EXTRACTION OF FEATURES FROM EEG USING WAVELET TRANSFORM

Author(s) : Anitha Raj, Shanavaz K.T.

Volume & Issue : VOLUME 2 / 2017 , ISSUE 1

Page(s) : 110-113


Abstract

In our body, brain is one of the most complex organ. It controls the coordination of human muscles and nerves Suitable analysis is necessary for EEG to differentiate the normal and epileptic seizures because EEG is a non stationary signal. Epilepsy is the most common neurological disorder. Due to abnormal elect-rical discharges from the brain cells a recurrent seizure disorder is caused in the cerebral cortex. In EEG based brain mapping analysis, feature extraction of EEG signal is the main issue. This paper proposes wavelet based feature extraction technique. Minimum value, maximum value, mean, median, standard deviation has been extracted. The power spectrum of the EEG signal drawn.



Keywords

wavelet transform; power spectral density

References

[1]Musa Peker, Baha Sen*,and Dursun Delen “a
Novel Method for Automated Diagnosis of Epilepsy
using Complex-Valued Classifiers’’ JBHI. 2014.,
2387795, IEEE Journal of Biomedical and Health
Informatics
[2]R. Caton, "The electric currents of brain", British
Medical Journal,vol.2, pp. 278, 1875
[3]Amjed S. Al-Fahoum1 and Ausilah A. Al-Fraihat2”
Methods of EEG Signal Features Extraction Using
Linear Analysis in Frequency and Time-Frequency
Domains”
[4]Ales Proch ˇ azka and Jarom ´ ´ır Kukal ” Wavelet
Transform Use for Feature Extraction and EEG
Signal Segments Classification”
[5]Lung Chuin Cheong, Rubita Sudirman and Siti
Suraya Hussin “feature extraction of eeg signal using
wavelet transform for autism classification” vol. 10,
no 19, october, 2015 issn 1819-6608 arpn Journal of
Engineering and Applied Sciences
[6]B. Sen, M. Peker, F.V. Celebi and A. Cavusoglu,
“A comparative study on classification of sleep stage
based on EEG signals using feature selection and
classification algorithms”, Journal of Medical
Systems