Search Results - (( java application using algorithm ) OR ( using eeg method algorithm ))

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

“…Future studies are envisioned to investigate EEG markers of pain in other clinical states, aiming to generalize the use of EEG as an objective method of pain assessment. …”
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Eeg-Based Person Identification Using Multi-Levelwavelet Decomposition With Multi-Objective Flower Pollination Algorithm by Yahya Alyasseri, Zaid Abdi Alkareem

“…The proposed method is tested using two standard EEG datasets, namely, Kiern’s and Motor Movement/Imagery.…”
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Real time ocular and facial muscle artifacts removal from EEG signals using LMS adaptive algorithm by Mehrkanoon, S., Moghavvemi, M., Fariborzi, H.

“…The EEG signal is most useful for clinical diagnosis and in biomedical research. …”
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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

“…High accuracy results of proposed algorithm using non-invasive and low-resolution EEG provide the potential of using this work for pre-surgical evaluation towards epileptogenic zone localization in clinics.…”
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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

“…High accuracy results of proposed algorithm using non-invasive and low-resolution EEG provide the potential of using this work for pre-surgical evaluation towards epileptogenic zone localization in clinics.…”
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Swarm negative selection algorithm for electroencephalogram signals classification by Sahel Ba-Karait, Nasser Omer, Shamsuddin, Siti Mariyam, Sudirman, Rubita

“…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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EEG-and MRI-based epilepsy source localization using multivariate empirical mode decomposition and inverse solution method by Khosropanah, Pegah

“…Since MEMD method is a data-driven method which meets the criteria to be applied for EEG processing, therefore this method was employed to extract EEG epileptic spike features. …”
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BRAIN ACTIVITIES FOR MOTOR MOVEMENT by ., WAFAA ELSAYED ELBASTY

“…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. …”
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Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion by Zafar, R., Dass, S.C., Malik, A.S.

“…The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. …”
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Single-trial visual evoked potential extraction using partial least-squares-based approach by Yanti, D.K., Yusoff, M.Z., Asirvadam, V.S.

“…For P100, the proposed PLS algorithm is able to provide comparable results to the generalized eigenvalue decomposition (GEVD) algorithm, which alters (prewhitens) the EEG input signal using the prestimulation EEG signal. …”
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Single-trial visual evoked potential extraction using partial least-squares-based approach by Yanti, D.K., Yusoff, M.Z., Asirvadam, V.S.

“…For P100, the proposed PLS algorithm is able to provide comparable results to the generalized eigenvalue decomposition (GEVD) algorithm, which alters (prewhitens) the EEG input signal using the prestimulation EEG signal. …”
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Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam by Asrul, Adam

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

“…Hence, this work aims to develop an algorithm using statistical-CSP feature for eye state classification from EEG signal. …”
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EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter by Woon, W.C., Yahya, N., Badruddin, N.

“…Hence, this work aims to develop an algorithm using statistical-CSP feature for eye state classification from EEG signal. …”
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Epileptic Seizure Detection Using Singular Values And Classical Features Of EEG Signals by Ahmed, Ahmed Elsayed Elmahdy

“…The proposed algorithm was tested through using CHB-MIT Scalp EEG Database which was recorded in the children hospital in Boston. …”
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Emotion Detection Based on EEG Signal by Mohamad Nasaruddin, Noradila

“…In this research, two class of emotion which are happy and sad are detected through EEG signal. Wavelet transform scalogram is used as feature extraction method. …”
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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

“…The electroencephalogram (EEG) is a tool that potentially can be used to detect gender differences. …”
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EEG-based brain source localization using visual stimuli by Jatoi, M.A., Kamel, N., Malik, A.S., Faye, I., Bornot, J.M., Begum, T.

“…According to the spatial resolution provided, the algorithms are categorized as either low resolution methods or high resolution methods. …”
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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

“…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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Deep learning for EEG data analysis by Cheah, Kit Hwa

“…In this project, deep neural network architectures have been constructed to perform binary classification on an EEG dataset that was shown by traditional EEG feature extraction methods to have no significant difference between its two data pools (resting EEG recorded before and recorded after listening to music). …”
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