Detection of Mental State from EEG Signal Data: An Investigation with Machine Learning Classifiers
Ahnaf Akif Rahman, Muntequa Imtiaz Siraji, Lamim Ibtisam Khalid, Fahim Faisal, Mirza Muntasir Nishat, Mohammad Rakibul Islam, Nchouwat Ndumgouo Ibrahim Moubarak
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.