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 authentication methods, owing to its resistance to spoofing and inherent movement/image individual variability. This study evaluated 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 of 160 Hz. A comprehensive set of 1,600 features per session was extracted in the time, frequency, and timefrequency domains. Following standard pre-processing and normalization, the models were trained in a stratified 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 learning architectures. Hyperparameter tuning was performed uniformly across all the models. The Ridge Classifier achieved 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 raw time-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 further adaptation to the structure and scale of EEG data.