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  1. 1
    Hardware Simulator for Seizure, Preseizure and Normal Mode Signal Generation in LabVIEW Environment for Research

    Hardware Simulator for Seizure, Preseizure and Normal Mode Signal Generation in LabVIEW Environment for Research by Qidwai, Uvais, Malik, Aamir Saeed, Shakir, Mohamed, Kamel, Nidal

    Published 2013
    “…Here we develop a hardware simulator of the EEG model or to simulator any EEG data set in either .edf or .tdmsot .txtformat from any patient or database depository. …”
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  2. 2
    Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh

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

    Published 2020
    “…The training and parameters selection of the machine learning algorithms are conducted using EEG data collected from ten subjects in the laboratory. …”
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  3. 3
    An enhanced computational model based on social spider optimisation algorithm for EEG- based emotion recognition

    An enhanced computational model based on social spider optimisation algorithm for EEG- based emotion recognition by Al-Qammaz, Abdullah Yousef Awwad

    Published 2019
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  4. 4
    Extraction of Inherent Frequency Components of Multiway EEG Data Using Two-Stage Neural Canonical Correlation Analysis

    Extraction of Inherent Frequency Components of Multiway EEG Data Using Two-Stage Neural Canonical Correlation Analysis by W Omar Ali Saifuddin, Wan Ismail, A. N. M. Enamul, Kabir

    Published 2014
    “…This paper presents an algorithm for extracting underlying frequency components of massive Electroencephalogram (EEG) data. …”
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  5. 5
    EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter

    EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter by Woon, W.C., Yahya, N., Badruddin, N.

    Published 2019
    “…Hence, this work aims to develop an algorithm using statistical-CSP feature for eye state classification from EEG signal. …”
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  6. 6
    EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter

    EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter by Woon, W.C., Yahya, N., Badruddin, N.

    Published 2019
    “…Hence, this work aims to develop an algorithm using statistical-CSP feature for eye state classification from EEG signal. …”
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  7. 7
    Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization

    Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization by Khosropanah, Pegah, Ramli, Abdul Rahman, Lim, Kheng Seang, Marhaban, Mohammad Hamiruce, Ahmedov, Anvarjon

    Published 2018
    “…Generally, every step of the data processing sequence affects the accuracy of EEG source localization. …”
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  8. 8
    Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization

    Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization by Khosropanah, Pegah, Ramli, Abdul Rahman, Lim, Kheng Seang, Marhaban, Mohammad Hamiruce, Ahmedov, Anvarjon

    Published 2018
    “…Generally, every step of the data processing sequence affects the accuracy of EEG source localization. …”
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  9. 9
    RSA Encryption & Decryption using JAVA

    RSA Encryption & Decryption using JAVA by Ramli, Marliyana

    Published 2006
    “…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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  10. 10
    Bayesian Framework based Brain Source Localization Using High SNR EEG Data

    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
    “…This research work discusses the results based on synthetically generated EEG data at an SNR level of 12 dB with Gaussian noise added linearly in data matrix. …”
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  11. 11
    “Analysis of EEG Recordings During Grasp and Lift (GAL) Trials

    “Analysis of EEG Recordings During Grasp and Lift (GAL) Trials by Musa, Huwaida

    Published 2018
    “…The purpose of analysing this EEG data is to help develop a prosthetic device that can control an upper limb and generate a power grasp or a pinch grasp involving the thumb and index finger. …”
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  12. 12
    EEG-based fatigue detection using binary pattern analysis and KNN algorithm

    EEG-based fatigue detection using binary pattern analysis and KNN algorithm by Abd Rahman, Faridah, Roslizan, Iszan Uwais Amer, Gunawan, Teddy Surya, Fitriawan, Helmy, Kartiwi, Mira, Habaebi, Mohamed Hadi

    Published 2024
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  13. 13
    Fuzzification of epileptic data: an application for prediction and identification of partial seizure

    Fuzzification of epileptic data: an application for prediction and identification of partial seizure by Malik, Aamir Saeed, Nasif, Mohammad Shakir, Kamel , Nidal, Qidwai, U.

    Published 2013
    “…Hence in this proposed system, we introduce fuzzification of epileptic EEG data using fuzzy logic interface to classify partial seizure EEG signals from the normal EEG. …”
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  14. 14
    Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool

    Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool by Nurshafiqa Saffah, Mohd Sharif

    Published 2018
    “…Classification and prediction are the functions provided by the data mining techniques that suit in EEG signal processing. …”
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  15. 15
    Bio-signal identification using simple growing RBF-network (OLACA)

    Bio-signal identification using simple growing RBF-network (OLACA) by Asirvadam , Vijanth Sagayan, McLoone, Sean, Palaniappan, R

    Published 2007
    “…These algorithms are developed primarily for applications with fast sampling rate which demands significant reduction in computation load per iteration. …”
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  16. 16
    Improving EEG Signal Peak Detection Using Feature Weight Learning of a Neural Network with Random Weights for Eye Event-Related Applications

    Improving EEG Signal Peak Detection Using Feature Weight Learning of a Neural Network with Random Weights for Eye Event-Related Applications by Asrul, Adam, Zuwairie, Ibrahim, Norrima, Mokhtar, Mohd Ibrahim, Shapiai, Cumming, Paul, Marizan, Mubin

    Published 2017
    “…The optimization of peak detection algorithms for electroencephalogram (EEG) signal analysis is an ongoing project; previously existing algorithms have been used with different models to detect EEG peaks in various applications. …”
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  17. 17
    Epileptic seizure detection using the singular values of EEG signals

    Epileptic seizure detection using the singular values of EEG signals by Shahid, Arslan, Kamel, Nidal, Malik, Aamir Saeed, Jatoi, Munsif Ali

    Published 2013
    “…EEG recordings of 4-paediatric patients with 20 seizures are used to validate the proposed algorithm and the preliminary results indicates good level of sensitivity by the singular values to the changes in the EEG signals due to epileptic seizure. …”
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  18. 18
    Brain source localization using reduced EEG sensors

    Brain source localization using reduced EEG sensors by Jatoi, M.A., Kamel, N.

    Published 2018
    “…The results are shown valid for synthetic EEG data at low SNR level, i.e., 5 dB and real-time EEG data, respectively. …”
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  19. 19
    Brain source localization using reduced EEG sensors

    Brain source localization using reduced EEG sensors by Jatoi, M.A., Kamel, N.

    Published 2018
    “…The results are shown valid for synthetic EEG data at low SNR level, i.e., 5 dB and real-time EEG data, respectively. …”
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  20. 20
    Cognitive load assessment through EEG: a dataset from arithmetic and stroop tasks

    Cognitive load assessment through EEG: a dataset from arithmetic and stroop tasks by Nirabi, Ali, Abd Rahman, Faridah, Habaebi, Mohamed Hadi, Sidek, Khairul Azami, Yusoff, Siti Hajar

    Published 2025
    “…By providing a solid foundation for the development of algorithms capable of detecting and classifying stress levels, the dataset supports innovations in non-invasive monitoring tools and contributes to personalized healthcare solutions that can adapt to the cognitive states of users. …”
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