The effects of human brainwave signals due to mobile phone radio frequency exposure using artificial neural network / Roshakimah Mohd Isa - Mohd Isa, Roshakimah (Institute of Graduate Studies, UiTM, 2018)
The frequency content of recorded electroencephalogram (EEG) signals plays an important role in describing the signals and also the state of the brain. It is found that the emitted of radiofrequency (RF) radiation energy due to the usage of mobile phones contributes to the changes of brainwave signals. Nevertheless, it is yet to be determined the effects of RF exposure to human’s health that related to the brain based on EEG and intelligent approach. Therefore this thesis proposed a novel approach for recognizing the characteristics of brainwave signals due to mobile phone RF exposure using intelligent techniques. The presented thought recognition methodology utilises correlation and asymmetry features between EEG and RF exposure and integrated with feed-forward Artificial Neural Networks (ANN) for classification. The procedures involved EEG recording at the frontal; left and right head and have been conducted in three sessions namely Before, During and After RF exposure. The duration of each session is five minutes. Ninety five volunteers involved in this study and they are divided into three exposure groups, which categorised as Left Exposure (LE), Right Exposure (RE) and Sham Exposure (SE) group. The RF exposure used in the experiment is sourced from a mobile phone with operating bandwidth between 0.9 to 2.2 GHz with 0.69 W/kg SAR rate. Then, the analysis to observe the brain hemisphere dominance due to the mobile phone RF exposure has been carried out through the Power Asymmetry Ratio (PAR) features…