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%.

KEYWORDS:

• Adaboost
• Chi square
• EEG
• GMM
• Harmonic search
• Hilbert transform
• Hyper parameters
• SGD
• Rigid regression

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Harikumar Rajaguru

R Harikumar – Presently, he is working as professor in the Department of ECE at Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, India. He is a recognized supervisor for Anna University, Chennai. Fifteen of his students awarded PhD in the area of machine learning and bio signal processing.

A. Vigneshkumar

A Vignesh Kumar – He was awarded both the BTech and ME degrees from Anna University, Chennai. Currently he is pursuing research at Department of ECE, Bannari Amman Institute of Technology, Tamilnadu. Email: [email protected]

M. Gowri Shankar

M Gowri Shankar – Presently, he is with Department of ECE, Bannari Amman Institute of Technology, Tamilnadu. He was awarded both the BE and ME degrees from Anna University, Chennai. Email: [email protected]