Search Results - (( developing eeg _ algorithm ) OR ( java implication based algorithm ))

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

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

“…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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Multivariate EEG signal processing techniques for the aid of severely disabled people by Ibrahimy, Muhammad Ibn, Ibrahimy, Ahmad Ibn

“…Therefore, the proposed research work is involved with three or more channels of EEG signal for online BCI. Two fundamental objectives for BCI based on motor movement imagery from multichannel signals are aimed at in this research work: i) to develop a technique of multivariate feature extraction for motor imagery related to multichannel EEG signals; and ii) to develop an appropriate machine learning based feature classification algorithm for Brain Computer Interface. …”
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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

“…This can help in developing wearable EEG Seizure monitoring system(WBAN-HL7). …”
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Epileptic Seizure Detection Using Singular Values And Classical Features Of EEG Signals by Ahmed, Ahmed Elsayed Elmahdy

“…This project aims at developing an automated epileptic seizure event detection algorithm. …”
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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

“…EEG source localization is determining possible cortical sources of brain activities with scalp EEG. …”
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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

“…EEG source localization is determining possible cortical sources of brain activities with scalp EEG. …”
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EEG EYE STATE IDENTIFICATION BASED ON STATISTICAL FEATURES AND COMMON SPATIAL PATTERN by WANG, CHIA WOON

“…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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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

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

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Dingle's Model-based EEG Peak Detection using a Rule-based Classifier by Asrul, Adam, Norrima, Mokhtar, Marizan, Mubin, Zuwairie, Ibrahim, Mohd Ibrahim, Shapiai

“…In this study, the performances of four different peak models of time domain approach which are Dumpala's, Acir's, Liu's, and Dingle's peak models are evaluated for electroencephalogram (EEG) signal peak detection algorithm. The algorithm is developed into three stages: peak candidate detection, feature extraction, and classification. …”
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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

“…Therefore, it is crucial to develop EEG channel selection algorithms that enable the creation of a wearable device capable of assessing mental stress in real-life scenarios. …”
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K-means Clustering Analysis for EEG Features of Situational Interest Detection in Classroom Learning by Othman, E.S., Faye, I., Babiker, A., Hussaan, A.M.

“…This paper proposes a method to detect situational interest in classroom learning using k-means algorithms. The developed algorithm in this paper had been tested on features from ten students who experienced mathematics learning in a classroom. …”
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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

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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

“…This paper presents an algorithm for extracting underlying frequency components of massive Electroencephalogram (EEG) data. …”
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Machine learning approach for stress detection based on alpha-beta and theta-beta ratios of EEG signals by Altaf, Hunain, Ibrahim, Siti Noorjannah, Mohd Azmin, Nor Fadhillah, Asnawi, Ani Liza, Walid, Balqis Hanisah, Harun, Noor Hasmiza

“…This study will ultimately contribute to society's development with improved robust machine learning algorithm for binary classification.…”
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Bio-signal identification using simple growing RBF-network (OLACA) by Asirvadam , Vijanth Sagayan, McLoone, Sean, Palaniappan, R

“…These algorithms are developed primarily for applications with fast sampling rate which demands significant reduction in computation load per iteration. …”
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A proposed frame work for real time epileptic seizure prediction using scalp EEG by Ahmad, R.F., Malik, A.S., Kamel, N., Reza, F.

“…Our aim is to study and develop a good epileptic seizure prediction algorithm/method with high value of sensitivity and specificity using scalp EEG i-e noninvasive approach. …”
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