Deep and Statistical Features Classification Model for Electroencephalography Signals

People strive to make sense of the complex electroencephalography (EEG) data generated by brain. This study uses a prepared dataset examine how easily people with alcohol use disorder (AUD) could be distinguished from healthy people. The signals each electrode are connected one another and first represented as single signal. signal is then denoised through variation mode decomposition (VMD) during preprocessing stage. statistical deep feature extraction phases two subsequent phases. crucial step in suggested strategy classify using combination these unique qualities. Deep performance was evaluated independently. Then, eigenvectors created merging all collected features, classification carried out our DSFC (Deep - Statistical Features Classification) model. Although accuracy rate only features 81.2 percent learning 95.71 percent, utilizing hybrid technique 99.2%. Therefore, it can proven that combining produce beneficial results.