Detection of Mental State from EEG Signal Data: An Investigation with Machine Learning Classifiers

Abstract

The mental state of a person is a combination of very complex neural activities which determine the current state of mind. It depends on a lot of external factors as well as internal factors of the brain itself. It is possible to determine an individual's mental state by analyzing their EEG patterns. Using a dataset acquired from Kaggle, ten machine learning techniques were investigated and models were built. The RandomSearchCV method was used to perform hyperparameter tuning and a comparative study has been portrayed for both tuning and without tuning of hyperparameter. After evaluating the performance parameters, Support Vector Machine (SVM) displayed the best accuracy (95.36%). However, Gradient Boosting (GrB) depicted promising accuracy of 95.24% whereas K-Nearest Neighbors (KNN) and XGBoost (XGB) both depicted 93.10% accuracy. As a result, with effective integration of the ML-based detection method, it is likely to regulate a person's state of mind, which will enable to develop a better understanding of human psychology and forecast their actions.