Alcoholic EEG Signal Classification Using Multi-Heuristic Classifiers with Stochastic Gradient Descent Technique for Tuning the Hyperparameters

Abstract

Electroencephalography (EEG) signals are utilized to examine various pathological as well as physiological brain activities. Alcoholism is an example of a significant behavior that may be investigated and comprehended utilizing electrical brain impulse models. In the area of biomedical research, categorizing alcoholic patients using EEG data is a complicated issue. To overcome this issue, in this research, alcoholic EEG signal classification was performed by various dimensionality reduction techniques like Hilbert Transform, Rigid Regression and Chi Square Probability Density Function. Finally, the Bayesian Linear Discriminant Classifier, Linear Regression, Logistic Regression, Gaussian Mixture Model (GMM), Adaboost, Detrend Fluctuation Analysis, Firefly Algorithm, Harmonic Search Algorithm, and Cuckoo Search Algorithm are employed to classify the dimensionally reduced alcoholic EEG dataset. In addition, we provide an approach for selecting the ideal combination of Stochastic Gradient Descent (SGD)-based hyper parameters updation algorithm to improve the accuracy of alcoholic EEG classification in GMM, Firefly, Harmonic Search, and Cuckoo Search classifiers in this study. When dimensionally reduced alcoholic EEG signal features from the Hilbert Transform are used with the SGD with GMM classifier, the results display good accuracy of 96.31%.