Feature selection with Genetic Algorithm for alcoholic detection using electroencephalogram

Electroencephalography is a technique of recording human brain waves through electrodes mounted on the human scalp. The obtained signal data or Electroencephalogram (EEG) can be used for the alcoholic detection as a substitution of the urine test or breathalyzer test, since in the urine or breathalyzer test, is time limited. Therefore, the usage of EEG for alcoholic detection is proposed in this research. There are four stages are proposed in this research. First, noise removal of the recorded signal by separating the signal using Independent Component Analysis. Second, feature extraction with Discrete Wavelet Transform. Third, extracted features from the previous stage are selected based on the importance of each feature using Genetic Algorithm, and final stage is the alcoholic detection with Backpropagation neural network. Experiments were conducted on 64 channels of EEG data. The feature selection stage using Genetic Algorithm made the accuracy of the detection process is increased, i.e. the maximum average accuracy of the detection is 79.38%. This feature selection stage also reduced the used feature for the detection process, up to 48% of all obtained features, in the extraction feature stage.