Automatic Detection of Mental Health Status using Alpha Subband of EEG Data

Abstract

Electroencephalography (EEG) is an indispensable non-invasive analytical method in the diagnosis and characterization of mental health. However, the conventional EEG interpretation process is quite subjective, time-consuming, and susceptible to error. The clinicians usually observe abnormalities in amplitude or frequency to markup the EEG signal as unhealthy, which is based on visual scrutiny of EEG data. In case of high-volume long-duration EEG recordings, it will be a grueling task for experts and may cause inaccurate classification of EEGs. In this work, a computer-aided automatic decision-making model has been designed to identify mental health status using only alpha band (8–12 Hz) of EEG signal to conquer the aforementioned difficulties. The demonstration of this study is carried out on the two publicly available EEG datasets of epileptical seizure and schizophrenia. The proposed simulation model followed the process flow of signal denoising, decomposition of EEG signal into various bands, feature extractions from alpha band of EEG data, and classification of mental health of human as healthy or unhealthy. The performance of chosen features is evaluated through popular classifiers. The ensemble bagged tree classifier outperforms the other methods on epileptical seizure and schizophrenia datasets with a classification accuracy of 99.5% and 98.68% respectively. Hence, this proposed method can be an alternative for the automatic classification of mental health status at the early stage of EEG analysis.