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  1. 1
    Swarm negative selection algorithm for electroencephalogram signals classification

    Swarm negative selection algorithm for electroencephalogram signals classification by Sahel Ba-Karait, Nasser Omer, Shamsuddin, Siti Mariyam, Sudirman, Rubita

    Published 2009
    “…The SNS classification model use negative selection and PSO algorithms to form a set of memory Artificial Lymphocytes (ALCs) that have the ability to distinguish between normal and epileptic EEG patterns. …”
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  2. 2
    Mental stress classification based on selected EEG channels using Correlation Coefficient of Hjorth Parameters

    Mental stress classification based on selected EEG channels using Correlation Coefficient of Hjorth Parameters by Hag, Ala, Fares, Al-Shargie, Handayani, Dini Oktarina Dwi, Houshyar, Asadi

    Published 2023
    “…To evaluate the effectiveness of CCHP, we conducted experiments using the DEAP public dataset. Comparing our results with other recent algorithms that utilize the full set of EEG channels, CCHP achieved a superior classification accuracy of 81.56% using only eight EEG channels. …”
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  3. 3
    Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam

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

    Published 2017
    “…However, the developed algorithms only consider the selected features from a peak model based on the understanding of the EEG signals characteristics. …”
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  4. 4
    Optimal input features selection of wavelet-based EEG signals using GA

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

    Published 2004
    “…A combination of genetic algorithm (GA) and artificial neural network (ANN) are used to select the relevant features. …”
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  5. 5
    Feature Selection using Binary Simulated Kalman Filter for Peak Classification of EEG Signals

    Feature Selection using Binary Simulated Kalman Filter for Peak Classification of EEG Signals by Badaruddin, Muhammad, Mohd Falfazli, Mat Jusof, Mohd Ibrahim, Shapiai, Asrul, Adam, Zulkifli, Md. Yusof, Kamil Zakwan, Mohd Azmi, Nor Hidayati, Abdul Aziz, Zuwairie, Ibrahim, Norrima, Mokhtar

    Published 2018
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  6. 6
    Eeg-Based Person Identification Using Multi-Levelwavelet Decomposition With Multi-Objective Flower Pollination Algorithm

    Eeg-Based Person Identification Using Multi-Levelwavelet Decomposition With Multi-Objective Flower Pollination Algorithm by Yahya Alyasseri, Zaid Abdi Alkareem

    Published 2020
    “…For addressing these points, a novel person identification method that is using EEG with multi-level wavelet decomposition and multi-objective flower pollination algorithm is proposed in this thesis. …”
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  7. 7
    Random subspace K-NN based ensemble classifier for driver fatigue detection utilizing selected EEG channels

    Random subspace K-NN based ensemble classifier for driver fatigue detection utilizing selected EEG channels by Rashid, Mamunur, Mahfuzah, Mustafa, Norizam, Sulaiman, Nor Rul Hasma, Abdullah, Rosdiyana, Samad

    Published 2021
    “…In the present framework, a new channel selection algorithm based on correlation coefficients and an ensemble classifier based on random subspace k-nearest neighbour (k-NN) has been presented to enhance the classification performance of EEG data for driver fatigue detection. …”
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  8. 8
    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…The simulation is implemented with iFogSim and java programming language. …”
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  9. 9
    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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  10. 10
    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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  11. 11
    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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  12. 12
    Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks

    Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks by Abualsaud, Khalid, Mahmuddin, Massudi, Saleh, Mohammad, Mohamed, Amr

    Published 2014
    “…Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems.Classification is the most important technique for wide-ranging applications to categorize the items according to its features with respect to predefined set of classes.In this paper, we conduct a performance evaluation based on the noiseless and noisy EEG-based epileptic seizure data using various classification algorithms including BayesNet, DecisionTable, IBK, J48/C4.5, and VFI.The reconstructed and noisy EEG data are decomposed with discrete cosine transform into several sub-bands.In addition, some of statistical features are extracted from the wavelet coefficients to represent the whole EEG data inputs into the classifiers.Benchmark on widely used dataset is utilized for automatic epileptic seizure detection including both normal and epileptic EEG datasets.The classification accuracy results confirm that the selected classifiers have greater potentiality to identify the noisy epileptic disorders.…”
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  13. 13
    Effective EEG channels for emotion identification over the brain regions using differential evolution algorithm

    Effective EEG channels for emotion identification over the brain regions using differential evolution algorithm by Al-Qazzaz, Noor Kamal, Sabir, Mohannad K., Md. Ali, Sawal Hamid, Ahmad, Siti Anom, Grammer, Karl

    Published 2019
    “…The differential evolution-based channel selection algorithm (DEFS_Ch) was computed to find the most suitable EEG channels that have the greatest efficacy for identifying the various emotional states of the brain regions. …”
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  14. 14
    Affective computation on EEG correlates of emotion from musical and vocal stimuli

    Affective computation on EEG correlates of emotion from musical and vocal stimuli by Khosrowabadi, Reza, Abdul Rahman, Abdul Wahab, Ang, Kai Keng, H Baniasad, Mohammad.

    Published 2009
    “…A classification algorithm is subsequently used to learn and classify the extracted EEG features. …”
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  15. 15
    Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm

    Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm by Zuwairie, Ibrahim, Mohd Zaidi, Mohd Tumari, Asrul, Adam, Norrima, Mokhtar, Marizan, Mubin, Mohd Ibrahim, Shapiai

    Published 2014
    “…This study focuses on using GSA method, a new computational intelligence algorithm. …”
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  16. 16
    EEG EYE STATE IDENTIFICATION BASED ON STATISTICAL FEATURES AND COMMON SPATIAL PATTERN

    EEG EYE STATE IDENTIFICATION BASED ON STATISTICAL FEATURES AND COMMON SPATIAL PATTERN by WANG, CHIA WOON

    Published 2019
    “…Hence, the proposed work aimed to analyse the EEG eye state signal as well as develop an algorithm using statistical-CSP features on the eye state identification.…”
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  17. 17
    Applying SAX-based time series analysis to classify EEG signal using a COTS EEG device

    Applying SAX-based time series analysis to classify EEG signal using a COTS EEG device by Shanmuga, Pillai A/L Murutha Muthu

    Published 2021
    “…In order to make BCI useful, one of the approaches is to classify the EEG time series signal that may indicate given eye movements that will be used as input instructions to a device. …”
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  18. 18
    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing

    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

    Published 2011
    “…Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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  19. 19
    Ant colony optimization algorithm for load balancing in grid computing

    Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2012
    “…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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  20. 20
    P300 detection of brain signals using a combination of wavelet transform techniques

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

    Published 2012
    “…Meanwhile the new approaches in channels selection methods help the algorithms for convenient online usage.…”
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