Search Results - (( using eeg based algorithm ) OR ( using optimization method algorithm ))

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  1. 1

    Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs by Al-Qazzaz, Noor Kamal, Sabir, Mohannad K., Mohd Ali, Sawal Hamid, Ahmad, Siti Anom, Grammer, Karl

    Published 2021
    “…Secondly, it aims to develop an automatic gender recognition model by employing optimization algorithms to identify the most effective channels for gender identification from emotional-based EEG signals. …”
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  2. 2

    Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam by Asrul, Adam

    Published 2017
    “…In the preliminary study, the algorithm is evaluated on the four different peak models of the three EEG signals using the artificial neural network (ANN) with particle swarm optimization (PSO) as learning algorithm. …”
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  3. 3

    Optimal input features selection of wavelet-based EEG signals using GA by Mohd. Daud, Salwani, Yunus, Jasmy

    Published 2004
    “…We present a method of selecting optimal input features from wavelet coefficients of electroencephalogram (EEG) signals. …”
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  4. 4

    Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh by Sai , Chong Yeh

    Published 2020
    “…All in all, this study aims to design, develop, optimize and test the method of pain assessment using the EEG signal during the active contraction phase of the first stage of labour. …”
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  5. 5

    Bayesian Framework based Brain Source Localization Using High SNR EEG Data by Jatoi, M.A., Kamel, N., Gaho, A.A., Dharejo, F.A.

    Published 2019
    “…These sources can be localized using different optimization algorithms. This localization information is usable for diagnoses of brain disorders such as epilepsy, Schizophrenia, depression and Alzheimer. …”
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  6. 6

    P300 detection of brain signals using a combination of wavelet transform techniques by Motlagh, Farid Esmaeili

    Published 2012
    “…In this research the BCI competition data-set has been processed through 5 optimized detection methods. Wavelet transform (WT), student’s two-sample t-statistic (T-Test) and support vector machines (SVM) used in designing the algorithms. …”
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  7. 7
  8. 8

    Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition by Al-Qammaz, Abdullah Yousef, Ahmad, Farzana Kabir, Yusof, Yuhanis

    Published 2018
    “…Due to some limitations of current heuristics and evolutionary algorithms, this paper proposed a new swarm based algorithm for feature selection method called Social Spider Optimization (SSO-FS). …”
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  9. 9
  10. 10

    Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition by Olakunle, Oyenuga Wasiu

    Published 2015
    “…However, the challenging issues regarding EEG-based emotional state recognition is that it requires well-designed methods and algorithms to extract necessary features from the complex, chaotic, and multichannel EEG signal in order to achieve optimum classification performance. …”
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  11. 11

    Electroencephalogram signal interpretation system for mobile robot by Hasan, Intan Helina

    Published 2013
    “…Using sample datasets, the EEG signal is analyzed to determine the most suitable scalp area for P300 detection, while optimization with genetic algorithm (GA) is developed to select best four channels. …”
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  13. 13

    The classification of wink-based eeg signals by means of transfer learning models by Jothi Letchumy, Mahendra Kumar

    Published 2021
    “…The implementation of pre-processing algorithms has been demonstrated to be able to mitigate the signal noises that arises from the winking signals without the need for the use signal filtering algorithms. …”
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  14. 14
  15. 15

    Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA by Javed, E., Faye, I., Malik, A.S., Abdullah, J.M.

    Published 2017
    “…Comparison with existing methods Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy. …”
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  16. 16

    Drowsiness Detection Using Ocular Indices from EEG Signal by Tarafder, S., Badruddin, N., Yahya, N., Nasution, A.H.

    Published 2022
    “…In this study, we used the BLINKER algorithm to extract 25 blink-related features from a public dataset comprising raw EEG signals collected from 12 participants. …”
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  17. 17

    Sleep arousal events detection using PNN-GBMO classifier based on EEG and ECG signals: A hybrid-learning model by Afsoon Badiei, Saeed Meshgini, Ali Farzamnia

    Published 2020
    “…A subset of the features is then applied into the probabilistic neural network optimized by Gases Brownian Motion Optimization (GBMO) algorithm. …”
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  18. 18

    Task-state EEG signal classification for spatial cognitive evaluation based on multiscale high-density convolutional neural network by Wen, Dong, Li, Rou, Tang, Hao, Liu, Yijun, Wan, Xianglong, Dong, Xianling, Saripan, M. Iqbal, Lan, Xifa, Song, Haiqing, Zhou, Yanhong

    Published 2022
    “…Besides, the multi-dimensional conditional mutual information method was used to extract the frequency band features of the EEG data. …”
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  19. 19

    A Time-Domain Subspace Technique for Estimating Visual Evoked Potential Latencies by Yusoff, Mohd Zuki, Kamel, Nidal

    Published 2010
    “…Further, GSA has been compared with a third-order correlation (TOC) method, using both realistic simulation and real patient data gathered in a hospital. …”
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  20. 20

    A Subspace Approach for Extracting Signals Highly Corrupted by Colored Noise by Yusoff, Mohd Zuki, Hussin, Fawnizu Azmadi

    Published 2010
    “…The simulation results produced by the post-modified SSA2 algorithm, show a higher degree of consistencies in detecting the VEP's P100, P200, and P300 peaks, in comparisons to the pre-modified SSA1 method. …”
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