Search Results - (( using eeg classification algorithm ) OR ( java simulation optimization algorithm ))

Search alternatives:

Refine Results
  1. 1
    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
    “…Many peak classification algorithms have been introduced for various EEG signals applications. …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  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. …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  3. 3
    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.…”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  4. 4
    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
    “…These indicate that both methods give good discrimination of the eye state condition but on it own, will not be sufficient to produce good classification accuracy. Hence, this work aims to develop an algorithm using statistical-CSP feature for eye state classification from EEG signal. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  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
    “…These indicate that both methods give good discrimination of the eye state condition but on it own, will not be sufficient to produce good classification accuracy. Hence, this work aims to develop an algorithm using statistical-CSP feature for eye state classification from EEG signal. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  6. 6
    Classification of left/right hand movement from EEG signal by intelligent algorithms

    Classification of left/right hand movement from EEG signal by intelligent algorithms by Baig, M.Z., Javed, E., Ayaz, Y., Afzal, W., Gillani, S.O., Naveed, M., Jamil, M.

    Published 2015
    “…We have given a relative study of currently used classification algorithms along with a new approach for classification i.e. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  7. 7
    BRAIN ACTIVITIES FOR MOTOR MOVEMENT

    BRAIN ACTIVITIES FOR MOTOR MOVEMENT by ., WAFAA ELSAYED ELBASTY

    Published 2012
    “…The research covers the procedure of designing the BCI algorithm and this consists of three stages firstly recording EEG brain signals, secondly EEG signals pre-processing, Last stage is EEG signals classification. …”
    Get full text
    Get full text
    Final Year Project
    Save to List
    Saved in:
  8. 8
    Embedded Fuzzy Classifier for Detection and Classification of Preseizure state using Real EEG data

    Embedded Fuzzy Classifier for Detection and Classification of Preseizure state using Real EEG data by Qidwai, Uvais, Malik, Aamir Saeed, Shakir, Mohamed

    Published 2014
    “…The algorithm also utilizes certain statistical features from the EEG signal that are used as features to the classifier logic. …”
    Get full text
    Get full text
    Book Section
    Save to List
    Saved in:
  9. 9
    Embedded fuzzy classifier for detection and classification of preseizure state using real EEG data

    Embedded fuzzy classifier for detection and classification of preseizure state using real EEG data by Qidwai, U., Malik, A.S., Shakir, M.

    Published 2014
    “…The algorithm also utilizes certain statistical features from the EEG signal that are used as features to the classifier logic. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  10. 10
    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. …”
    Get full text
    Article
    Save to List
    Saved in:
  11. 11
    Classification of multichannel EEG signal by single layer perceptron learning algorithm

    Classification of multichannel EEG signal by single layer perceptron learning algorithm by Hasan, Mohammad Rubaiyat, Ibrahimy, Muhammad Ibn, Motakabber, S. M. A., Shahid, Shahjahan

    Published 2014
    “…Motor imagery (MI) related Electroencephalogram (EEG) signal classification is one of the main challenge in designing a brain computer interface (BCI) system. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Proceeding Paper
    Save to List
    Saved in:
  12. 12
    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
    “…The main motivation of this study is to find out techniques that may improve EEG signal classification. SAX algorithm may bring improvement to classic time series classification, so we investigate it`s impact on EEG signal classification. …”
    Get full text
    Thesis
    Save to List
    Saved in:
  13. 13
    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. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  14. 14
    Functional link PSO neural network based classification of EEG mental task signals

    Functional link PSO neural network based classification of EEG mental task signals by Hema, Chengalvarayan Radhakrishnamurthy, Paulraj, Murugesapandian, Sazali, Yaacob, Abdul Hamid, Adom, Nagarajan, Ramachandran

    Published 2009
    Get full text
    Working Paper
    Save to List
    Saved in:
  15. 15
    Embedded Fuzzy Classifier for Detection and Classification of Preseizure State Using Real EEG Data

    Embedded Fuzzy Classifier for Detection and Classification of Preseizure State Using Real EEG Data by Qidwai, Uvais, Malik, Aamir Saeed, Shakir, Mohamed

    Published 2013
    “…The algorithm also utilizes certain statistical features from the EEG signal that are used as features to the classifier logic. …”
    Get full text
    Get full text
    Get full text
    Book Section
    Save to List
    Saved in:
  16. 16
    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
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  17. 17
    Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs

    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
    “…Finally, the k-nearest neighbors ( kNN) classification technique was used for automatic gender identification of an emotional-based EEG dataset. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  18. 18
    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
    “…Besides, common spatial pattern (CSP) is the well-known method for classification algorithm in the BCI field. However, application of CSP in EEG eye state classification considered uncommon as compared to motor imagery classification. …”
    Get full text
    Get full text
    Final Year Project
    Save to List
    Saved in:
  19. 19
    EEG Spectrogram Classification Employing ANN for IQ Application

    EEG Spectrogram Classification Employing ANN for IQ Application by Mahfuzah, Mustafa, Mohd Nasir, Taib, Sahrim, Lias, Zunairah, Murat, Norizam, Sulaiman

    Published 2013
    “…Then, Principal Component Analysis (PCA) is used to reduce the big matrix, and is followed with the classification of the EEG spectrogram image in IQ application using ANN algorithm. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  20. 20
    kNN and SVM classification for EEG: a review

    kNN and SVM classification for EEG: a review by Fuad, N., Sha'abani, M.N.A.H., Jamal, Norezmi, Ismail, M.F.

    Published 2020
    “…Moreover, most EEG applications involve high dimensional feature vector. kNN and SVM were used in EEG classification and has been proven successfully in discriminating features in EEG dataset. …”
    Get full text
    Get full text
    Book Section
    Save to List
    Saved in:

Search Tools: