Simulation study on artifact elimination in EEG signals by artificial neural network

A prey species using crypsis to avoid predators has the opportunity to evolve polymorphic crypsis when it is being exposed to two (or more) habitats with different backgrounds. Here, we investigate when this phenomenon can occur, in a simulation study with a sexually reproducing prey and a predator that can learn to find hiding prey, represented by an artificial neural network. Initially, the prey is well adapted to one habitat, but tries to expand its range by invading another, different, habitat. This can cause the prey to evolve toward an intermediate phenotype, equally cryptic in both habitats. The prey can also fail in adapting to its new environment, and stay the same. Alternatively, it can evolve polymorphic crypsis. We find that the evolutionary outcome depends on the amount of dispersal between the habitats, with polymorphic crypsis evolving for low dispersal rates, an intermediate phenotype will evolve for intermediate dispersal rates and no adaptation to the new habitat will occur for high dispersal rates. The distribution of phenotypes of the prey will also vary for different dispersal rates, with narrow distributions for low and high dispersal rate and a wide distribution for intermediate dispersal rates.

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Simulation study on dynamic response of an intersection in artificial neural network

Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems ◽

10.1109/itsc.2003.1252750 ◽

2004 ◽

Author(s):

Wei Lian-yu ◽

Pang Ming-bao

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Dynamic Response ◽

Simulation Study ◽

Artificial Neural

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Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network

Sensors ◽

10.3390/s20174952 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4952

Author(s):

Prasanna J. ◽

M. S. P. Subathra ◽

Mazin Abed Mohammed ◽

Mashael S. Maashi ◽

Begonya Garcia-Zapirain ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Approximate Entropy ◽

Classification Performance ◽

Permutation Entropy ◽

Hadamard Transform ◽

Epileptogenic Zone ◽

Eeg Signals ◽

Walsh Hadamard Transform ◽

Artificial Neural

The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh–Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.

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PARALLEL ARTIFICIAL NEURAL NETWORK APPROACHES FOR DETECTING THE BEHAVIOUR OF EYE MOVEMENT USING CUDA SOFTWARE ON HETEROGENEOUS CPU-GPU SYSTEMS

Jurnal Teknologi ◽

10.11113/jt.v78.10145 ◽

2016 ◽

Vol 78 (12-2) ◽

Cited By ~ 1

Author(s):

Norma Alias ◽

Husna Mohamad Mohsin ◽

Maizatul Nadirah Mustaffa ◽

Siti Hafilah Mohd Saimi ◽

Ridhwan Reyaz

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Eye Movement ◽

Large Scale ◽

Real Data ◽

Digital Data ◽

Movement Behaviour ◽

Eeg Signals ◽

Data Set ◽

Artificial Neural

Eye movement behaviour is related to human brain activation either during asleep or awake. The aim of this paper is to measure the three types of eye movement by using the data classification of electroencephalogram (EEG) signals. It will be illustrated and train using the artificial neural network (ANN) method, in which the measurement of eye movement is based on eye blinks close and open, moves to the left and right as well as eye movement upwards and downwards. The integrated of ANN with EEG digital data signals is to train the large-scale digital data and thus predict the eye movement behaviour with stress activity. Since this study is using large-scale digital data, the parallelization of integrated ANN with EEG signals has been implemented on Compute Unified Device Architecture (CUDA) supported by heterogeneous CPU-GPU systems. The real data set from eye therapy industry, IC Herbz Sdn Bhd was carried out in order to validate and simulate the eye movement behaviour. Parallel performance analyses can be captured based on execution time, speedup, efficiency, and computational complexity.

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