Brain Signal Analysis in Alcoholism

  Ramesh Kumar,  Raj Kumar Mistri,  Amit Ranjan Roy

: In this study, the magnitude and frequency spectrum in the electroencephalogram (EEG) were examined to address the classification possibility of alcoholism in the central nervous system. The pre-recorded EEG signals for chronic alcoholic conditions & control condition taken from the motor cortex region and methodologies used for feature extraction is Fast Fourier Transform, four Feature Real Power, Absolute Power, Peak Power Frequency; Median Power Frequency having five sub frequencies band (delta, theta, alpha.beta1, beta2) for each feature is extracted from the alcoholic condition as well as a control condition. These extracted features are classified by SVM and FLANN . The maximum classification accuracy is achieved with the EEG spectral of feature of PEAK POWER FREQUENCY in the THETA band in F3 channel (80%), by using SVM, and by using FLANN max accuracy is obtained in f3 electrode.

Keywords- Alcohol; Cerebral motor cortex; Electroencephalogram; Fuzzy C means clustering

Unique Identification Number - IJEDR1704212
Page Number(s) - 1336-1341
Pubished in - Volume 5 | Issue 4 | December 2017
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Ramesh Kumar,  Raj Kumar Mistri,  Amit Ranjan Roy,   "Brain Signal Analysis in Alcoholism", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.5, Issue 4, pp.1336-1341, December 2017, Available at :http://www.ijedr.org/papers/IJEDR1704212.pdf