Search Results - (( java implementation based algorithm ) OR ( using eeg machine 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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EEG-based emotion recognition using machine learning algorithms by Lam, Yee Wei

“…Thus, this project proposed an optimised machine learning algorithms to classify emotion by analysing brain activity using Electroencephalogram (EEG) signals. …”
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Brain Machine Interface Controlled Robot Chair by Hema Chengalvarayan, Radhakrishnamurthy

“…The BMI controls the joystick of the robot chair using a shared control algorithm. Real-time experiments are also presented using 10 trained and 5 untrained subjects to validate the applicability of the brain machine interface. …”
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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 work explores the impact of bandpower of alpha/beta and theta/beta ratios when combined with other features to classify two-levels of human stress based on EEG signals using five commonly used machine learning algorithms. …”
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Fatigue State Detection Through Multiple Machine Learning Classifiers Using EEG Signal by Hasan, Md Mahmudul, Hossain, Mirza Mahfuj, Norizam, Sulaiman

“…This study is conducted to provide a comprehensive and reliable fatigue state detection system to avoid accidents and make a good decision. Three machine learning algorithms were applied to seventy-six subjects' electroencephalogram (EEG) readings to test their performance. …”
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Detection of eye movements based on EEG signals and the SAX algorithm by Shanmuga, P. M. M., Lau, Sian Lun *, Jou, Chichang.

“…We would like to investigate another technique, namely the Symbolic Aggregate Approximation (SAX) algorithm, to find out its suitability and performance against known classification algorithms such as Support Vector Machine (SVM), k-Nearest Neighbour (KNN) and Decision Tree (DT).…”
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EEG-Based Brain-Machine Interface (BMI) for Controlling Mobile Robots: The Trend of Prior Studies by Murali Krishnan, Muralindran Mariappan

“…In this paper, we review the background, feature extraction and classification algorithms used to design the Electroencephalography (EEG) based Brain-Machine Interface (BMI) to control the mobile robots.…”
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Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks by Hema, Chengalvarayan Radhakrishnamurthy, Paulraj, Murugesa Pandiyan, Assoc. Prof., Nagarajan, Ramachandran, Prof. Dr., Sazali, Yaacob, Prof. Dr., Abdul Hamid, Adom, Prof. Madya

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The classification of EEG signal processing using different machine learning techniques for BCI application by Rashid, Mamunur, Norizam, Sulaiman, Mahfuzah, Mustafa, Sabira, Khatun, Bari, Bifta Sama

“…Finally, the translation algorithm will be con-structed using selected and classified EEG features to control the BCI devices.…”
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Brain machine interfaces: recognition of mental tasks using neural networks and PSO learning algorithms / Hema C.R. ...[et al.] by C.R., Hema, M.P., Paulraj, Yaacob, S., Adom, A.H., R., Nagarajan

“…BMIs are designed using the electrical activity of the brain detected by scalp EEG electrodes. …”
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EEG signal classification using Particle Swarm Optimization (PSO) neural network for brain machine interfaces by Paulraj, Murugesapandian, Hema, Chengalvarayan Radhakrishnamurthy, Ramachandran, Nagarajan, Sazali, Yaacob, Abdul Hamid, Adom

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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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Machine Learning-Based Stress Level Detection from EEG Signals by Nirabi, Ali, Abd Rhman, Faridah, Habaebi, Mohamed Hadi, Sidek, Khairul Azami, Yusoff, Siti Hajar

“…This paper presented a system to detect the stress level from the EEG signals using machine learning algorithms. …”
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Evaluation of rehearsal effects of multimedia content based on EEG using machine learning algorithms by Mazher, M., Aziz, A.A., Malik, A.S.

“…This paper will present the rehearsal effects based on electroencephalography (EEG) recorded data for multimedia contents. Three frequency based features are used to discriminate the three learning states mentioned as L1, L2 and L3 using machine learning algorithms. …”
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Empirical Analysis of Intra vs. Inter-Subject Variability in VR EEG-Based Emotion Modelling by N.S. Suhaimi, J. Teo, J. Mountstephens

“…This study presents the classification of emotions on EEG signals using commercial BCI headsets known as wearable EEG. …”
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RSA Encryption & Decryption using JAVA by Ramli, Marliyana

“…Today, with online marketing, banking, healthcare and other services, even the average householder is aware of encryption. 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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Predicting game-induced emotions using EEG, data mining and machine learning by Min, Xuan Lim, Jason Teo

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Brain machine interface: classification of mental tasks using short-time PCA and recurrent neural networks by Hema, Chengalvarayan Radhakrishnamurthy, Paulraj, Murugesapandian, Sazali, Yaacob, Abd Hamid, Adom, Ramachandran, Nagarajan

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Fuzzy based classification of EEG mental tasks for a brain machine interface by Hema, Chengalvarayan Radhakrishnamurthy, Paulraj, Murugesapandian, Ramachandran, Nagarajan, Sazali, Yaacob, Abdul Hamid, Adom

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Drowsiness Detection Using Ocular Indices from EEG Signal by Tarafder, S., Badruddin, N., Yahya, N., Nasution, A.H.

“…In this study, we used the BLINKER algorithm to extract 25 blink-related features from a public dataset comprising raw EEG signals collected from 12 participants. …”
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