Electroencephalogram (EEG) is widely used for recording the brain electrical activity. It plays an important role in the detection and classification of epilepsy seizures. In addition, it is believed to possess the ability to detect the location of epileptic focus to some degree. In this study, an EEG data during seizure obtained is analysed and transformed into an image form, namely Flat EEG (fEEG). The fEEG is a method for mapping high dimensional signal into a low dimensional space. The EEG of the obtained signal is clustered and the interactions of the cluster centers are studied and presented in this study.
Electroencephalography (EEG) is one of the field in diagnosing g epilepsy. Analysis of the EEG records can provide valuable insight and improve understanding of the mechanisms causing epileptic disorders. In this paper, the fast Fourier transform (FFT) and wavelet transform are used as spectral analysis tools of the EEG signals. These methods are chosen because they provide time–frequency shifted on the EEG signals. Since the frequency characteristics are important information that can be observed from the signals, FFT and wavelet transform are among a the best methods in analysis of EEG signals. The comparisons between these two methods are also carried out. Result showed that the wavelet transform is better than FFT in analysis of EEG signals. A software for analysing EEG signal is also developed using C++ programming. The software is able to compute and show the results of the analysis signal data by both of the two methods in graphical form.
Electroencephalography (EEG) is one of the fields in clinical neurophysiology, which is a recording of the electrical activity of the brain from the scalp. One of the major roles of EEG is as an aid to diagnose epilepsy. Abnormal patterns such as spikes, sharp waves and, spikes and wave complexes can be seen. It is important to extract spatial information about the dynamics from a few observations of this recorded signal regardless where EEG sensors are located. A developed method by Theoretical & Computational Modelling for Complex System (TCM), UTM, namely Flat EEG for mapping high dimensional signal into a low dimensional space will be used as platforms to analyse EEG signal spatially during epileptic seizure. The spatial interactions among clusters are identified through spatial interaction model, namely gravity model. This paper also reveals that gravity model used is a measure.
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