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This study highlighted the use of Cluster analysis approach to classify the participants' Learning Style (LS) based on the EEG brain asymmetry (BA) dataset. BA is importance to indicate brain activity in both right hemisphere (RH) and left hemisphere (LH). The RH and LH dominant states are closely related to human learning traits such as Attention, Perception and Emotions. In this research, we determine...
This paper proposed the heart disease diagnosis system using nonlinear ARX (NARX) model. The system uses neural network for model estimation and classification of Normal and several heart diseases based on heart sounds. In classification, a spectrogram was applied to the modeled heart sounds for features extraction and selection. The features were fed to the FFNN and trained using Resilient Backpropagation...
This paper proposed the heart disease modeling system based on heart sounds. The model uses ARX model as regression vector and Neural Network as nonlinear model structures. The number of hidden neurons was optimised by minimizing the criterion of NSSE, fit and FPE criterion. The model architecture of 2-4-1 perfectly fits the original heart sound signals with average R-square of above 99.9%. The weight...
This paper presents a study of EEG pattern for smokers based on Theta, Alpha and Beta Band power using Power Spectral Density (PSD). Smoking cigarettes is a bad habit, but the numbers of smokers keep growing. Most smokers gives `to release stress' as a reason for smoking. The objective of this research is to investigate smokers' EEG pattern with the hypothesis that they will have higher Alpha Band...
The purpose of this research is to establish the fundamental brainwave balancing index (BBI) using EEG signals. Brainwave signals from EEG were measured and analyzed using intelligent signal processing techniques and specific algorithm. Consequently, the signals were statistically correlated with established psychoanalysis techniques to produce BBI system. The result shows that the PSD analysis provides...
The main focus of this research is to observe EEG pattern of smokers on the Theta and Delta Frequency Bands. 8 male smokers and 8 non-smokers were sampled in this study. EEG data was recorded for 3 minutes. It was found that, there some difference in the Alpha and Beta Frequency bands for smokers compared to non-smokers EEG pattern.
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