) : 607 - 615