EEG-Based User Identification using Machine Learning and Deep Learning Approaches - Sushma S.; Venkat S.; Mohanavelu K.; Fredo Jac A. R.; Bobby T. Christy (Current Directions in Biomedical Engineering, 2024)
In recent years, Electroencephalogram (EEG) based user authentication systems have gained significant interest as an innovative approach for identity verification. EEGs are considered to be a novel biometric attribute due to the individuality of each person’s cerebral activity patterns. This work explores the feasibility and efficiency of utilizing EEG signals, generated in response to emotional stimuli, for user authentication applications, by implementing Machine Learning (ML) and Deep Learning (DL) approaches. Support Vector Machine (SVM), Random Forest (RF) classifier and 1D Convolution Neural Network (CNN) were employed to evaluate and compare the performance of EEG-based user authentication for two publicly available EEG datasets, namely DEAP and DENS database. The performance of EEGbased user authentication was significantly high in LAHV emotional state for DENS dataset, achieving an accuracy of 99.2 % and 92.59 % with SVM and modified 1D CNN, respectively.