Comparative Performance Analysis of Machine and Deep Learning Models for EEG-Based Biometric Authentication - Tarawneh, Ahmad Ayman; Kamel, Aloui; Naceur, Mohamed Saber (Informatica, 2025)
EEG-based biometric authentication has emerged as a secure alternative to conventional authenticationmethods, owing to its resistance to spoofing and inherent movement/image individual variability. This studyevaluated the performance of various classification models in the EEG motor movement/image dataset,which comprises 1,526 sessions recorded from 109 subjects using 64 EEG channels at a sampling rate of160 Hz. A comprehensive set of 1,600 features per session was extracted in the time, frequency, and timefrequencydomains. Following standard pre-processing and normalization, the models were trained in astratified 70/30 training test split using features standardized to zero mean and unit variance.We systematically compared traditional machine learning classifiers, ensemble methods, and deep learningarchitectures. Hyperparameter tuning was performed uniformly across all the models. The Ridge Classifierachieved the highest accuracy (93.8%), followed by Logistic Regression (91.27%) and MLP (89.96%),demonstrating the strength of linear and shallow neural models on engineered EEG features. In contrast,deep learning models, including CNN, LSTM, GRU, and BiLSTM, recorded significantly lower accuracy( 0.87%) because of limited training data and the use of pre-extracted statistical features instead of rawtime-series input, which restricted their ability to learn temporal patterns.These findings indicate that traditional machine-learning models, when applied to well-crafted features,remain highly competitive for EEG-based authentication. They offer a favorable balance between performance,computational efficiency, and interpretability, whereas deep learning approaches require furtheradaptation to the structure and scale of EEG data.