Classification EEG Signal Using Texture Analysis and Artificial Neural Network for Alcoholic Detection

EEG is a powerful and popular technique for measuring brain activity, which reflects the condition of a person’s brain. A person’s brain health can be determined by monitoring brain activity used EEG technique. It has been demonstrated that EEG signals can be used as a diagnostic tool in evaluating individuals with alcoholism. Using the proper diagnostic method for EEG signals, the individual evaluating under alcoholism has been demonstrated. EEG signals record the brain’s electrical activity, measured from the scalp. The measurements obtained from the EEG are used to confirm or rule out conditions such as alcoholism. Alcohol consumption is associated with specific patterns of brain electrical activity in adults, and the brain activity of individuals with alcoholism differs from non-alcoholics in several ways. This study proposes a feature extraction method for multichannel EEG signals using texture analysis for alcoholic and non-alcoholic classification. Multichannel EEG signal is treated as an image and processed using the texture analysis method. Then it is classified using an artificial neural network. The highest accuracy is achieved using the GLDM feature extraction method at a distance of 3 and an angle of 0°, resulting in an accuracy rate of 93.9%. The method used proved to be higher than previous studies using similar methods. The proposed method is expected to be used for other multichannel biomedical signal processing.