Open Science Research Excellence

Maan M Shaker

Publications

2

Publications

2
2354
EEG Spikes Detection, Sorting, and Localization
Abstract:
This study introduces a new method for detecting, sorting, and localizing spikes from multiunit EEG recordings. The method combines the wavelet transform, which localizes distinctive spike features, with Super-Paramagnetic Clustering (SPC) algorithm, which allows automatic classification of the data without assumptions such as low variance or Gaussian distributions. Moreover, the method is capable of setting amplitude thresholds for spike detection. The method makes use of several real EEG data sets, and accordingly the spikes are detected, clustered and their times were detected.
Keywords:
EEG time localizations, EEG spike detection, superparamagnetic algorithm, wavelet transform.
1
13033
EEG Waves Classifier using Wavelet Transform and Fourier Transform
Abstract:
The electroencephalograph (EEG) signal is one of the most widely signal used in the bioinformatics field due to its rich information about human tasks. In this work EEG waves classification is achieved using the Discrete Wavelet Transform DWT with Fast Fourier Transform (FFT) by adopting the normalized EEG data. The DWT is used as a classifier of the EEG wave's frequencies, while FFT is implemented to visualize the EEG waves in multi-resolution of DWT. Several real EEG data sets (real EEG data for both normal and abnormal persons) have been tested and the results improve the validity of the proposed technique.
Keywords:
Bioinformatics, DWT, EEG waves, FFT.